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Individualization of Data-Segment-Related Parameters for Improvement of EEG Signal Classification in Brain-Computer Interface 认领 引用 被引量：1
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作者 曹红宝 BESIO Walter G +1 位作者 JONES Steven 周鹏 《Transactions of Tianjin University》 EI CAS 2010年第3期235-238,共4页
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... 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. 展开更多
关键词 data segment parameter selection EEG classification brain-computer interface （BCI）
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