Paper Info
Title
EEG Channel Selection Based on Correlation Coefficient for Motor Imagery Classification: A Study on Healthy Subjects and ALS Patient.
Abstract
Brain-Computer Interface (BCI) provides an alternate channel of interaction for people with severe motor disabilities. The Common Spatial Pattern (CSP) algorithm is effective in extracting discriminative features from EEG data for motor imagery-based Brain-Computer Interface (BCI). CSP yields signal from various locations for better performance. In this study, we selected a subset of EEG channels using correlation coefficient of spectral entropy and compared the classification performance using the Filter Bank Common Spatial Pattern (FBCSP) algorithm. We conducted experiments on 4 healthy subjects and one Amyotrophic Lateral Sclerosis (ALS) patient. The results showed that the proposed channel selection method increased classification accuracy of all subjects from 1.25% to 8.22%. Optimal performance was obtained using between 13 to 24 channels, and channels located over the motor cortex zone possess higher probabilities of being selected. Comparing with the channels manually selected to over the motor cortex area, the correlation coefficient method is able to identify the optimal channel combination and improve the motor imagery decoding accuracy of Healthy and ALS subjects.
Year
DOI
Venue
2018
10.1109/EMBC.2018.8512701
EMBC
Field
DocType
Volume
Computer vision,Correlation coefficient,Pattern recognition,Computer science,Brain–computer interface,Communication channel,Motor cortex,Artificial intelligence,Decoding methods,Discriminative model,Electroencephalography,Motor imagery
Conference
2018
Citations 
PageRank 
References 
0
0.34
0
Authors
7
Name
Order
Citations
PageRank
Tao Yang116076.32
Kai Keng Ang280464.19
Kok Soon Phua3144.94
Juanhong Yu453.21
Valerie Toh500.68
Wai Hoe Ng600.68
Rosa Q. So7287.42