P24-21 Automatic detection of photic evoked EEG spikes with slow burst

29th International Congress of Clinical Neurophysiology band, age-group under 30 has a smallest Z-score among three groups due to suppression by photic stimulus. The age-group from 30 to 60 showed the value intermediate between under 30 and over 60. Conclusions: We proposed the analysis method eliminating individual difference and then applied the method to photic driving response of normal subjects. Z-scores at frequencies such as the fundamental wave and higher harmonics is closely related to age. Significance: By using our statistical analysis method, the frequency characteristics of Z-score varied with advancing age drastically. Our method may be helpful to evaluate brain aging. P24-20 Assessment of stress states based on EEG activity using multiple regression analysis T. Hayashi1 , E. Okamoto1 , S. Ukai2 , K. Shinosaki2 , R. Ishii3 , Y. Mizuno-Matsumoto1 1 Graduate School of Applied Informatics, University of Hyogo, Japan, 2 Department of Neuropsychiatry, Wakayama Medical University, Wakayama, Japan, 3 Department of Neuropsychiatry, Osaka University Graduate School of Medicine, Osaka, Japan The aim of this paper is to investigate whether stress states affect electroencephalogram (EEG) activities and to assess relationship between them quantitatively. Twenty-two healthy graduate students with a mean age of 25.0±4.8 (13 men and 9 women) were evaluated for their stress levels with the Stress Self Rating Scale (SSRS) and divided into two groups: stress and non-stress groups. EEG was measured under emotional stress tasks (relaxed, pleasant, and unpleasant sessions) using audiovisual stimuli and analyzed with a discrete Fourier transform (DFT) to obtain power spectra in the theta (4 8 Hz), alpha (8 14 Hz), and beta (14 30 Hz) bands. The mean power spectra for every 20 seconds in each electrode were calculated and normalized with the value of resting state to get a relative power spectra density (rPSD) ratio. Regression analysis of linear, quadratic, and cubic models were conducted to evaluate a time-course rPSD of the EEG in the stress and non-stress groups in each session. Moreover, a regression model was yielded to predict stress degree from rPSD on all electrodes in three frequency bands and three sessions using multiple linear regression analysis. The results of regression analysis showed that the brain activity of both groups in each session significantly increased with time in theta band and decreased with time in beta band. In alpha band, the rPSD in stress group significantly decreased with time, whereas those in the non-stress group significantly increased with time at frontal, central, and temporal areas. The results of multiple linear regression analysis showed that SSRS was predicted from rPSD on F4, C3, P4, O1, F7, T3, T5, and T6 in the theta band in the unpleasant session (adjusted R2 = 0.433, p < 0.001). These events suggest that stress states can be abstracted using EEG activity. P24-21 Automatic detection of photic evoked EEG spikes with slow burst S. Nishida1 , T. Sugi2 , A. Ikeda3 , T. Nagamine4 , H. Shibasaki5 , M. Nakamura6 1 Department of Computer and Communication Engineering, Fukuoka Institute of Technology, Fukuoka, Japan, 2 Department of Advanced Systems Control Engineering, Saga University, Saga, Japan, 3 Department of Neurology, Kyoto University School of Medicine, Kyoto, Japan, 4 Department of System Neuroscience, Sapporo Medical University, Sapporo, Japan, 5 Takeda General Hospital, Kyoto, Japan, 6 Research Institute of Systems Control, Saga, Japan

