Common Spatial Pattern Method for Channel Selelction in Motor Imagery Based Brain-computer Interface

TLDR

Motor‑imagery BCIs translate imagined hand and foot movements into control signals by classifying EEG spatial patterns, but require many channels and cumbersome preparation, limiting them to laboratory demonstrations. This study proposes a channel‑reduction method for motor‑imagery BCIs. Using common spatial pattern analysis, the authors identified the most informative channels by locating maxima in scalp‑mapped spatial pattern vectors and built a classifier combining linear discriminant analysis with event‑related desynchronization and readiness potential signals. The reduced‑channel system achieved 93.45 % and 91.88 % classification accuracy on two subjects using only four channels.

Abstract

A brain-computer interface(BCI) based on motor imagery (MI) translates the subject's motor intention into a control signal through classifying the electroencephalogram (EEG) patterns of different imagination tasks, e.g. hand and foot movements. Characteristic EEG spatial patterns make MI tasks substantially discriminable. Multi-channel EEGs are usually necessary for spatial pattern identification and therefore MI-based BCI is still in the stage of laboratory demonstration, to some extent, due to the need for constanly troublesome recording preparation. This paper presents a method for channel reduction in MI-based BCI. Common spatial pattern (CSP) method was employed to analyze spatial patterns of imagined hand and foot movements. Significant channels were selelcted by searching the maximunms of spatial pattern vectors in scalp mappings. A classification algorithm was developed by means of combining linear discriminat analysis towards even-related desynchronization (ERD) and readiness potential (RP). The classification accuracies with four optimal channels were 93.45% and 91.88% for two subjects.