Optimized Bi-Objective EEG Channel Selection and Cross-Subject Generalization With Brain–Computer Interfaces

• Published in: IEEE transactions on human-machine systems Vol. 46; no. 6; pp. 777 - 786
• Main Authors: Handiru, Vikram Shenoy, Prasad, Vinod A.
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.12.2016; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Brain-computer interfaces; Channel selection; Computational complexity; dimension reduction; Electroencephalography; electroencephalography (EEG); Human-computer interface; Iterative methods; motor imagery (MI); multi-objective optimization; Optimization; Pareto optimization; Signal processing; subject variability
• ISSN: 2168-2291; 2168-2305
• DOI: 10.1109/THMS.2016.2573827

Electroencephalography (EEG) signal processing to decode motor imagery (MI) involves high-dimensional features, which increases the computational complexity. To reduce this computational burden due to the large number of channels, an iterative multiobjective optimization for channel selection (IMOCS) is proposed in this paper. For a given MI classification task, the proposed method initializes a reference candidate solution and subsequently finds a set of the most relevant channels in an iterative manner by exploiting both the anatomical and functional relevance of EEG channels. The proposed approach is evaluated on the Wadsworth dataset for the right fist versus left fist MI tasks, while considering the cross-validation accuracy as the performance evaluation criteria. Furthermore, 12 other dimension reduction and channel selection algorithms are used for benchmarking. The proposed approach (IMOCS) achieved an average classification accuracy of about 80% when evaluated using 35 best-performing subjects. One-way analysis of variance revealed the statistical significance of the proposed approach with at least 7% improvement over other benchmarking algorithms. Furthermore, a cross-subject generalization of channel selection on untrained subjects shows that the subject-independent channels perform as good as using all channels achieving an average classification accuracy of 61%. These results are promising for the online brain-computer interface (BCI) paradigm that requires low computational complexity and also for reducing the preparation time while conducting multiple session BCI experiments for a larger pool of subjects.