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Brain–computer interface–functional electrical stimulation: from control to neurofeedback in rehabilitation
Brain computer interface functional electrical stimulation: from control to neurofeedback in rehabilitation
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Saugat Bhattacharyya1 and Mitsuhiro Hayashibe2 1 School of Computer Science & Electronic Engineering, University of Essex, Colchester, United Kingdom, 2Division of Mechanical Engineering, Tohoku University, Sendai, Japan
Introduction Strokes are one of the foremost reasons of disability in Western countries, with over 795,000 new cases in the United States each year (as mentioned in the 2017 report about heart disease and stroke statistics published by the American Heart Association). There are indications from studies that suggest a tremendous increase in numbers as the population ages further and stroke survival rates increase. In the European Union itself, it is projected that the percentage of the population over 65 years old will increase from 17.1% in 2008 to 30% in 2060, and the population over 80 years will rise from 4.4% to 12.1% over the same period (EUROSTAT population projections). Thus the incidence and prevalence of a first stroke in Europe is about 1.1 and 6 million, respectively. The present projection estimates that about 75% of people affected by a stroke will survive 1 year or more, and this proportion is estimated to increase in the future because of better quality pre- and postrehabilitation and enhancement of lifelong treatment procedures. It is reported that a meager amount (14%) of stroke survivors show complete recovery after upper limb rehabilitation at hospitals, while a huge number of 56% show no recovery at all. Currently, physical therapy is the most widely accepted procedure of rehabilitation for stroke patients. Methods such as intensive exercise and augmented feedback, constraint- induced movement therapy, and exercise in virtual environments with feedback to assist skills learning are a few measures preferred by physical therapy researchers and clinicians. Methods such as robotic assistive devices with sensory feedback are gaining precedence among the professionals to provide longterm therapy in a consistent and measurable manner. In the United States itself, more than 2 million stroke patients suffer from long-term gait even after physiotherapy and spontaneous recovery. Poststroke gait disability may create other morbities
Bioelectronics and Medical Devices. DOI: https://doi.org/10.1016/B978-0-08-102420-1.00037-6 Copyright © 2019 Elsevier Ltd. All rights reserved.
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such as diabetes, cardiovascular disease, and depression due to decreased participation in physical, social, and professional activities. Looking into the high risk posed to stroke survivors, there is a constant need to develop newer therapy strategies that provide substantial improvement in rehabilitation procedures. Currently available orthoses and assistive technologies such as functional electrical stimulation (FES) devices have not shown any lasting improvement in gait rehabilitation, and hence there is a rising attraction among researchers to incorporate brain computer interface (BCI) technology in poststroke rehabilitation therapy. BCIs aim at providing a new communication channel between the human brain and external devices without any neuromuscular intervention. BCIs translate signals originating from the central nervous system and recorded by devices such as electroencephalography (EEG) into control commands to control external devices such as prosthesis, wheelchairs, and mobile robots. Despite their growing popularity, BCI technologies are not yet ready for commercial or clinical applications because of not producing optimal performance in terms of accuracy and requiring a long training session, thus making it a costly affair. BCI has been considered as a potential alternative to improve standard motor therapy after stroke by taking into account the damaged motor network of the brain. On the other hand, FES is employed during rehabilitation to directly engage muscles of the targeted, damaged region (limb). FES is capable of restoring certain daily life skills for physically challenged patients by directly stimulating the targeted muscles group. Previous studies have reported on the ability of FES to elicit recovery of basic activities performed on a daily basis such as standing up, grasping, cycling, and walking by retraining the users on these tasks. FES-based rehabilitative systems do not employ the cortical activity of the patient (Cauraugh, Light, Kim, Thigpen, & Behrman, 2000; Chen, Yu, Huang, Ann, & Chang, 1997; Kojovic, Djuric-Jovicic, Dosen, Popovic, & Popovic, 2009; Riener, Ferrarin, Pavan, & Frigo, 2000) and hence can lead to monotonicity and lack of interest at relearning the activity among the patients. It has been hypothesized that lasting neurological and functional enhancement in motor recovery can be accomplished when the activation of upper motor neurons (UMNs) in the poststroke cerebral cortex are coupled with the activation of lower motor neurons via FES. Employing BCI with FES therapy would train and assist patients to activate UMNs and have lasting impact on the rehabilitation rather than simply using passive (nonvolitional) electrical stimulation. The implementation of brain-controlled FES systems as new physiotherapeutic alternative has been suggested by many researchers in Daly et al. (2009), Takahashi et al. (2012), and Mukaino, Ono, and Shindo (2014). Therapy based on BCI FES has been reported to be safe, with a large proportion of users experiencing improvements in their gait speed and pattern. Further studies on a controlled population are required to investigate the potential efficacy and optimal duration of BCI FES therapy, the targeted clinical population to benefit most from this therapy, its performance on combination with other traditional conventional therapies, and most importantly, the neurobiological principles that govern any functional changes. In hindsight, it seems quite natural to combine FES rehabilitation with BCI systems, where FES can activate the sensory channel to provide information in
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the brain and the BCI would generate the necessary motor commands to close the motor loop. Hence, both FES and BCI will influence each other and work together to improve the cortical and peripheral learning of the user simultaneously.
Combining brain computer interface with functional electrical stimulation BCI provides an efferent outflow of commands from the electrical signals generated in the brain, while the FES activates the sensory channel to provide a maximal inflow in the brain. Thus it is quite natural to combine FES rehabilitation with BCI systems as it is expected that both FES and BCI would influence each other positively and augment the retraining of users to enable regulation of upper and lower limbs through brain signals to achieve motor rehabilitation. Generally, the BCI regulates the motor rehabilitation by collecting and processing signals related to motor action in real time and converting them into commands that can be easily understood by peripheral devices connected to the BCI system. This allows the EEG-based BCI to circumvent damaged motor pathways and send the control the control command directly to the targeted limb in paralytic patients. A block diagram of the BCI FES system is shown in Fig. 30.1. To date, motor functions (such as grasping) are restored by using FES. A prerequisite for such restoration is when the nerves connecting the ventral roots of the spinal cord to the peripheral muscle are still intact. Surface or subcutaneous electrodes are placed near the motor point of the targeted muscles and short, constant-current pulses are applied to it. This leads to a depolarization of the action potential of the nerve membrane, which further leads to a contraction of the innervated muscle fibers similar to the same at physiological conditions. A tetanic contraction of muscles is achieved due to temporal summation of pulses at higher frequency (20 Hz). Pfurtscheller, Mu¨ller, Pfurtscheller, Gerner, and Rupp (2003) and Pfurtscheller, Mu¨ller-Putz, Pfurtscheller, and Rupp (2005) initiated the integration of noninvasive
Figure 30.1 Block diagram of a BCI FES system. The BCI decodes the brain signals to send to the FES device, which in turn activates the muscles of the targeted limb. BCI, Brain computer interface; FES, functional electrical stimulation.
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EEG-based BCI with FES rehabilitation. They aimed at controlling hand grasping using imagined motor responses. The task involved grasping of an object and moving it to another place. They also presented the first empirical evidence resembling voluntary wrist movements, represented by a short-lasting event related desynchronisation (ERD) in the alpha (mu) and lower beta band and a subsequent beta event related synchronisation (ERS), when induced by 20 Hz electrical stimulation. The group reported a beta oscillation burst localized predominantly over the sensorimotor cortex of the contralateral and the mid-central region of the targeted limb. Such a pattern, as known from previous EEG studies on voluntary movements, indicates a (de-)activation of the sensorimotor area and adjoining premotor and related areas. The processes occurring in the sensorimotor region during FES of the forearm muscles are similar to the ones during active voluntary hand movements. This indicates that afferent proprioceptive inputs (from joint, tendon, and muscle receptors) to the primary somatosensory area have an influence during the movement and may partly be due to the postmovement beta ERS as a reflection of a desynchronization of the motor cortex after termination of the FES-induced movement. It is also necessary to quantitatively compare the effects of FES-induced movement to that of active and passive movement in the brain. Active movements are carried out by the participants themselves without any assistance in any form. For passive movements, the participants carry out the tasks with help from a mechanical device or human assistance. FES-induced movements stimulate the targeted limb of the participant using FES. Muller et al. (2003) compared the three conditions for upper limbs while Qiu et al. (2016) compared it for lower limbs for both healthy subjects and hemiplegic stroke patients. Mu¨ller et al. (2003) reported no generation of ERD patterns, prior to the onset of movement, during FES-induced state. This indicates no occurrence of movement planning and preparation during FES-induced movement. On the other hand, Qiu et al. (2016) stated that stroke patients exhibited ERD patterns in the central region of the brain during FES-induced movements while there were no such behaviors for healthy subjects. It was reported that ERD in the beta frequency band were significantly correlated with active movements, whereas no ERD patterns were generated during passive movements. This indicates that EEG oscillatory pattern under FES-induced movement originates during active movement and is carried forward during the FES-induced state. Instead of somatosensory signals, Chu, Zhao, Han, Zhao, and Yao (2014) employed EEG signals generated through steady-state visual evoked potential (SSVEP) in the BCI to activate the necessary motor commands. The basic idea of this BCI was to acquire responses generated due to the SSVEP elicited by stimulation of a flashing red square at three frequencies, 20, 15, 12 Hz. The participants would need to focus their attention on the flickering square to generate the required control command. Furthermore, an integrated iterative learning control with proportional derivative (PD) feedback was used to improve the performance of the FES. The controller would optimize the stimulating sequence to make the upper limb track the preplanned trajectory. The results from this study show the effectiveness of the integration of SSVEP-BCI with FES that can track a desired trajectory at a limited precision.
