Time-Series Prediction of the Oscillatory Phase of EEG Signals Using the Least Mean Square Algorithm-Based AR Model
Aqsa Shakeel, Toshihisa Tanaka, Keiichi Kitajo
Abstract
Neural oscillations are vital for the functioning of a central nervous system because they assist in brain communication across a huge network of neurons. Alpha frequency oscillations are believed to depict idling or inhibition of task-irrelevant cortical activities. However, recent studies on alpha oscillations (particularly alpha phase) hypothesize that they have an active and direct role in the mechanisms of attention and working memory. To understand the role of alpha oscillations in several cognitive processes, accurate estimations of phase, amplitude, and frequency are required. Herein, we propose an approach for time-series forward prediction by comparing an autoregressive (AR) model and an adaptive method (least mean square (LMS)-based AR model). This study tested both methods for two prediction lengths of data. Our results indicate that for shorter data segments (prediction of 128 ms), the AR model outperforms the LMS-based AR model, while for longer prediction lengths (256 ms), the LMS- based AR model surpasses the AR model. LMS with low computational cost can aid in electroencephalography (EEG) phase prediction (alpha oscillations) in basic research to reveal the functional role of the oscillatory phase as well as for applications for brain-computer interfaces.