An Optimized Channel Selection Method Based on Multifrequency CSP-Rank for Motor Imagery-Based BCI System

Jian Kui Feng,Yugang Niu,Xingyu Wang,Andrzej Cichocki,Ian Daly,Jing Jin,Jiale Zhou

doi:10.1155/2019/8068357

Abstract

Background Due to the redundant information contained in multichannel electroencephalogram (EEG) signals, the classification accuracy of brain-computer interface (BCI) systems may deteriorate to a large extent. Channel selection methods can help to remove task-independent electroencephalogram (EEG) signals and hence improve the performance of BCI systems. However, in different frequency bands, brain areas associated with motor imagery are not exactly the same, which will result in the inability of traditional channel selection methods to extract effective EEG features. New Method To address the above problem, this paper proposes a novel method based on common spatial pattern- (CSP-) rank channel selection for multifrequency band EEG (CSP-R-MF). It combines the multiband signal decomposition filtering and the CSP-rank channel selection methods to select significant channels, and then linear discriminant analysis (LDA) was used to calculate the classification accuracy. Results The results showed that our proposed CSP-R-MF method could significantly improve the average classification accuracy compared with the CSP-rank channel selection method.

Highlights

Brain-computer interface (BCI) technology enables the human brain to communicate directly with the outside world through electroencephalogram (EEG) signals and has attracted considerable attention in recent years [1]
Filter bank common spatial pattern (FBCSP) comprises four progressive stages of EEG measurements processing: frequency filtering, spatial filtering (CSP was selected in this paper), feature selection (LASSO was selected in this paper), and classification (LDA was selected in this paper)
It is well established that motor imagery produces an eventrelated de\synchronization (ERD\S) over the sensorimotor areas within the mu rhythm (8–13 Hz) and the beta band (13–30 Hz) [7]. erefore, the bandpass filter used for measuring the ERD/S was between 8 to 30 Hz [8, 12]. is frequency band may be further divided into 7 subbands (4–8 Hz, 8–12 Hz, 12–16 Hz, 16–20 Hz, 20–24 Hz, 24–28 Hz, 28–32 Hz, 32–36 Hz, and 36–40 Hz) to study the effect of frequency band selection on motor imagery-based brain-computer interface (BCI) control, as proposed in work on subband common spatial pattern (SBCSP) [30] and FBCSP

Summary

Background

Due to the redundant information contained in multichannel electroencephalogram (EEG) signals, the classification accuracy of brain-computer interface (BCI) systems may deteriorate to a large extent. To address the above problem, this paper proposes a novel method based on common spatial pattern- (CSP-) rank channel selection for multifrequency band EEG (CSP-RMF). It combines the multiband signal decomposition filtering and the CSP-rank channel selection methods to select significant channels, and linear discriminant analysis (LDA) was used to calculate the classification accuracy. E results showed that our proposed CSP-R-MF method could significantly improve the average classification accuracy compared with the CSP-rank channel selection method Results. e results showed that our proposed CSP-R-MF method could significantly improve the average classification accuracy compared with the CSP-rank channel selection method

Introduction

Methods

8–12 Hz 12–16 Hz 16–20 Hz 20–24 Hz 24–28 Hz 28–30 Hz 8–30 Hz

Findings

Discussion