天津大学学报（英文版）
天津大學學報（英文版）
천진대학학보（영문판）
TRANSACTIONS OF TIANJIN UNIVERSITY
2010年
3期
235-238
,共4页
data segment%parameter selection%EEG classification%brain-computer interface (BCI)
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.