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A study on functional magnetic resonance imaging (fMRI) data by Shoham, Halgren, Maynard, and Normann (2001) dealing with tetraplegic patients shows that even chronically deefferented sensorimotor representation areas respond to attempts at movement and display a minimal somatotopical reorganization. However, the impact of FES on the EEG reactivity in paralyzed patients remains to be validated and is a question for future research. The main purpose of BCI FES studies is to design a user-efficient rehabilitative tool for users, and thus in the next section we discuss BCI FES studies on the patient population.
Brain computer interface functional electrical stimulation in rehabilitation BCI technology has huge scope in the fields of medical rehabilitation as a form of assistive tool. This technology was developed with an aim to allow patients suffering from severe motor disabilities such as amyotrophic lateral sclerosis to communicate and interact with their external environment. The BCI-based technology and its corresponding rehabilitative therapy have a similar working principle to classical neuromotor rehabilitation. It would provide the physical therapist with a monitoring instrument that would assess the patient’s performance and level of learning in the rehabilitative cognitive task and assist the patient to recover lost functionality of limbs or muscles. Rehabilitation exercise based on BCIs promotes neuroplasticity in the motor region of the brain and hence would lead to a better learning/relearning of movement activities in the motor areas. The idea of a BCI-based FES system is vital in the recovery process of patients suffering from stroke and thus is valuable in the field of medicine. The current available methods in stroke rehabilitation are passive in nature, and its effectiveness is limited and time-consuming. The BCI FES system aims at employing brain signals, associated with motor movement (motor imagery), to generate activation commands (by using digital signal processing and machine learning methods) that would control the functionality of a FES system. The BCI FES allows the patient to modulate their own rehabilitation in manners they deem to be suitable and hence improves upon the limitation of passive rehabilitation. It has been found that BCI FES systems stimulated the brain plasticity of patients and hence improved the efficiency of their rehabilitation. However, every BCI system would require feedback (in form of visual, auditory, or tactile sensation) to be provided to the patient to allow them to actively participate in their rehabilitation and also not cause any form of aversion in patient. Functional electric stimulus, on the other hand, reduces muscle spasticity and joint flexibility and hence can be said to be an efficient proprioceptive feedback to BCI. In such a setup, the BCI system detects motor intention (as ERD phenomena generated due to voluntary movement intention) of stroke patients and subsequently triggers the FES when motor intention is detected.
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FES and motor imagery BCI have been widely used in the rehabilitation training of stroke patients. BCI solely based on motor imagery exhibits the problem of performance variability due to the absence of feedback and leads to monotonicity toward the task at hand. BCI inherently links the brain and the outward environment directly and has shown great perspective to help patients to regain or recover their ability to communicate and control. The introduction of FES would further enhance the performance of the BCI and help in providing a more inclusive rehabilitation training to patients. A study by Chung, Kim, Park, and Lee (2015) investigated the BCI FES system for clinical rehabilitation of patients suffering from poststroke hemiplegia. It was reported that the motor function was enhanced due to the brain plasticity occurring in the affected motor-related cortex of patients which were activated significantly during the rehabilitation period. The ERD power of motor-related cortex was easily distinguishable from signals in the relaxation state, which proved that intact brain neurons were activated to replace the impaired cortex areas by brain plasticity. Results showed that patients who completed the rehabilitation tests showed significant recovery of target muscle function and exhibited functional improvements, especially in movements involving the extensor digitorum. All patients exhibited a bilateral representation of the motor action while the discriminant frequency components were consistently localized in the mu and beta bands. Remarkably, the patient with the worst conditions exhibited functional recovery from a totally paretic arm to a very limited but still noticeable voluntary activity of the fist. These results validate that the incorporation of BCI with FES in rehabilitation leads to an enhancement of functional recovery of targeted muscles. Another group of researchers, Daly et al. (2008), developed a BCI FES system interfaced with a robot for convenience by stroke survivors for upper limb (wrist/ hand) motor rehabilitation. The enforced motor tasks are wrist/hand or shoulder/ elbow tasks that were imagined, attempted, or imposed relaxation of muscles. Subjects showed high performance for imagined and attempted and less for relaxation tasks, which indicated the practicability of using the BCI FES/robot for the purpose of motor rehabilitation. The same system was also tested on chronic stroke survivors who were able to generate characteristic brain signals for imagined and attempted wrist/hand and shoulder/elbow tasks but could not generate signals during relaxation tasks.
Importance and types of brain computer interface feedback The design of a user-efficient feedback is an important issue in motor imagery BCI systems. Most researchers focus on the machine learning aspect of BCI by concentrating on improving the classification algorithms, while the human and training aspect of BCI is neglected. Developing a suitable training paradigm
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could help in improving the performance of BCI and make the user comfortable with the paradigm. If feedback of a user’s performance is provided, then the BCI users tend to coadapt with the system. Hence, the inclusion of feedback to BCI experiments can enhance the learning among users and thus improve their performance. Visual feedback (like a moving cursor or target on the computer screen) is the most common form of feedback provided to users in BCI. Feedback in the form of grasping hands (realistic feedback) or extended bar (abstract feedback) were employed in Neuper, Scherer, Wriessnegger, and Pfurtscheller (2009). In Barbero and Grosse-Wentrup (2010), the feedback accuracy was biased to investigate the performance of BCI. The results indicated that better performers were impeded by unreliable feedback, while it was noted that poor BCI performers benefited greatly with positive feedback. Gonzalez-Franco, Peng, Dan, Bo, and Shangkai (2011) also reported that a system with negative feedback had a superior learning effect on motor imagery BCIs as compared to positive feedback. It was reported by Angulo-Sherman and Gutie´rrez (2014) that biasing feedback could not immediately boost subjects’ performance in the same session. However, it could change the trend of motor imagery learning for future sessions. There is no conclusive evidence about which feedback must be provided, as the results are highly variable among individuals. While designing a feedback for BCI systems, one must keep in mind to model a simple and user-friendly feedback presentation that can help naive users to improve their own individual performance by numerous training and frequent adaptation of the system more efficiently. Also, feedback in BCI also depends in the personality of the users, so some may benefit from positive feedback while others may benefit from negative ones. Presenting a feedback through a visual medium is the most preferred feedback paradigm on BCI because it can be simple in nature and is known to provide the largest improvement in performance while controlling a BCI system. Components of the visual system such as vision, visual attention, and focusing gaze are directly involved while maintaining a direct dynamic contact with the environment. But there may be instances where feedback through a visual medium could serve as a deterrent (like working on conditions with poor or no visibility or when the visual system is compromised). On such occasions, auditory or vibrotactile feedback could serve the purpose and has been studied among researchers. Angulo-Sherman and Gutie´rrez (2014) evaluated the influence of different sensory modalities on the performance of a motor imagery (MI)-BCI system. They devised two different ways of feedback, which were presented to the users. They are positive feedback, which indicates a good performance, and negative feedback, which motivated the user to perform better in the task. Results from this study revealed that none of the feedback—auditory, vibrotactile, and the classical visual —turned out to be superior to one another and so it was concluded that the performance achieved depends more on the user rather than the feedback provided. Thus they strongly suggested that the BCI systems require a more personalized feedback strategy that caters to the preference of the user.
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Visual feedback Visual feedback is displayed to the participants on a screen in the form of a more realistic presentation (like the target limbs) or in the form of abstract shapes (like an elongated bar). Sometimes, the visual feedback can be implemented as a game where the subject moves an object (say, a ball) to a target area. Controlling of mobile robots or wheelchairs can be considered as visual feedback, as the subject directly visualizes the performance of the devices within his/her visual range. Here, we will discuss several examples where realistic visual feedbacks were implemented. A hypothesis exists which suggests that the visual feedback could improve the learning experience of motor-related BCI tasks if they are similar to daily activities (such as walking, standing) performed by the human body. A study conducted by Alimardani, Nishio, and Ishiguro (2016) showed that with changes in the visual feedback, the motor imagery skills learned while controlling a human-like robotic hand are more robust with time. The same group also studied the influence of realistic visual feedback on the motor imagery BCI performance of users. They studied the effect of positive and negative feedback bias on subjects’ BCI performance and motor imagery skills. It was noted that the feedback had no influence in the classification score, but on evaluation of brain activity patterns, it was revealed that the positive bias of the feedback improved the subjects’ self-regulation of motor imagery signals.
