Exploration of Temporal and Spectral Features of EEG Signals in Motor Imagery Tasks

Abstract

Brain Computer Interfaces (BCIs) have several applications. One of the most crucial domains that BCIs cater to is prosthetics, orthotics and rehabilitation devices. Such devices aim to better the lives of specially-abled people. Motor Imagery or Movement Intention (MI) neural signals are collected to obtain a better insight of the underlying neural activities. EEG is the most popular modality for obtaining neural signals due to its advantages like non-invasiveness, portability, and the ease of use. The utilization of MI EEG signals to design BCIs has been heavily exploited. Spectral estimation methods have been exploited to delineate different MI tasks. This article focusses on exploring channel-wise temporal and spectral features of MI EEG signals. A rich MI EEG dataset involving five different motor imageries has been utilized in this study. The analysis based upon channel-wise temporal and spectral decomposition involves two major biomarkers, Event Related Spectral Perturbation (ERSP) and Inter Trial Coherence (ITC). ERSP can be considered as a generalization of power increase and decrease within a frequency range of interest. ITC reflects consistency of rhythms across the trials. This article aims to exploit ERSP and ITC methods to explore relevant time segment, frequency range and informative channels associated to MI signatures.