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Neurofeedback systems and emotions monitoring
Symposia Abstracts / International Journal of Psychophysiology 85 (2012) 291–360
Personalizing neurofeedback training with EMG control in ADHD children E.A. Sapina State research Institute of Molecular Biology and Biophysics, Siberian Branch, Russian Academy of Medical Science, Novosibirsk, Russian Federation Attention deficit/hyperactivity disorder (ADHD) is defined as a behavior disorder in which the essential features are signs of developmentally inappropriate inattention, impulsivity, and hyperactivity. Theta-beta ratio is traditionally used in neurofeedback training (NFT) protocols to treat children with ADHD (Monastra et al., 2005). Nevertheless increasing cognitive performance efficiency in children with ADHD is also very important for treatment. EEG cognitive efficiency predictors are individual alpha activity indices, individual alpha peak frequency (IAPF), alpha band width and alpha amplitude desynchronization in response to eyes open (Bazanova, 2009). Besides ADHD subjects show high psycho emotional tension which is associated with increased frontal muscle EMG amplitude (Bakhshayesh et al 2011). The aim of this study was to refine NFT protocol for ADHD treatment. To achieve the aim first alpha rhythm peculiarities should be studied in ADHD in comparison with healthy children, second the effect of muscle tension control simultaneously with theta-beta ratio decreasing NFT should be studied. Thirty two children 6–9 years old diagnosed with ADHD and 13 healthy children the same age were investigated. Children with ADHD were randomly assigned to either the individual NFT group (INFT; n = 21) or the standard NFT group (SNFT; n = 11). Treatment for both groups consisted of 10 15 min sessions. Pre- and post-treatment assessment consisted of psychophysiological measures, behavioral rating scales completed by parents and teachers, as well as psychometric measures. Psychometric tests included attention test, cognitive task tests (verbal test, Kreapelin test modified for children) and anxiety test (Tamml et al. 2000). EEG and EMG were recorded in rest and during cognitive task. SNFT group had 10 sessions with the use of standard frequency domain power of the ranges: theta (4–8 Hz), alpha (8–12 Hz), and beta-1 (13–18 Hz). INFT group—with individual EEG ranges determined according the IAPF. Each theta-beta decreasing NFT session was accompanied with simultaneously forehead muscle relaxation. After 6 months both ADHD and healthy children were retested. Inter individual comparison showed significantly higher muscle tension level both in rest and during cognitive task performance in ADHD, than in healthy children in pre-treatment period. Also individual alpha peak frequency, alpha band width and desynchronization reaction were lower in ADHD than in healthy children. After 10 NFT sessions EMG and theta-beta ratio decreased significantly in INFT group and in those SNFT participants whose IAPF was ≥10 Hz. In SNFT participants with IAPF b 8 Hz NFT was ineffective. IAPF, alpha band width, amount of alpha suppression increased after effective NFT. Six month delayed test showed neurofeedback effect saving and no change in healthy children. This study results showed significant individual alpha frequency EEG pattern contribution and muscle tension control in neurofeedback training effectiveness for ADHD treatment. doi:10.1016/j.ijpsycho.2012.06.147
Neurofeedback systems and emotions monitoring O. Sourina, Y. Liu, M.K. Nguyen, Q. Wang Nanyang Technological University, Singapore Real-time emotion recognition from EEG could be used for emotions monitoring to study correlation between the user's emotions and
345
neurofeedback (NF) game performance. Then, the adaptive neurofeedback games could be designed where the NF system parameters/ thresholds could be changed according to the user's prevalent emotion and the neurofeedback results. Thus, emotion recognition from EEG could be used as additional input to the neurofeedback systems. We proposed and implemented real-time emotion recognition algorithm based on Valence–Arousal–Dominance emotional model. There are two types of emotion recognition algorithms: subject-dependent and subject-independent algorithms. The last ones have the better accuracy comparing to the subject-independent algorithms but need the system training session for the users. In this work, we proposed to integrate a subject‐dependent emotion recognition algorithm into neurofeedback systems for emotion monitoring. The system training session includes emotions induction with audio stimuli. During the training session, 4 types of the emotional states could be invoked: Negative/High Dominance (N/ HD), Positive/High Dominance (P/HD), Positive/Low Dominance (P/LD) and Negative/Low Dominance (N/LD) by sound clips selected from standard database. We tested the proposed emotion recognition algorithm on EEG databases labeled with emotions. The algorithm accuracy improves with number of electrodes used. As we targeted neurofeedback systems, 3–5 electrodes could be used for emotion recognition with adequate accuracy in our algorithm. In this work, we implemented subject-dependent algorithm based just on Valence dimension. Negative and positive emotional states could be recognized from EEG in real time with average 95% accuracy. The open source BrainBay neurofeedback system was used for emotion integration into neurofeedback training. The Emotiv device was integrated in the system. Negative and positive emotional states were added to the design for alpha+beta training as follows. If the training was successful but the user had mostly negative emotions during the session, the training was repeated with another visual/audio feedback. If the training was successful, and the user was mostly positive, the training was completed. If the training was unsuccessful, and the user was mostly positive, the training was repeated on the same level. If the training was unsuccessful, and the user was mostly negative, the thresholds of alpha+beta training were changed. Neurofeedback systems would benefit from monitoring of more emotions. It could allow developing of adaptive neurofeedback systems and games. The next step is to use the proposed system in neurofeedback systems to get statistics about correlation between different emotions and successful sessions. Then, improvement of the neurofeedback systems could be proposed.