Vibrotactile feedback When it is not possible to provide visual feedback, then vibrotactile feedback systems can be used to transmit information through a tactile interface. Vibrotactile feedback is advantageous in its way because it is easy to implement and safe and does not require the user to maintain focus on the visual attention. As with all types of neurofeedback, vibrotactile feedback also requires an appropriate amount of training for the BCI task at hand. It also does not interfere with visual stimuli if employed simultaneously. It could also improve the performance of the BCI when the subject’s attention is highly loaded by a simultaneous visual task. Chatterjee, Aggarwal, Ramos, Acharya, and Thakor (2007) showed that all users using only vibrotactile feedback could operate a three-state motor imagery BCI. Certain variations in tactor placement led to a notable bias in accuracy. The results indicated that the different locations of vibrotactile stimulus influenced the user’s modulation of mu-rhythm activity toward desynchronization generated by imagination of the ipsilateral hand. They also reported that the classifier performance from the vibrotactile biofeedback trials was far better than predicted by random chance. The biasing effect may be solved by training and modifying signal processing elements of the BCI. To further compensate for the stimuation and reduction of bias, one may adjust the thresholds and weights of the machine learning technique accordingly.
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Possibility of functional electrical stimulation as feedback FES technologies are used to provide restoration of normal movement in patients with paralysis due to UMN diseases such as stroke, multiple sclerosis, and spinal cord injury. Effects of FES treatment can occur by peripheral or central mechanisms, where the former uses FES to stimulate the patient’s remaining motor units to enhance muscular strength, increase range of motion, and reduce stiffness, while the latter treatment occurs by the reorganization of the cortex based on neurophysiological responses to help control movement through neural plasticity in stroke patients. An existing hypothesis behind the augmented movement therapy by FES assumes that cortical plasticity plays a more major role in recovery rather than peripheral mechanism. The hypothesis was further confirmed in motor training tasks with physiological tests involving transcranial magnetic stimulation and imaging based on fMRI. Functional recovery depends on coherence between afferent and efferent neural activity, where the role of efferent activity for recovery has been demonstrated with motor imagery BCI and the afferent activities are characterized by the FES activating the sensory channels to the brain. If this afferent activity is coherent with the efferent activity then the loop for motor control would be a closed control system with the BCI being used to control the FES-based rehabilitation by decoding the motor intentions of the patient while the FES could provide a form of natural proprioceptive feedback to the patient in the cortical level. Fig. 30.2 provides an intuitive representation of the BCI system with FES as neurofeedback.
Brain biosignal recording (EEG, NIRS)
Brain plasticity
Signal processing and feature selection Afferent sensory feedback Motor intention adaptive decoding
Motor learning for multiple muscle control signal generation Stim
EMG Wireless FES muscle stimulation with evoke EMG feedback
Figure 30.2 Example of implementing FES as a neurofeedback to the BCI tasks. BCI, Brain computer interface; FES, functional electrical stimulation.
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BCI systems are inherently plagued with the presence of massive biological and externally induced artefacts along with the signals of interest. For a BCI FES system, brain signals would usually be contaminated with electrical noise and neural correlates of muscle contractions and movements originating due to the FES stimulation. The artificial neural contamination comes from the FES activation of muscle contraction and limb movement, which are also used as a source of information for the BCI. These phenomena thus could either bias or reinforce an online sensory motor rhythm (SMR)-based BCI. Preliminary studies by Bhattacharyya, Clerc, and Hayashibe (2016) on FES in the form of a neurofeedback in BCI have suggested a positive training by FES during motor learning of the subjects. A steady improvement in the performance of the BCI system during FES-induced feedback was noted in these studies. The improvement in performance using FES was superior to the performance using visual feedback. The participants in this experiment also reported an increase in the level of concentration while performing the tasks, especially for the longer sessions, as compared to the experiments using FES feedback. The results suggest that electrical stimulation shows a greater improvement during motor training of participants than the standard visual feedback. It enhances the ability of the user to focus more on the task at hand by providing a natural proprioceptive feedback.
Conclusion BCI in combination of FES has been widely used in rehabilitation, but the question of its effect on the improvement in motor learning at the cortical level still remains, and thus the usage of FES as a neurofeedback. Neurofeedback is a vital component in BCI-based rehabilitation, as it aids in faster learning and better performance from the participants. It is without any doubt that the standard visual feedback has been successful in improving the performance of the BCI. But as mentioned earlier in the chapter, the type and design of the feedback depend on the objective of the experiment and individual users. Especially for stroke rehabilitation, FES would also provide a more realistic alternative to the classical visual feedback, as it would be intuitive and natural to the patients. Implementation of FES as a neurofeedback seems natural for BCI rehabilitation, as FES activates the sensory channel to provide maximal inflow in the brain to the efferent outflow of motor commands from the BCI to close the motor loop. As a result, both FES and BCI would influence each other and work to improve the cortical and peripheral learning of the patients. Results do suggest an improvement in motor learning while implementing FES as a neurofeedback as compared to the visual medium, but studies on a larger group of participants still needs to done for more convincing results. Positive results in this direction can provide a practical solution to enhance the motor recovery process of the patient.
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Lotte, F., Larrue, F., & Muhl, C. (2013). Flaws in current human training protocols for spontaneous brain-computer interfaces: Lessons learned from instructional design. Frontiers in Human Neuroscience, 7, 568. Meng, F., Tong, K., Chan, S., Wong, W., Lui, K., Tang, K., et al., (2008). BCI-FES training system design and implementation for rehabilitation of stroke patients. In 2008 IEEE international joint conference on neural networks (IEEE World Congress on Computational Intelligence) (pp. 4103 4106). Mukaino, M., Ono, T., Shindo, K., Fujiwara, T., Ota, T., Kimura, A., et al. (2014). Efficacy of brain-computer interface-driven neuromuscular electrical stimulation for chronic paresis after stroke. Journal of Rehabiliation Medicine, 46, 378 382. Mu¨ller, G. R., Neuper, C., Rupp, R., Keinrath, C., Gerner, H. J., & Pfurtscheller, G. (2003). Event-related beta EEG changes during wrist movements induced by functional electrical stimulation of forearm muscles in man. Neuroscience Letters, 340(2), 143 147. Neuper, C., Scherer, R., Wriessnegger, S., & Pfurtscheller, G. (2009). Motor imagery and action observation: Modulation of sensorimotor brain rhythms during mental control of a brain computer interface. Clinical Neurophysiology, 120, 239 247. Pfurtscheller, G., Mu¨ller, G. R., Pfurtscheller, J., Gerner, H. J., & Rupp, R. (2003). Thoughtcontrol of functional electrical stimulation to restore hand grasp in a patient with tetraplegia. Neuroscience Letters, 351(1), 33 36. Pfurtscheller, G., Mu¨ller-Putz, G. R., Pfurtscheller, J., & Rupp, R. (2005). EEG-based asynchronous BCI controls functional electrical stimulation in a tetraplegic patient. EURASIP Journal on Applied Signal Processing, 19, 3152 3155. Qiu, S., Yi, W., Xu, J., Qi, H., Du, J., & Wang, C. (2016). Event-related beta EEG changes during active, passive movement and functional electrical stimulation of the lower limb. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 24(2), 283 290. Riener, R., Ferrarin, M., Pavan, E. E., & Frigo, C. A. (2000). Patient-driven control of FES-supported standing up and sitting down: Experimental results. IEEE Transactions on Rehabilitation Engineering, 8(4), 523 529. Shoham, S., Halgren, E., Maynard, E. M., & Normann, R. A. (2001). Motor-cortical activity in tetraplegics. Nature, 413, 793. Takahashi, M., Takeda, K., Otaka, Y., Osu, R., Hanakawa, T., Gouko, M., & Ito, K. (2012). Event related desynchronization-modulated functional electrical stimulation system for stroke rehabilitation: A feasibility study. Journal of Neuroengineering Rehabilitation, 9, 56. Tidoni, E., Gergondet, P., Kheddar, A., & Aglioti, S. M. (2014). Audio-visual feedback improves the BCI performance in the navigational control of a humanoid robot. Frontiers in Neurorobotics, 8, 20.