S249 spike detection method were determined by considering the results of the slow burst detection to accurately detect the small spike with the slow burst. Results: The proposed method was applied for the photic evoked EEG data containing spikes and slow burst, and brought satisfactory coincidence with the results interpreted by a qualified electroencephalographer. The majority of spikes were detected correctly, and the small spikes with slow burst were also detected by considering the results of the slow burst detection. Furthermore, the characteristics of the detected spikes (synchronization for stimulation, scalp topography) were represented quantitatively. Conclusions: By introducing the proposed method in already developed system for the automatic interpretation of awake background EEG, the system becomes more powerful for assisting the electroencephalographer for their EEG interpretation. P24-22 Development of Polydimethylsiloxane (PDMS) and silver ball based dry type flexible EEG electrode for EEG recording D. Ko1,4 , G.-T. Lee2,4 , E.-J. Lee3 , J.-H. Lee4 , S.-H. Oh4 , S.-H. Lee3 , B.-J. Kim4 , K.-Y. Jung1,2,4 1 BK21 Program for Biomedical Science, Korea University College of Medicine, Seoul, Korea, 2 Department of Biomedical Engineering, Korea University College of Medicine, Seoul, Korea, 3 Department of Biomedical Engineering, Korea University College of Health Science, Seoul, Korea, 4 Department of Neurology, Korea University College of medicine, Seoul, Korea Objectives: Ag/AgCl electrode, which is the most commonly used to measure scalp EEG potential, is usually required conductive gel or paste that convey ion between scalp and electrode. For using this electrode, skin abrasion is required to decrease scalp-electrode impedance. It may become painful, EEG recording based upon discomfort. Furthermore, individual site preparation precludes rapid application of an EEG electrode in emergency settings. It has risk of infection, too. The aims of this study are develop dry-type of EEG electrode which does not requires conductive gel or paste to record scalp EEG potential and tests of performance. Methods: Newly developed electrode consists of 4 silver balls that were embedded in polydimethylsiloxane (PDMS). The silver ball connected by 50um copper wire. It has function which transmit EEG wave to amplifier. The first experiment compares dry type electrode using PDMS-silver ball and Ag/AgCl electrode which use conductive paste. To test, descriptive analysis and correlation coefficient, spectral characteristics are used. Results: There was no significant difference of descriptive analysis (P = 0.946). Result of correlation coefficient, dry type electrode which we developed has lower correlation coefficient (0.764) than sweat type Ag/AgCl electrode (0.957). Coherence of delta and theta frequency band is lower (0.41 and 0.66, respectively) than Ag/AgCl electrode (0.86 and 0.94 respectively), and alpha to gamma frequency band is a little lower (0.83 0.88) than Ag/AgCl electrode (0.90 0.66). Conclusions: Our results support PDMS silver-ball based EEG electrode can use for EEG recording from alpha to gamma (8 Hz-50 Hz) frequency band. The electrode can identify the trend of EEG signal, and applied to BCI (brain-computer-interface) using EEG signal. P24-23 Algorithmic complexity measure of EEG for staging brain state B.S. Machado1 , T.B. Miranda2 , E. Morya3 , E. Amaro Jr.1 , K. Sameshima4 Brain Institute, Albert Einstein IIEP, S˜ ao Paulo, Brazil, 2 Experimental ao Paulo, Brazil, Pathophysiology, FMUSP, S˜ ao Paulo, Brazil, 3 AASDAP, S˜ 4 Department of Radiology, FMUSP, S˜ ao Paulo, Brazil

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Objective: An automatic interpretation system for an awake background EEG has been developed by the some of the authors. A function of automatic detection for spike and slow burst evoked by photic stimulation was required, but was not included in this system. In this study, a method for detecting the spike and the slow burst in the photic evoked EEG was proposed for a part of the automatic interpretation of awake background EEG. Method: The proposed method consisted of the spike detection method and the slow burst detection method. The spikes were detected by considering properties of spike and slow portions separately, and simultaneous appearance in adjacent region of the scalp, by combining methods of the morphological filter and the similarity coefficient in the time domain. On the other hand, the slow burst was detected based on peak frequency and power of main component, which were calculated from pole of AR model in the frequency domain. The conditions of the

Objective: We propose a measure from algorithmic complexity theory, Lempel-Ziv complexity (LZC), to classify brain states through the brain electrical activity analysis. The LZC performance is compared with sample entropy. Methods: The sample entropy measure is based on observed recurrence patterns of phase trajectories of the attractor set reconstructed from original time series that requires conditions hardly observed in real data, e.g. low noise-to-signal ratio, consistent estimators for both time delay and embedding dimension, and deterministic mechanisms generating the data. As an alternative, the theory of algorithmic complexity can be used to provide the meaning of randomness for symbolic sequences derived from time series. According to Kolmogorov the algorithmic complexity