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Individualization of data-segment-related parameters for improvement of EEG signal classification in brain-computer interface |
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| Authors: | CAO Hongbao BESIO Walter G JONES Steven ZHOU Peng |
| Institution: | 1. Department of Biomedical Engineering, Latech University, LA 71270, USA 2. Department of Biomedical Engineering, University of Rhode Island, RI 02881, USA 3. School of Precision Instrument and Opto-Electronies Engineering, Tianjin University, Tianjin 300072, China |
| Abstract: | In electroencephalogram (EEG) modeling techniques, data segment selection is the first and still an im-portant step. The influence of a set of data-segment-related parameters on feature extraction and classification in an EEG-based brain-computer interface (BCI) was studied. An auto search algorithm was developed to study four data-segment-related parameters in each trial of 12 subjects' EEG. The length of data segment (LDS), the start position of data (SPD) segment, AR order, and number of trials (NT) were used to build the model. The study showed that, compared with the classification ratio (CR) without parameter selection, the CR was increased by 20% to 30% with proper selection of these data-segment-related parameters, and the optimum parameter values were subject-dependent. This suggests that the data-segment-related parameters should be individualized when building models for BCI. |
| Keywords: | data segment parameter selection EEG classification brain-computer interface (BCI) |
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