Paper Info
Title
Channel Selection For Optimal Eeg Measurement In Motor Imagery-Based Brain-Computer Interfaces
Abstract
A method for selecting electroencephalographic (EEG) signals in motor imagery-based brain-computer interfaces (MI-BCI) is proposed for enhancing the online interoperability and portability of BCI systems, as well as user comfort. The attempt is also to reduce variability and noise of MI-BCI, which could be affected by a large number of EEG channels. The relation between selected channels and MI-BCI performance is therefore analyzed. The proposed method is able to select acquisition channels common to all subjects, while achieving a performance compatible with the use of all the channels. Results are reported with reference to a standard benchmark dataset, the BCI competition IV dataset 2a. They prove that a performance compatible with the best state-of-the-art approaches can be achieved, while adopting a significantly smaller number of channels, both in two and in four tasks classification. In particular, classification accuracy is about 77-83% in binary classification with down to 6 EEG channels, and above 60% for the four-classes case when 10 channels are employed. This gives a contribution in optimizing the EEG measurement while developing non-invasive and wearable MI-based brain-computer interfaces.
Year
DOI
Venue
2021
10.1142/S0129065721500039
INTERNATIONAL JOURNAL OF NEURAL SYSTEMS
Keywords
DocType
Volume
EEG channels selection, EEG channel reduction, motor imagery, brain-computer interface
Journal
31
Issue
ISSN
Citations 
3
0129-0657
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
Pasquale Arpaia121.09
Francesco Donnarumma2425.89
Antonio Esposito37818.72
Marco Parvis49628.85