Paper Info
Title
A New Motor Imagery EEG Classification Method FB-TRCSP+RF Based on CSP and Random Forest.
Abstract
There is a general agreement in the brain computer interface community that the feature extracting method called the common spatial pattern (CSP) combined with nonlinear classifiers can provide excellent results in some cases. However, CSP is also known to be very sensitive to noise and prone to over fitting, and the performance of this spatial filter is closely related to the operational frequency band of electroencephalogram data. To address this issue, we propose a new method FB-TRCSP+RF based on CSP and random forest. The FB-TRCSP is combined by the 8th-order Butterworth bandpass-filters and the CSP with Tikhonov regularization, which is a more robust feature extraction method compared to the CSP. Then, the model is applied to an experimental data set collected from 14 subjects and is compared with the non-regularization method FB-CSP+RF. The results show that the method we proposed yields relatively higher median classification accuracies and shows a stronger ability in subject-to-subject learning compared to prevailing approaches.
Year
DOI
Venue
2018
10.1109/ACCESS.2018.2860633
IEEE ACCESS
Keywords
Field
DocType
Classification,CSP,feature,FB-TRCSP plus RF
Tikhonov regularization,Pattern recognition,Frequency band,Computer science,Brain–computer interface,Feature extraction,Artificial intelligence,Overfitting,Statistical classification,Random forest,Distributed computing,Spatial filter
Journal
Volume
ISSN
Citations 
6
2169-3536
0
PageRank 
References 
Authors
0.34
0
9
Name
Order
Citations
PageRank
Ranran Zhang100.34
Xiaoyan Xiao211.69
Zhi Liu32314.28
Wei Jiang414050.14
Jianwen Li54815.44
Yankun Cao634.74
Jianmin Ren701.01
Jiang Dongmei811515.28
Li-zhen Cui928271.41