A New Motor Imagery EEG Classification Method FB-TRCSP+RF Based on CSP and Random Forest | Zendy

Ranran Zhang | Zendy; Xiaoyan Xiao | Zendy; Zhi Liu | Zendy; Wei Jiang | Zendy; Jianwen Li | Zendy; Yankun Cao | Zendy; Jianmin Ren | Zendy; Dongmei Jiang | Zendy; Lizhen Cui | Zendy

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A New Motor Imagery EEG Classification Method FB-TRCSP+RF Based on CSP and Random Forest

Author(s) -

Ranran Zhang,

Xiaoyan Xiao,

Zhi Liu,

Wei Jiang,

Jianwen Li,

Yankun Cao,

Jianmin Ren,

Dongmei Jiang,

Lizhen Cui

Publication year - 2018

Publication title -

ieee access

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.587

H-Index - 127

ISSN - 2169-3536

DOI - 10.1109/access.2018.2860633

Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation

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.

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