Further reading Benjamin, E. J., Blaha, M. J., Chiuve, S. E., Cushman, M., Das, S. R., Deo, R., et al. (2017). Heart disease and stroke statistics—2017 update: A report from the American Heart Association. Circulation, 135, e146 e603. Cho, W., Vidaurre, C., Hoffmann, U., Birbaumer, N., & Ramos-Murguialday, A., (2011). Afferent and efferent activity control in the design of brain computer interfaces for motor rehabilitation. In 2011 Annual international conference of the IEEE Engineering in Medicine and Biology Society (pp. 7310 7315).
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Cincotti, F., Pichiorri, F., Arico`, P., Aloise, F., Leotta, F., & de Vico Fallani, F. (2012). EEG-based brain-computer interface to support post-stroke motor rehabilitation of the upper limb. In 2012 Annual international conference of the IEEE Engineering in Medicine and Biology Society (pp. 4112 4115). Cramer, S. C. (2011). Improving outcomes after stroke by LEAPS (Locomotor Experience Applied Post-Stroke) and bounds. Stroke, 42, 3659 3660. de Wit, D. C., Buurke, J. H., Nijlant, J. M., Ijzerman, M. J., & Hermens, H. J. (2004). The effect of an ankle-foot orthosis on walking ability in chronic stroke patients: A randomized controlled trial. Clinical Rehabilitation, 18, 550 557. Do, A. H., Wang, P. T., King, C. E., Abiri, A., & Nenadic, Z. (2011). Brain-computer interface controlled functional electrical stimulation system for ankle movement. Journal of Neuroengineering and Rehabilitation, 8, 49. Do, A.H., Wang, P.T., King, C.E., Schombs, A., Cramer, S.C., & Nenadic, Z. (2012). Braincomputer interface controlled functional electrical stimulation device for foot drop due to stroke. In Proceedings of the 34th annual international conference on IEEE EMBS (pp. 6414 6417). Dorsch, S., Ada, L., Canning, C. G., Al-Zharani, M., & Dean, C. (2012). The strength of the ankle dorsiflexors has a significant contribution to walking speed in people who can walk independently after stroke: An observational study. Archives of Physical Medical Rehabilitation, 93, 1072 1076. Ferreira, L. A. B., Neto, H. P., Grecco, L. A. C., Christova˜o, T. C. L., Duarte, N. A. C., Lazzari, R. D., et al. (2013). Effect of ankle-foot orthosis on gait velocity and cadence of stroke patients: A systematic review. Journal of Physical Therapy Science, 25, 1503 1508. Ibrahim, B. S. K. K., & Sherwani, F. (2014). Brain computer interface based functional electrical stimulation: An outline. In 2014 IEEE 19th International Functional Electrical Stimulation Society annual conference (IFESS) (pp. 1 5). King, C. E., Wang, P. T., McCrimmon, C.M., Chou, C. C. Y., Do, A. H., & Nenadic, Z. (2014). Brain-computer interface driven functional electrical stimulation system for overground walking in spinal cord injury participant. In 36th Annual international conference of the IEEE Engineering in Medicine and Biology Society (pp. 1238 1242). Kluding, P. M., Dunning, K., O’Dell, M. W., Wu, S. S., Ginosian, J., Feld, J., et al. (2013). Foot drop stimulation versus ankle foot orthosis after stroke: 30-Week outcomes. Stroke, 44, 1660 1669. Langhorne, P., Coupar, F., & Pollock, A. (2009). Motor recovery after stroke: A systematic review. The Lancet Neurology, 8, 741 754. Leung, J., & Moseley, A. (2003). Impact of ankle-foot orthoses on gait and leg muscle activity in adults with hemiplegia: Systematic literature review. Physiotherapy, 89, 39 55. Liepert, J. (2010). Evidence-based methods in motor rehabilitation after stroke. Fortschritte Neurologie Psychiatrie, 11(1), 5 10. Lord, S. E., McPherson, K., McNaughton, H. K., Rochester, L., & Weatherall, M. (2004). Community ambulation after stroke: How important and obtainable is it and what measures appear predictive? Archive of Physical Mededical Rehabilitation, 85, 234 239. McCrimmon, C. M., King, C. E., Wang, P. T., Cramer, S. C., Nenadic, Z., & Do, A. H. (2015). Brain-controlled functional electrical stimulation therapy for gait rehabilitation after stroke: A safety study. Journal of Neuroengineering and Rehabilitation, 12, 57. McGie, S. C., Zariffa, J., Popovic, M. R., & Nagai, M. K. (2015). Short-term neuroplastic effects of brain-controlled and muscle-controlled electrical stimulation. Neuromodulation, 18, 233 240.
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Merians, A. S., Poizner, H., Boian, R., Burdea, G., & Adamovich, S. (2006). Sensorimotor training in a virtual reality environment: Does it improve functional recovery poststroke? Neurorehabilitation and Neural Repair, 20, 252 267. Popovic, D. B., & Popovic, M. B. (2006). Hybrid assistive systems for rehabilitation: Lessons learned from functional electrical therapy in hemiplegics. In Proc. IEEE-EMBS ’06 (pp. 2146 2149). ¨ ., Brasil, F. L., et al. Ramos-Murguialday, A., Broetz, D., Rea, M., L¨aer, L., Yilmaz, O (2013). Brain-machine interface in chronic stroke rehabilitation: A controlled study. Annals of Neurology, 74, 100 108. Sunderland, A., Tinson, D. J., Bradley, E. L., Fletcher, D., Langton Hewer, R., & Wade, D. T. (1992). Enhanced physical therapy improves recovery of arm function after stroke. A randomised controlled trial. Journal of Neurology, Neurosurgery, and Psychiatry, 55 (7), 530 535. Takahashi, C. D., Der-Yeghiaian, L., Le, V., Motiwala, R. R., & Cramer, S. C. (2008). Robot-based hand motor therapy after stroke. Brain, 131, 425 437. Taub, E., Uswatte, G., & Pidikiti, R. (1999). Constraint-induced movement therapy: A new family of techniques with broad application to physical rehabilitation—A clinical review. Journal of Rehabilitation and Research and Development, 36, 237 251. Tyˇc, F., & Boyadjian, A. (2011). Plasticity of motor cortex induced by coordination and training. Clinical Neurophysiology, 122(1), 153 162. Van Peppen, R. P. S., Kwakkel, G., Wood-Dauphinee, S., Hendriks, H. J. M., Van der Wees, P. J., & Dekker, J. (2004). The impact of physical therapy on functional outcomes after stroke: What’s the evidence? Clinical Rehabilitation, 18, 833 862. Volpe, B. T., Lynch, D., Rykman-Berland, A., Ferraro, M., Galgano, M., & Hogan, N. (2008). Intensive sensorimotor arm training mediated by therapist or robot improves hemiparesis in patients with chronic stroke. Neurorehabilitation and Neural Repair, 22 (3), 305 310. Wade, D. T., Wood, V. A., Heller, A., Maggs, J., & Langton, H. R. (1987). Walking after stroke. Measurement and recovery over the first 3 months. Scandinavian Journal of Rehabilitation Medicine, 19, 25 30.
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Saugat Bhattacharyya1 and Mitsuhiro Hayashibe2 1 School of Computer Science & Electronic Engineering, University of Essex, Colchester, United Kingdom, 2Division of Mechanical Engineering, Tohoku University, Sendai, Japan
Introduction Strokes are one of the foremost reasons of disability in Western countries, with over 795,000 new cases in the United States each year (as mentioned in the 2017 report about heart disease and stroke statistics published by the American Heart Association). There are indications from studies that suggest a tremendous increase in numbers as the population ages further and stroke survival rates increase. In the European Union itself, it is projected that the percentage of the population over 65 years old will increase from 17.1% in 2008 to 30% in 2060, and the population over 80 years will rise from 4.4% to 12.1% over the same period (EUROSTAT population projections). Thus the incidence and prevalence of a first stroke in Europe is about 1.1 and 6 million, respectively. The present projection estimates that about 75% of people affected by a stroke will survive 1 year or more, and this proportion is estimated to increase in the future because of better quality pre- and postrehabilitation and enhancement of lifelong treatment procedures. It is reported that a meager amount (14%) of stroke survivors show complete recovery after upper limb rehabilitation at hospitals, while a huge number of 56% show no recovery at all. Currently, physical therapy is the most widely accepted procedure of rehabilitation for stroke patients. Methods such as intensive exercise and augmented feedback, constraint- induced movement therapy, and exercise in virtual environments with feedback to assist skills learning are a few measures preferred by physical therapy researchers and clinicians. Methods such as robotic assistive devices with sensory feedback are gaining precedence among the professionals to provide longterm therapy in a consistent and measurable manner. In the United States itself, more than 2 million stroke patients suffer from long-term gait even after physiotherapy and spontaneous recovery. Poststroke gait disability may create other morbities
Bioelectronics and Medical Devices. DOI: https://doi.org/10.1016/B978-0-08-102420-1.00037-6 Copyright © 2019 Elsevier Ltd. All rights reserved.
