Paper Info
Title
Efficient Channel Selection Approach for Motor Imaginary Classification based on Convolutional Neural Network
Abstract
Brain Computer Interface (BCI) may be the only way to communicate and control for disabled people. Someone's intention can be decoded from their brainwaves during motor imagery action. This can be used to help them control their environment without making any physical movement. To decode someone's intention from brainwaves during motor imagery activities, machine learning models trained on features extracted from the acquired EEG signals have been used. Although the technique has been successful, it has encountered several limitations and difficulties especially during feature extraction. Moreover, many current BCI systems rely on a large number of channels (e.g. 64) to capture spatial information which are necessary during training a machine learning model. In this study, Convolutional Neural Network (CNN) is used to decode five motor imagery intentions from EEG signals obtained from four subjects using 64 channels EEG device. A CNN model trained on raw EEG data managed to achieve a mean classification accuracy of 99.7%. Channel selection based on learned weights extracted from a trained CNN model has been performed with subsequent models trained on only two selected channels with higher weights attained a high accuracy (average of 98%) among three participants out of four.
Year
DOI
Venue
2018
10.1109/CBS.2018.8612157
2018 IEEE International Conference on Cyborg and Bionic Systems (CBS)
Keywords
DocType
ISBN
Convolutional Neural Network (CNN),machine learning,Electroencephalography (EEG),brain-computer interface (BCI),feature maps
Conference
978-1-5386-7356-0
Citations 
PageRank 
References 
0
0.34
3
Authors
6
Name
Order
Citations
PageRank
Deogratias Mzurikwao111.02
Chee Siang Ang228130.47
O. W. Samuel316122.87
Mojisola Grace Asogbon4206.33
Xiangxin Li5458.34
Guanglin Li631457.23