Meditation EEG Overview Based on Subband Features Quantified by AR Model

Abstract

This paper reports a computerized scheme Subband-AR EEG Viewer that provides a comprehensive view of the meditation EEG record. The scheme is mainly designed to trace the varying spectral characteristics in meditation EEG. To accomplish this task, a meditation EEG signal is first decomposed into subband components by tree-structured filter banks. The second-order autoregressive model is then applied to each subband component to estimate its root frequency. Based on the estimated root frequencies and sound logic, specific criterion can be deduced for a particular problem-domain application. To demonstrate the performance of the proposed scheme, two algorithms are introduced for slow α-rhythm detection and meditation EEG interpretation. These algorithms do not require exhausting work at determining appropriate parameters in implementation. Further, due to the second-order autoregressive model adopted, the computation load is greatly reduced. This approach is practically favorable to long-term EEG monitoring and real-time processing, Finally, the meditation scenario can be illustrated by a running gray-scale chart with each gray tone coding a particular EEG rhythmic pattern. Observed meditation scenarios differ significantly from those of the control subjects.