doi:10.1016/j.ijpsycho.2012.06.148
– Afternoon session: 5.00–6.45 p.m. – Symposium A: Time and frequency domain identification of interacting neural structures Symposium Chair: Pedro Valdes-Sosa (Cuba) and Wael El-Deredy (United Kingdom) Recent interest in the estimation of “effective” (causal) neural interactions in the brain on the basis of dynamic neuroimaging modalities has highlighted the need for novel techniques developed specifically for neuroimaging data (1). In this symposium we present recent work in this area. In the time domain, Molenaar and Gates extend the usual Multivariate Autoregressive Model (MVAR) to account for both instantaneous and lagged interactions as determined for resting state, blocked, and event related fMRI designs. El-Deredy et al., estimate the MVAR and the covariance matrix of clustered sources of EEG/MEG in time and space by means of a hierarchical Bayesian model. In the frequency domain Valdes-Sosa et al. apply their new type of inde? pendent component analysis (STTONICA) to identify the sources of the
Personalizing neurofeedback training with EMG control in ADHD children E.A. Sapina State research Institute of Molecular Biology and Biophysics, Siberian Branch, Russian Academy of Medical Science, Novosibirsk, Russian Federation Attention deficit/hyperactivity disorder (ADHD) is defined as a behavior disorder in which the essential features are signs of developmentally inappropriate inattention, impulsivity, and hyperactivity. Theta-beta ratio is traditionally used in neurofeedback training (NFT) protocols to treat children with ADHD (Monastra et al., 2005). Nevertheless increasing cognitive performance efficiency in children with ADHD is also very important for treatment. EEG cognitive efficiency predictors are individual alpha activity indices, individual alpha peak frequency (IAPF), alpha band width and alpha amplitude desynchronization in response to eyes open (Bazanova, 2009). Besides ADHD subjects show high psycho emotional tension which is associated with increased frontal muscle EMG amplitude (Bakhshayesh et al 2011). The aim of this study was to refine NFT protocol for ADHD treatment. To achieve the aim first alpha rhythm peculiarities should be studied in ADHD in comparison with healthy children, second the effect of muscle tension control simultaneously with theta-beta ratio decreasing NFT should be studied. Thirty two children 6–9 years old diagnosed with ADHD and 13 healthy children the same age were investigated. Children with ADHD were randomly assigned to either the individual NFT group (INFT; n = 21) or the standard NFT group (SNFT; n = 11). Treatment for both groups consisted of 10 15 min sessions. Pre- and post-treatment assessment consisted of psychophysiological measures, behavioral rating scales completed by parents and teachers, as well as psychometric measures. Psychometric tests included attention test, cognitive task tests (verbal test, Kreapelin test modified for children) and anxiety test (Tamml et al. 2000). EEG and EMG were recorded in rest and during cognitive task. SNFT group had 10 sessions with the use of standard frequency domain power of the ranges: theta (4–8 Hz), alpha (8–12 Hz), and beta-1 (13–18 Hz). INFT group—with individual EEG ranges determined according the IAPF. Each theta-beta decreasing NFT session was accompanied with simultaneously forehead muscle relaxation. After 6 months both ADHD and healthy children were retested. Inter individual comparison showed significantly higher muscle tension level both in rest and during cognitive task performance in ADHD, than in healthy children in pre-treatment period. Also individual alpha peak frequency, alpha band width and desynchronization reaction were lower in ADHD than in healthy children. After 10 NFT sessions EMG and theta-beta ratio decreased significantly in INFT group and in those SNFT participants whose IAPF was ≥10 Hz. In SNFT participants with IAPF b 8 Hz NFT was ineffective. IAPF, alpha band width, amount of alpha suppression increased after effective NFT. Six month delayed test showed neurofeedback effect saving and no change in healthy children. This study results showed significant individual alpha frequency EEG pattern contribution and muscle tension control in neurofeedback training effectiveness for ADHD treatment. doi:10.1016/j.ijpsycho.2012.06.147
