Selective Feature Generation Method Based on Time Domain Parameters and Correlation Coefficients for Filter-Bank-CSP BCI Systems.

Yongkoo Park,Wonzoo Chung

doi:10.3390/s19173769

Abstract

This paper presents a novel motor imagery (MI) classification algorithm using filter-bank common spatial pattern (FBCSP) features based on MI-relevant channel selection. In contrast to existing channel selection methods based on global CSP features, the proposed algorithm utilizes the Fisher ratio of time domain parameters (TDPs) and correlation coefficients: the channel with the highest Fisher ratio of TDPs, named principle channel, is selected and a supporting channel set for the principle channel that consists of highly correlated channels to the principle channel is generated. The proposed algorithm using the FBCSP features generated from the supporting channel set for the principle channel significantly improved the classification performance. The performance of the proposed method was evaluated using BCI Competition III Dataset IVa (18 channels) and BCI Competition IV Dataset I (59 channels).

Highlights

Brain–computer interfaces (BCIs) enable the translation of neural signals related to a user’s intention into control signals in the absence of muscle movements, and have drawn considerable attention in various research fields, including rehabilitation and engineering [1,2,3]
The classification performance of the proposed method was compared with time domain parameters (TDPs) [24], filter-bank common spatial pattern (FBCSP) [17], filter-bank regularized CSP (FBRCSP) [19], sparse CSP (SCSP) [20] and its filter-bank version denoted as FBSCSP, and common spatial pattern (CSP)-R-MF [21]
Optimization of threshold for each individual subject performed better, this paper presents the experiment results obtained under both settings

Summary

Introduction

Brain–computer interfaces (BCIs) enable the translation of neural signals related to a user’s intention into control signals in the absence of muscle movements, and have drawn considerable attention in various research fields, including rehabilitation and engineering [1,2,3]. The EEG-based BCI studies show that, when imagining movement of the body, the EEG signals from the regions associated of the cerebral cortex show decreased and increased power in sensorimotor and beta rhythm, called event-related desynchronization (ERD) and event-related synchronization (ERS), respectively [7,8]. Motor imagery (MI)-based BCIs are widely studied by identifying ERD/ERS patterns. EEG signals suffer from low signal-to-noise ratios (SNR) and are highly correlated due to the volume conduction effect [9]. They are susceptible to strong artifacts [10,11]

Methods

Results

Discussion

Conclusion