Individualization of data-segment-related parameters for improvement of EEG signal classification in brain-computer interface
Document Type
Article
Date of Original Version
6-1-2010
Abstract
In electroencephalogram (EEG) modeling techniques, data segment selection is the first and still an important 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 datasegment-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. © Tianjin University and Springer-Verlag Berlin Heidelberg 2010.
Publication Title, e.g., Journal
Transactions of Tianjin University
Volume
16
Issue
3
Citation/Publisher Attribution
Cao, Hongbao, Walter G. Besio, Steven Jones, and Peng Zhou. "Individualization of data-segment-related parameters for improvement of EEG signal classification in brain-computer interface." Transactions of Tianjin University 16, 3 (2010). doi: 10.1007/s12209-010-0041-2.