Neurofeedback systems and emotions monitoring O. Sourina, Y. Liu, M.K. Nguyen, Q. Wang Nanyang Technological University, Singapore Real-time emotion recognition from EEG could be used for emotions monitoring to study correlation between the user's emotions and
345
neurofeedback (NF) game performance. Then, the adaptive neurofeedback games could be designed where the NF system parameters/ thresholds could be changed according to the user's prevalent emotion and the neurofeedback results. Thus, emotion recognition from EEG could be used as additional input to the neurofeedback systems. We proposed and implemented real-time emotion recognition algorithm based on Valence–Arousal–Dominance emotional model. There are two types of emotion recognition algorithms: subject-dependent and subject-independent algorithms. The last ones have the better accuracy comparing to the subject-independent algorithms but need the system training session for the users. In this work, we proposed to integrate a subject‐dependent emotion recognition algorithm into neurofeedback systems for emotion monitoring. The system training session includes emotions induction with audio stimuli. During the training session, 4 types of the emotional states could be invoked: Negative/High Dominance (N/ HD), Positive/High Dominance (P/HD), Positive/Low Dominance (P/LD) and Negative/Low Dominance (N/LD) by sound clips selected from standard database. We tested the proposed emotion recognition algorithm on EEG databases labeled with emotions. The algorithm accuracy improves with number of electrodes used. As we targeted neurofeedback systems, 3–5 electrodes could be used for emotion recognition with adequate accuracy in our algorithm. In this work, we implemented subject-dependent algorithm based just on Valence dimension. Negative and positive emotional states could be recognized from EEG in real time with average 95% accuracy. The open source BrainBay neurofeedback system was used for emotion integration into neurofeedback training. The Emotiv device was integrated in the system. Negative and positive emotional states were added to the design for alpha+beta training as follows. If the training was successful but the user had mostly negative emotions during the session, the training was repeated with another visual/audio feedback. If the training was successful, and the user was mostly positive, the training was completed. If the training was unsuccessful, and the user was mostly positive, the training was repeated on the same level. If the training was unsuccessful, and the user was mostly negative, the thresholds of alpha+beta training were changed. Neurofeedback systems would benefit from monitoring of more emotions. It could allow developing of adaptive neurofeedback systems and games. The next step is to use the proposed system in neurofeedback systems to get statistics about correlation between different emotions and successful sessions. Then, improvement of the neurofeedback systems could be proposed.
doi:10.1016/j.ijpsycho.2012.06.148
– Afternoon session: 5.00–6.45 p.m. – Symposium A: Time and frequency domain identification of interacting neural structures Symposium Chair: Pedro Valdes-Sosa (Cuba) and Wael El-Deredy (United Kingdom) Recent interest in the estimation of “effective” (causal) neural interactions in the brain on the basis of dynamic neuroimaging modalities has highlighted the need for novel techniques developed specifically for neuroimaging data (1). In this symposium we present recent work in this area. In the time domain, Molenaar and Gates extend the usual Multivariate Autoregressive Model (MVAR) to account for both instantaneous and lagged interactions as determined for resting state, blocked, and event related fMRI designs. El-Deredy et al., estimate the MVAR and the covariance matrix of clustered sources of EEG/MEG in time and space by means of a hierarchical Bayesian model. In the frequency domain Valdes-Sosa et al. apply their new type of inde? pendent component analysis (STTONICA) to identify the sources of the