Identification of MEG-related brain dynamics induced by a yogic breathing technique

The automatic detection of various differences in brain dynamics has been studied here using two approaches, nonlinear time series analysis, and the clustering method. Six data sets of whole-head 148 channel MEG activity were collected from a single subject performing a yogic breathing protocol in three two-day experiments repeated with a time lag of one month. The MEG signals have been analyzed by evaluating five nonlinear indicators, two statistical measures and the power for each minute. The trends of eight parameters in time and space have been used in an effort to explore the classification schemes using Growing Hierarchical Self Organizing Maps. The results show the utility of this approach for distinguishing the different phases of the yogic breathing protocol and for observing brain activity changes at successive months.