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such as diabetes, cardiovascular disease, and depression due to decreased participation in physical, social, and professional activities. Looking into the high risk posed to stroke survivors, there is a constant need to develop newer therapy strategies that provide substantial improvement in rehabilitation procedures. Currently available orthoses and assistive technologies such as functional electrical stimulation (FES) devices have not shown any lasting improvement in gait rehabilitation, and hence there is a rising attraction among researchers to incorporate brain computer interface (BCI) technology in poststroke rehabilitation therapy. BCIs aim at providing a new communication channel between the human brain and external devices without any neuromuscular intervention. BCIs translate signals originating from the central nervous system and recorded by devices such as electroencephalography (EEG) into control commands to control external devices such as prosthesis, wheelchairs, and mobile robots. Despite their growing popularity, BCI technologies are not yet ready for commercial or clinical applications because of not producing optimal performance in terms of accuracy and requiring a long training session, thus making it a costly affair. BCI has been considered as a potential alternative to improve standard motor therapy after stroke by taking into account the damaged motor network of the brain. On the other hand, FES is employed during rehabilitation to directly engage muscles of the targeted, damaged region (limb). FES is capable of restoring certain daily life skills for physically challenged patients by directly stimulating the targeted muscles group. Previous studies have reported on the ability of FES to elicit recovery of basic activities performed on a daily basis such as standing up, grasping, cycling, and walking by retraining the users on these tasks. FES-based rehabilitative systems do not employ the cortical activity of the patient (Cauraugh, Light, Kim, Thigpen, & Behrman, 2000; Chen, Yu, Huang, Ann, & Chang, 1997; Kojovic, Djuric-Jovicic, Dosen, Popovic, & Popovic, 2009; Riener, Ferrarin, Pavan, & Frigo, 2000) and hence can lead to monotonicity and lack of interest at relearning the activity among the patients. It has been hypothesized that lasting neurological and functional enhancement in motor recovery can be accomplished when the activation of upper motor neurons (UMNs) in the poststroke cerebral cortex are coupled with the activation of lower motor neurons via FES. Employing BCI with FES therapy would train and assist patients to activate UMNs and have lasting impact on the rehabilitation rather than simply using passive (nonvolitional) electrical stimulation. The implementation of brain-controlled FES systems as new physiotherapeutic alternative has been suggested by many researchers in Daly et al. (2009), Takahashi et al. (2012), and Mukaino, Ono, and Shindo (2014). Therapy based on BCI FES has been reported to be safe, with a large proportion of users experiencing improvements in their gait speed and pattern. Further studies on a controlled population are required to investigate the potential efficacy and optimal duration of BCI FES therapy, the targeted clinical population to benefit most from this therapy, its performance on combination with other traditional conventional therapies, and most importantly, the neurobiological principles that govern any functional changes. In hindsight, it seems quite natural to combine FES rehabilitation with BCI systems, where FES can activate the sensory channel to provide information in
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the brain and the BCI would generate the necessary motor commands to close the motor loop. Hence, both FES and BCI will influence each other and work together to improve the cortical and peripheral learning of the user simultaneously.
Combining brain computer interface with functional electrical stimulation BCI provides an efferent outflow of commands from the electrical signals generated in the brain, while the FES activates the sensory channel to provide a maximal inflow in the brain. Thus it is quite natural to combine FES rehabilitation with BCI systems as it is expected that both FES and BCI would influence each other positively and augment the retraining of users to enable regulation of upper and lower limbs through brain signals to achieve motor rehabilitation. Generally, the BCI regulates the motor rehabilitation by collecting and processing signals related to motor action in real time and converting them into commands that can be easily understood by peripheral devices connected to the BCI system. This allows the EEG-based BCI to circumvent damaged motor pathways and send the control the control command directly to the targeted limb in paralytic patients. A block diagram of the BCI FES system is shown in Fig. 30.1. To date, motor functions (such as grasping) are restored by using FES. A prerequisite for such restoration is when the nerves connecting the ventral roots of the spinal cord to the peripheral muscle are still intact. Surface or subcutaneous electrodes are placed near the motor point of the targeted muscles and short, constant-current pulses are applied to it. This leads to a depolarization of the action potential of the nerve membrane, which further leads to a contraction of the innervated muscle fibers similar to the same at physiological conditions. A tetanic contraction of muscles is achieved due to temporal summation of pulses at higher frequency (20 Hz). Pfurtscheller, Mu¨ller, Pfurtscheller, Gerner, and Rupp (2003) and Pfurtscheller, Mu¨ller-Putz, Pfurtscheller, and Rupp (2005) initiated the integration of noninvasive
Figure 30.1 Block diagram of a BCI FES system. The BCI decodes the brain signals to send to the FES device, which in turn activates the muscles of the targeted limb. BCI, Brain computer interface; FES, functional electrical stimulation.
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EEG-based BCI with FES rehabilitation. They aimed at controlling hand grasping using imagined motor responses. The task involved grasping of an object and moving it to another place. They also presented the first empirical evidence resembling voluntary wrist movements, represented by a short-lasting event related desynchronisation (ERD) in the alpha (mu) and lower beta band and a subsequent beta event related synchronisation (ERS), when induced by 20 Hz electrical stimulation. The group reported a beta oscillation burst localized predominantly over the sensorimotor cortex of the contralateral and the mid-central region of the targeted limb. Such a pattern, as known from previous EEG studies on voluntary movements, indicates a (de-)activation of the sensorimotor area and adjoining premotor and related areas. The processes occurring in the sensorimotor region during FES of the forearm muscles are similar to the ones during active voluntary hand movements. This indicates that afferent proprioceptive inputs (from joint, tendon, and muscle receptors) to the primary somatosensory area have an influence during the movement and may partly be due to the postmovement beta ERS as a reflection of a desynchronization of the motor cortex after termination of the FES-induced movement. It is also necessary to quantitatively compare the effects of FES-induced movement to that of active and passive movement in the brain. Active movements are carried out by the participants themselves without any assistance in any form. For passive movements, the participants carry out the tasks with help from a mechanical device or human assistance. FES-induced movements stimulate the targeted limb of the participant using FES. Muller et al. (2003) compared the three conditions for upper limbs while Qiu et al. (2016) compared it for lower limbs for both healthy subjects and hemiplegic stroke patients. Mu¨ller et al. (2003) reported no generation of ERD patterns, prior to the onset of movement, during FES-induced state. This indicates no occurrence of movement planning and preparation during FES-induced movement. On the other hand, Qiu et al. (2016) stated that stroke patients exhibited ERD patterns in the central region of the brain during FES-induced movements while there were no such behaviors for healthy subjects. It was reported that ERD in the beta frequency band were significantly correlated with active movements, whereas no ERD patterns were generated during passive movements. This indicates that EEG oscillatory pattern under FES-induced movement originates during active movement and is carried forward during the FES-induced state. Instead of somatosensory signals, Chu, Zhao, Han, Zhao, and Yao (2014) employed EEG signals generated through steady-state visual evoked potential (SSVEP) in the BCI to activate the necessary motor commands. The basic idea of this BCI was to acquire responses generated due to the SSVEP elicited by stimulation of a flashing red square at three frequencies, 20, 15, 12 Hz. The participants would need to focus their attention on the flickering square to generate the required control command. Furthermore, an integrated iterative learning control with proportional derivative (PD) feedback was used to improve the performance of the FES. The controller would optimize the stimulating sequence to make the upper limb track the preplanned trajectory. The results from this study show the effectiveness of the integration of SSVEP-BCI with FES that can track a desired trajectory at a limited precision.
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A study on functional magnetic resonance imaging (fMRI) data by Shoham, Halgren, Maynard, and Normann (2001) dealing with tetraplegic patients shows that even chronically deefferented sensorimotor representation areas respond to attempts at movement and display a minimal somatotopical reorganization. However, the impact of FES on the EEG reactivity in paralyzed patients remains to be validated and is a question for future research. The main purpose of BCI FES studies is to design a user-efficient rehabilitative tool for users, and thus in the next section we discuss BCI FES studies on the patient population.
