Abstract
The study of meditation offers a perfect setting for the study of a large variety of states of consciousness. Here, we present a classification paradigm that can be used for staging of individual meditation sessions into a variety of predefined mental states. We have measured 64 channels of the electroencephalogram (EEG) plus peripheral physiological measures in 49 participants with varying ...
Abstract
The study of meditation offers a perfect setting for the study of a large variety of states of consciousness. Here, we present a classification paradigm that can be used for staging of individual meditation sessions into a variety of predefined mental states. We have measured 64 channels of the electroencephalogram (EEG) plus peripheral physiological measures in 49 participants with varying experiences in meditation practice. The data recorded in a meditation session of seven meditative tasks were analyzed with respect to EEG power spectral density measures plus peripheral measures. A multiclass linear discriminant analysis classifier was trained for classification of data epochs of the seven standard tasks. The classification results were verified using random partitions of the data. As an overall result, about 83% (+/- 7%) of the epochs could be correctly classified to their originating task. The best classification method was then applied to individual meditation sessions, which allowed for staging of meditation states similarly to the staging possibility of sleep states. This study exemplarily demonstrates the possibility of developing an automatized staging tool that can be used for monitoring changes in the states of consciousness offline or online for training or therapeutic purpose.
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