Discriminative Frequencies and Temporal EEG Segmentation in the Motor Imagery Classification Approach

A linear discriminant analysis transformation-based approach to the classification of three different motor imagery types for brain-computer interfaces was considered. The study involved 16 conditionally healthy subjects (12 men, 4 women, mean age of 21.5 years). First, the search for subject-specific discriminative frequencies was conducted in the task of movement-related activity. This procedure was shown to increase the classification accuracy compared to the conditional common spatial pattern (CSP) algorithm, followed by a linear classifier considered as a baseline approach. In addition, an original approach to finding discriminative temporal segments for each motor imagery was tested. This led to a further increase in accuracy under the conditions of using Hjorth parameters and interchannel correlation coefficients as features calculated for the EEG segments. In particular, classification by the latter feature led to the best accuracy of 71.6%, averaged over all subjects (intrasubject classification), and, surprisingly, it also allowed us to obtain a comparable value of intersubject classification accuracy of 68%. Furthermore, scatter plots demonstrated that two out of three pairs of motor imagery were discriminated by the approach presented.

Automated summary

This study presents a linear discriminant analysis transformation-based approach for classifying three different types of motor imagery in brain-computer interfaces. The results demonstrate that this method improves classification accuracy and successfully discriminates two out of three pairs of motor imagery.