Brain computer interface functional electrical stimulation in rehabilitation BCI technology has huge scope in the fields of medical rehabilitation as a form of assistive tool. This technology was developed with an aim to allow patients suffering from severe motor disabilities such as amyotrophic lateral sclerosis to communicate and interact with their external environment. The BCI-based technology and its corresponding rehabilitative therapy have a similar working principle to classical neuromotor rehabilitation. It would provide the physical therapist with a monitoring instrument that would assess the patient’s performance and level of learning in the rehabilitative cognitive task and assist the patient to recover lost functionality of limbs or muscles. Rehabilitation exercise based on BCIs promotes neuroplasticity in the motor region of the brain and hence would lead to a better learning/relearning of movement activities in the motor areas. The idea of a BCI-based FES system is vital in the recovery process of patients suffering from stroke and thus is valuable in the field of medicine. The current available methods in stroke rehabilitation are passive in nature, and its effectiveness is limited and time-consuming. The BCI FES system aims at employing brain signals, associated with motor movement (motor imagery), to generate activation commands (by using digital signal processing and machine learning methods) that would control the functionality of a FES system. The BCI FES allows the patient to modulate their own rehabilitation in manners they deem to be suitable and hence improves upon the limitation of passive rehabilitation. It has been found that BCI FES systems stimulated the brain plasticity of patients and hence improved the efficiency of their rehabilitation. However, every BCI system would require feedback (in form of visual, auditory, or tactile sensation) to be provided to the patient to allow them to actively participate in their rehabilitation and also not cause any form of aversion in patient. Functional electric stimulus, on the other hand, reduces muscle spasticity and joint flexibility and hence can be said to be an efficient proprioceptive feedback to BCI. In such a setup, the BCI system detects motor intention (as ERD phenomena generated due to voluntary movement intention) of stroke patients and subsequently triggers the FES when motor intention is detected.
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FES and motor imagery BCI have been widely used in the rehabilitation training of stroke patients. BCI solely based on motor imagery exhibits the problem of performance variability due to the absence of feedback and leads to monotonicity toward the task at hand. BCI inherently links the brain and the outward environment directly and has shown great perspective to help patients to regain or recover their ability to communicate and control. The introduction of FES would further enhance the performance of the BCI and help in providing a more inclusive rehabilitation training to patients. A study by Chung, Kim, Park, and Lee (2015) investigated the BCI FES system for clinical rehabilitation of patients suffering from poststroke hemiplegia. It was reported that the motor function was enhanced due to the brain plasticity occurring in the affected motor-related cortex of patients which were activated significantly during the rehabilitation period. The ERD power of motor-related cortex was easily distinguishable from signals in the relaxation state, which proved that intact brain neurons were activated to replace the impaired cortex areas by brain plasticity. Results showed that patients who completed the rehabilitation tests showed significant recovery of target muscle function and exhibited functional improvements, especially in movements involving the extensor digitorum. All patients exhibited a bilateral representation of the motor action while the discriminant frequency components were consistently localized in the mu and beta bands. Remarkably, the patient with the worst conditions exhibited functional recovery from a totally paretic arm to a very limited but still noticeable voluntary activity of the fist. These results validate that the incorporation of BCI with FES in rehabilitation leads to an enhancement of functional recovery of targeted muscles. Another group of researchers, Daly et al. (2008), developed a BCI FES system interfaced with a robot for convenience by stroke survivors for upper limb (wrist/ hand) motor rehabilitation. The enforced motor tasks are wrist/hand or shoulder/ elbow tasks that were imagined, attempted, or imposed relaxation of muscles. Subjects showed high performance for imagined and attempted and less for relaxation tasks, which indicated the practicability of using the BCI FES/robot for the purpose of motor rehabilitation. The same system was also tested on chronic stroke survivors who were able to generate characteristic brain signals for imagined and attempted wrist/hand and shoulder/elbow tasks but could not generate signals during relaxation tasks.
Importance and types of brain computer interface feedback The design of a user-efficient feedback is an important issue in motor imagery BCI systems. Most researchers focus on the machine learning aspect of BCI by concentrating on improving the classification algorithms, while the human and training aspect of BCI is neglected. Developing a suitable training paradigm
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could help in improving the performance of BCI and make the user comfortable with the paradigm. If feedback of a user’s performance is provided, then the BCI users tend to coadapt with the system. Hence, the inclusion of feedback to BCI experiments can enhance the learning among users and thus improve their performance. Visual feedback (like a moving cursor or target on the computer screen) is the most common form of feedback provided to users in BCI. Feedback in the form of grasping hands (realistic feedback) or extended bar (abstract feedback) were employed in Neuper, Scherer, Wriessnegger, and Pfurtscheller (2009). In Barbero and Grosse-Wentrup (2010), the feedback accuracy was biased to investigate the performance of BCI. The results indicated that better performers were impeded by unreliable feedback, while it was noted that poor BCI performers benefited greatly with positive feedback. Gonzalez-Franco, Peng, Dan, Bo, and Shangkai (2011) also reported that a system with negative feedback had a superior learning effect on motor imagery BCIs as compared to positive feedback. It was reported by Angulo-Sherman and Gutie´rrez (2014) that biasing feedback could not immediately boost subjects’ performance in the same session. However, it could change the trend of motor imagery learning for future sessions. There is no conclusive evidence about which feedback must be provided, as the results are highly variable among individuals. While designing a feedback for BCI systems, one must keep in mind to model a simple and user-friendly feedback presentation that can help naive users to improve their own individual performance by numerous training and frequent adaptation of the system more efficiently. Also, feedback in BCI also depends in the personality of the users, so some may benefit from positive feedback while others may benefit from negative ones. Presenting a feedback through a visual medium is the most preferred feedback paradigm on BCI because it can be simple in nature and is known to provide the largest improvement in performance while controlling a BCI system. Components of the visual system such as vision, visual attention, and focusing gaze are directly involved while maintaining a direct dynamic contact with the environment. But there may be instances where feedback through a visual medium could serve as a deterrent (like working on conditions with poor or no visibility or when the visual system is compromised). On such occasions, auditory or vibrotactile feedback could serve the purpose and has been studied among researchers. Angulo-Sherman and Gutie´rrez (2014) evaluated the influence of different sensory modalities on the performance of a motor imagery (MI)-BCI system. They devised two different ways of feedback, which were presented to the users. They are positive feedback, which indicates a good performance, and negative feedback, which motivated the user to perform better in the task. Results from this study revealed that none of the feedback—auditory, vibrotactile, and the classical visual —turned out to be superior to one another and so it was concluded that the performance achieved depends more on the user rather than the feedback provided. Thus they strongly suggested that the BCI systems require a more personalized feedback strategy that caters to the preference of the user.
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Visual feedback Visual feedback is displayed to the participants on a screen in the form of a more realistic presentation (like the target limbs) or in the form of abstract shapes (like an elongated bar). Sometimes, the visual feedback can be implemented as a game where the subject moves an object (say, a ball) to a target area. Controlling of mobile robots or wheelchairs can be considered as visual feedback, as the subject directly visualizes the performance of the devices within his/her visual range. Here, we will discuss several examples where realistic visual feedbacks were implemented. A hypothesis exists which suggests that the visual feedback could improve the learning experience of motor-related BCI tasks if they are similar to daily activities (such as walking, standing) performed by the human body. A study conducted by Alimardani, Nishio, and Ishiguro (2016) showed that with changes in the visual feedback, the motor imagery skills learned while controlling a human-like robotic hand are more robust with time. The same group also studied the influence of realistic visual feedback on the motor imagery BCI performance of users. They studied the effect of positive and negative feedback bias on subjects’ BCI performance and motor imagery skills. It was noted that the feedback had no influence in the classification score, but on evaluation of brain activity patterns, it was revealed that the positive bias of the feedback improved the subjects’ self-regulation of motor imagery signals.
Vibrotactile feedback When it is not possible to provide visual feedback, then vibrotactile feedback systems can be used to transmit information through a tactile interface. Vibrotactile feedback is advantageous in its way because it is easy to implement and safe and does not require the user to maintain focus on the visual attention. As with all types of neurofeedback, vibrotactile feedback also requires an appropriate amount of training for the BCI task at hand. It also does not interfere with visual stimuli if employed simultaneously. It could also improve the performance of the BCI when the subject’s attention is highly loaded by a simultaneous visual task. Chatterjee, Aggarwal, Ramos, Acharya, and Thakor (2007) showed that all users using only vibrotactile feedback could operate a three-state motor imagery BCI. Certain variations in tactor placement led to a notable bias in accuracy. The results indicated that the different locations of vibrotactile stimulus influenced the user’s modulation of mu-rhythm activity toward desynchronization generated by imagination of the ipsilateral hand. They also reported that the classifier performance from the vibrotactile biofeedback trials was far better than predicted by random chance. The biasing effect may be solved by training and modifying signal processing elements of the BCI. To further compensate for the stimuation and reduction of bias, one may adjust the thresholds and weights of the machine learning technique accordingly.
