Subject-Specific Channel Selection Using Time Information for Motor Imagery Brain–Computer Interfaces

Yuan Yang,Joe Wiart,Sylvain Chevallier,Isabelle Bloch

doi:10.1007/s12559-015-9379-z

Abstract

Keeping a minimal number of channels is essential for designing a portable brain–computer interface system for daily usage. Most existing methods choose key channels based on spatial information without optimization of time segment for classification. This paper proposes a novel subject-specific channel selection method based on a criterion called F score to realize the parameterization of both time segment and channel positions. The F score is a novel simplified measure derived from Fisher’s discriminant analysis for evaluating the discriminative power of a group of features. The experimental results on a standard dataset (BCI competition III dataset IVa) show that our method can efficiently reduce the number of channels (from 118 channels to 9 in average) without a decrease in mean classification accuracy. Compared to two state-of-the-art methods in channel selection, our method leads to comparable or even better classification results with less selected channels.

Highlights

Brain–computer interfaces (BCIs) are systems that support a direct communication between brain and computer without any use of peripheral nerves and muscle movements [1, 31]
The testing results obtained when using the selected electrodes in different time segments of 2 s are provided in Table 1, and the results from the selected time segments are in Italic
The results are evaluated by classification accuracy (ACC), which is defined as the observed overall agreement between classification outputs and true classes

Summary

Introduction

Brain–computer interfaces (BCIs) are systems that support a direct communication between brain and computer without any use of peripheral nerves and muscle movements [1, 31]. The BCIs based on electroencephalography (EEG) are noninvasive BCIs, which record EEG signal with electrodes placed on the surface of the scalp [1, 8, 23]. EEG studies show that imaginary movements of different body parts can cause a power decrease in sensorimotor rhythms of EEG, i.e., l (8–13 Hz) and b rhythms (14–35 Hz), called event-related desynchronization (ERD), at corresponding ‘‘active’’ cortex areas [25]; a power increase in sensorimotor rhythms called event-related synchronization (ERS) might be observed at other ‘‘idling’’ areas during the motor imagery [24]. Motor imagery of different body parts can be identified by classifying ERD/ERS patterns, which gives birth to a type of EEG-based BCI called motor imagery BCI [31]

Methods

Results

Conclusion