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Possibility of functional electrical stimulation as feedback FES technologies are used to provide restoration of normal movement in patients with paralysis due to UMN diseases such as stroke, multiple sclerosis, and spinal cord injury. Effects of FES treatment can occur by peripheral or central mechanisms, where the former uses FES to stimulate the patient’s remaining motor units to enhance muscular strength, increase range of motion, and reduce stiffness, while the latter treatment occurs by the reorganization of the cortex based on neurophysiological responses to help control movement through neural plasticity in stroke patients. An existing hypothesis behind the augmented movement therapy by FES assumes that cortical plasticity plays a more major role in recovery rather than peripheral mechanism. The hypothesis was further confirmed in motor training tasks with physiological tests involving transcranial magnetic stimulation and imaging based on fMRI. Functional recovery depends on coherence between afferent and efferent neural activity, where the role of efferent activity for recovery has been demonstrated with motor imagery BCI and the afferent activities are characterized by the FES activating the sensory channels to the brain. If this afferent activity is coherent with the efferent activity then the loop for motor control would be a closed control system with the BCI being used to control the FES-based rehabilitation by decoding the motor intentions of the patient while the FES could provide a form of natural proprioceptive feedback to the patient in the cortical level. Fig. 30.2 provides an intuitive representation of the BCI system with FES as neurofeedback.
Brain biosignal recording (EEG, NIRS)
Brain plasticity
Signal processing and feature selection Afferent sensory feedback Motor intention adaptive decoding
Motor learning for multiple muscle control signal generation Stim
EMG Wireless FES muscle stimulation with evoke EMG feedback
Figure 30.2 Example of implementing FES as a neurofeedback to the BCI tasks. BCI, Brain computer interface; FES, functional electrical stimulation.
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BCI systems are inherently plagued with the presence of massive biological and externally induced artefacts along with the signals of interest. For a BCI FES system, brain signals would usually be contaminated with electrical noise and neural correlates of muscle contractions and movements originating due to the FES stimulation. The artificial neural contamination comes from the FES activation of muscle contraction and limb movement, which are also used as a source of information for the BCI. These phenomena thus could either bias or reinforce an online sensory motor rhythm (SMR)-based BCI. Preliminary studies by Bhattacharyya, Clerc, and Hayashibe (2016) on FES in the form of a neurofeedback in BCI have suggested a positive training by FES during motor learning of the subjects. A steady improvement in the performance of the BCI system during FES-induced feedback was noted in these studies. The improvement in performance using FES was superior to the performance using visual feedback. The participants in this experiment also reported an increase in the level of concentration while performing the tasks, especially for the longer sessions, as compared to the experiments using FES feedback. The results suggest that electrical stimulation shows a greater improvement during motor training of participants than the standard visual feedback. It enhances the ability of the user to focus more on the task at hand by providing a natural proprioceptive feedback.
Conclusion BCI in combination of FES has been widely used in rehabilitation, but the question of its effect on the improvement in motor learning at the cortical level still remains, and thus the usage of FES as a neurofeedback. Neurofeedback is a vital component in BCI-based rehabilitation, as it aids in faster learning and better performance from the participants. It is without any doubt that the standard visual feedback has been successful in improving the performance of the BCI. But as mentioned earlier in the chapter, the type and design of the feedback depend on the objective of the experiment and individual users. Especially for stroke rehabilitation, FES would also provide a more realistic alternative to the classical visual feedback, as it would be intuitive and natural to the patients. Implementation of FES as a neurofeedback seems natural for BCI rehabilitation, as FES activates the sensory channel to provide maximal inflow in the brain to the efferent outflow of motor commands from the BCI to close the motor loop. As a result, both FES and BCI would influence each other and work to improve the cortical and peripheral learning of the patients. Results do suggest an improvement in motor learning while implementing FES as a neurofeedback as compared to the visual medium, but studies on a larger group of participants still needs to done for more convincing results. Positive results in this direction can provide a practical solution to enhance the motor recovery process of the patient.
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References Alimardani, M., Nishio, S., & Ishiguro, H. (2016). The importance of visual feedback design in BCIS; from embodiment to motor imagery learning. PLoS One, 11(9), e0161945. Allison, B. Z., Leeb, R., Brunner, C., Mu¨ller-Putz, G. R., Bauernfeind, G., Kelly, J. W., & Neuper, C. (2011). Towards smarter BCIs: Extending BCIs through hybridization and intelligent control. Journal of Neural Engineering, 9(1), 013001. Angulo-Sherman, I. N., & Gutie´rrez, D. (2014). Effect of different feedback modalities in the performance of brain-computer interfaces. In 2014 International conference on electronics, communications and computers (CONIELECOMP) (pp. 14 21). ´ ., & Grosse-Wentrup, M. (2010). Biased feedback in brain-computer interfaces. Barbero, A Journal of Neuroenineering Rehabilitation, 7, 1 4. Bhattacharyya, S., Clerc, M., & Hayashibe, M. (2016). A study on the effect of electrical stimulation during motor imagery learning in brain-computer interfacing. In 2016 IEEE international conference on systems, man, and cybernetics (SMC) (pp. 002840 002845). Cauraugh, J., Light, K., Kim, S., Thigpen, M., & Behrman, A. (2000). Chronic motor dysfunction after stroke: Recovering wrist and finger extension by electromyographytriggered neuromuscular stimulation. Stroke, 31(6), 1360 1364. Chatterjee, A., Aggarwal, V., Ramos, A., Acharya, S., & Thakor, N. V. (2007). A braincomputer interface with vibrotactile biofeedback for haptic information. Journal of Neuroengineering and Rehabilitation, 4, 40. Chen, J. J., Yu, N.-Y., Huang, D. G., Ann, B. T., & Chang, G. C. (1997). Applying fuzzy logic to control cycling movement induced by functional electrical stimulation. IEEE Transactions on Rehabilitation Engineering, 5(2), 158 169. Chu, Y., Zhao, X., Han, J., Zhao, Y., & Yao, J. (2014). SSVEP based brain-computer interface controlled functional electrical stimulation system for upper extremity rehabilitation. In 2014 IEEE international conference on robotics and biomimetics (ROBIO 2014) (pp. 2244 2249). Bali. Chung, E., Kim, J.-H., Park, D.-S., & Lee, B.-H. (2015). Effects of brain-computer interfacebased functional electrical stimulation on brain activation in stroke patients: A pilot randomized controlled trial. Journal of Physical Therapy Science, 27(3), 559 562. Daly, J. J., Cheng, R., Hrovat, K., Rogers, J. M., Litinas, K., & Dohring, M. E. (2008). Development and testing of non-invasive BCI 1 FES/robot system for use in motor relearning after stroke. In Proceedings of the 13th annual conference of the International Functional Electrical Stimulation Society “from movement to mind” (Vol. 53; pp. 200 202). Daly, J. J., Cheng, R., Rogers, J., Litinas, K., Hrovat, K., & Dohring, M. (2009). Feasibility of a new application of noninvasive brain computer interface (BCI): A case study of training for recovery of volitional motor control after stroke. Journal of Neurological Physical Therapy, 33, 203 211. Gonzalez-Franco, M., Peng, Y., Dan, Z., Bo, H., & Shangkai, G. (2011). Motor imagery based brain-computer interface: A study of the effect of positive and negative feedback. In Engineering in Medicine and Biology Society (EMBC), annual international conference of the IEEE (Boston, MA) (pp. 6323 6326). Kojovic, J., Djuric-Jovicic, M., Dosen, S., Popovic, M. B., & Popovic, D. B. (2009). Sensordriven four-channel stimulation of paretic leg: Functional electrical walking therapy. Journal of Neuroscience Methods, 181(1), 100 105.
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Lotte, F., Larrue, F., & Muhl, C. (2013). Flaws in current human training protocols for spontaneous brain-computer interfaces: Lessons learned from instructional design. Frontiers in Human Neuroscience, 7, 568. Meng, F., Tong, K., Chan, S., Wong, W., Lui, K., Tang, K., et al., (2008). BCI-FES training system design and implementation for rehabilitation of stroke patients. In 2008 IEEE international joint conference on neural networks (IEEE World Congress on Computational Intelligence) (pp. 4103 4106). Mukaino, M., Ono, T., Shindo, K., Fujiwara, T., Ota, T., Kimura, A., et al. (2014). Efficacy of brain-computer interface-driven neuromuscular electrical stimulation for chronic paresis after stroke. Journal of Rehabiliation Medicine, 46, 378 382. Mu¨ller, G. R., Neuper, C., Rupp, R., Keinrath, C., Gerner, H. J., & Pfurtscheller, G. (2003). Event-related beta EEG changes during wrist movements induced by functional electrical stimulation of forearm muscles in man. Neuroscience Letters, 340(2), 143 147. Neuper, C., Scherer, R., Wriessnegger, S., & Pfurtscheller, G. (2009). Motor imagery and action observation: Modulation of sensorimotor brain rhythms during mental control of a brain computer interface. Clinical Neurophysiology, 120, 239 247. Pfurtscheller, G., Mu¨ller, G. R., Pfurtscheller, J., Gerner, H. J., & Rupp, R. (2003). Thoughtcontrol of functional electrical stimulation to restore hand grasp in a patient with tetraplegia. Neuroscience Letters, 351(1), 33 36. Pfurtscheller, G., Mu¨ller-Putz, G. R., Pfurtscheller, J., & Rupp, R. (2005). EEG-based asynchronous BCI controls functional electrical stimulation in a tetraplegic patient. EURASIP Journal on Applied Signal Processing, 19, 3152 3155. Qiu, S., Yi, W., Xu, J., Qi, H., Du, J., & Wang, C. (2016). Event-related beta EEG changes during active, passive movement and functional electrical stimulation of the lower limb. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 24(2), 283 290. Riener, R., Ferrarin, M., Pavan, E. E., & Frigo, C. A. (2000). Patient-driven control of FES-supported standing up and sitting down: Experimental results. IEEE Transactions on Rehabilitation Engineering, 8(4), 523 529. Shoham, S., Halgren, E., Maynard, E. M., & Normann, R. A. (2001). Motor-cortical activity in tetraplegics. Nature, 413, 793. Takahashi, M., Takeda, K., Otaka, Y., Osu, R., Hanakawa, T., Gouko, M., & Ito, K. (2012). Event related desynchronization-modulated functional electrical stimulation system for stroke rehabilitation: A feasibility study. Journal of Neuroengineering Rehabilitation, 9, 56. Tidoni, E., Gergondet, P., Kheddar, A., & Aglioti, S. M. (2014). Audio-visual feedback improves the BCI performance in the navigational control of a humanoid robot. Frontiers in Neurorobotics, 8, 20.
Further reading Benjamin, E. J., Blaha, M. J., Chiuve, S. E., Cushman, M., Das, S. R., Deo, R., et al. (2017). Heart disease and stroke statistics—2017 update: A report from the American Heart Association. Circulation, 135, e146 e603. Cho, W., Vidaurre, C., Hoffmann, U., Birbaumer, N., & Ramos-Murguialday, A., (2011). Afferent and efferent activity control in the design of brain computer interfaces for motor rehabilitation. In 2011 Annual international conference of the IEEE Engineering in Medicine and Biology Society (pp. 7310 7315).
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Cincotti, F., Pichiorri, F., Arico`, P., Aloise, F., Leotta, F., & de Vico Fallani, F. (2012). EEG-based brain-computer interface to support post-stroke motor rehabilitation of the upper limb. In 2012 Annual international conference of the IEEE Engineering in Medicine and Biology Society (pp. 4112 4115). Cramer, S. C. (2011). Improving outcomes after stroke by LEAPS (Locomotor Experience Applied Post-Stroke) and bounds. Stroke, 42, 3659 3660. de Wit, D. C., Buurke, J. H., Nijlant, J. M., Ijzerman, M. J., & Hermens, H. J. (2004). The effect of an ankle-foot orthosis on walking ability in chronic stroke patients: A randomized controlled trial. Clinical Rehabilitation, 18, 550 557. Do, A. H., Wang, P. T., King, C. E., Abiri, A., & Nenadic, Z. (2011). Brain-computer interface controlled functional electrical stimulation system for ankle movement. Journal of Neuroengineering and Rehabilitation, 8, 49. Do, A.H., Wang, P.T., King, C.E., Schombs, A., Cramer, S.C., & Nenadic, Z. (2012). Braincomputer interface controlled functional electrical stimulation device for foot drop due to stroke. In Proceedings of the 34th annual international conference on IEEE EMBS (pp. 6414 6417). Dorsch, S., Ada, L., Canning, C. G., Al-Zharani, M., & Dean, C. (2012). The strength of the ankle dorsiflexors has a significant contribution to walking speed in people who can walk independently after stroke: An observational study. Archives of Physical Medical Rehabilitation, 93, 1072 1076. Ferreira, L. A. B., Neto, H. P., Grecco, L. A. C., Christova˜o, T. C. L., Duarte, N. A. C., Lazzari, R. D., et al. (2013). Effect of ankle-foot orthosis on gait velocity and cadence of stroke patients: A systematic review. Journal of Physical Therapy Science, 25, 1503 1508. Ibrahim, B. S. K. K., & Sherwani, F. (2014). Brain computer interface based functional electrical stimulation: An outline. In 2014 IEEE 19th International Functional Electrical Stimulation Society annual conference (IFESS) (pp. 1 5). King, C. E., Wang, P. T., McCrimmon, C.M., Chou, C. C. Y., Do, A. H., & Nenadic, Z. (2014). Brain-computer interface driven functional electrical stimulation system for overground walking in spinal cord injury participant. In 36th Annual international conference of the IEEE Engineering in Medicine and Biology Society (pp. 1238 1242). Kluding, P. M., Dunning, K., O’Dell, M. W., Wu, S. S., Ginosian, J., Feld, J., et al. (2013). Foot drop stimulation versus ankle foot orthosis after stroke: 30-Week outcomes. Stroke, 44, 1660 1669. Langhorne, P., Coupar, F., & Pollock, A. (2009). Motor recovery after stroke: A systematic review. The Lancet Neurology, 8, 741 754. Leung, J., & Moseley, A. (2003). Impact of ankle-foot orthoses on gait and leg muscle activity in adults with hemiplegia: Systematic literature review. Physiotherapy, 89, 39 55. Liepert, J. (2010). Evidence-based methods in motor rehabilitation after stroke. Fortschritte Neurologie Psychiatrie, 11(1), 5 10. Lord, S. E., McPherson, K., McNaughton, H. K., Rochester, L., & Weatherall, M. (2004). Community ambulation after stroke: How important and obtainable is it and what measures appear predictive? Archive of Physical Mededical Rehabilitation, 85, 234 239. McCrimmon, C. M., King, C. E., Wang, P. T., Cramer, S. C., Nenadic, Z., & Do, A. H. (2015). Brain-controlled functional electrical stimulation therapy for gait rehabilitation after stroke: A safety study. Journal of Neuroengineering and Rehabilitation, 12, 57. McGie, S. C., Zariffa, J., Popovic, M. R., & Nagai, M. K. (2015). Short-term neuroplastic effects of brain-controlled and muscle-controlled electrical stimulation. Neuromodulation, 18, 233 240.
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Merians, A. S., Poizner, H., Boian, R., Burdea, G., & Adamovich, S. (2006). Sensorimotor training in a virtual reality environment: Does it improve functional recovery poststroke? Neurorehabilitation and Neural Repair, 20, 252 267. Popovic, D. B., & Popovic, M. B. (2006). Hybrid assistive systems for rehabilitation: Lessons learned from functional electrical therapy in hemiplegics. In Proc. IEEE-EMBS ’06 (pp. 2146 2149). ¨ ., Brasil, F. L., et al. Ramos-Murguialday, A., Broetz, D., Rea, M., L¨aer, L., Yilmaz, O (2013). Brain-machine interface in chronic stroke rehabilitation: A controlled study. Annals of Neurology, 74, 100 108. Sunderland, A., Tinson, D. J., Bradley, E. L., Fletcher, D., Langton Hewer, R., & Wade, D. T. (1992). Enhanced physical therapy improves recovery of arm function after stroke. A randomised controlled trial. Journal of Neurology, Neurosurgery, and Psychiatry, 55 (7), 530 535. Takahashi, C. D., Der-Yeghiaian, L., Le, V., Motiwala, R. R., & Cramer, S. C. (2008). Robot-based hand motor therapy after stroke. Brain, 131, 425 437. Taub, E., Uswatte, G., & Pidikiti, R. (1999). Constraint-induced movement therapy: A new family of techniques with broad application to physical rehabilitation—A clinical review. Journal of Rehabilitation and Research and Development, 36, 237 251. Tyˇc, F., & Boyadjian, A. (2011). Plasticity of motor cortex induced by coordination and training. Clinical Neurophysiology, 122(1), 153 162. Van Peppen, R. P. S., Kwakkel, G., Wood-Dauphinee, S., Hendriks, H. J. M., Van der Wees, P. J., & Dekker, J. (2004). The impact of physical therapy on functional outcomes after stroke: What’s the evidence? Clinical Rehabilitation, 18, 833 862. Volpe, B. T., Lynch, D., Rykman-Berland, A., Ferraro, M., Galgano, M., & Hogan, N. (2008). Intensive sensorimotor arm training mediated by therapist or robot improves hemiparesis in patients with chronic stroke. Neurorehabilitation and Neural Repair, 22 (3), 305 310. Wade, D. T., Wood, V. A., Heller, A., Maggs, J., & Langton, H. R. (1987). Walking after stroke. Measurement and recovery over the first 3 months. Scandinavian Journal of Rehabilitation Medicine, 19, 25 30.