脑-机接口
计算机科学
运动表象
判别式
脑电图
线性判别分析
人工智能
模式识别(心理学)
接口(物质)
感觉运动节律
空间滤波器
分类器(UML)
过程(计算)
语音识别
支持向量机
过滤器组
机器学习
滤波器(信号处理)
计算机视觉
精神科
操作系统
最大气泡压力法
气泡
并行计算
心理学
作者
Q. Novi,Cuntai Guan,Tran Huy Dat,Ping Xue
标识
DOI:10.1109/cne.2007.369647
摘要
Brain-computer interface (BCI) is a system to translate humans thoughts into commands. For electroencephalography (EEG) based BCI, motor imagery is considered as one of the most effective ways. Different imagery activities can be classified based on the changes in mu and/or beta rhythms and their spatial distributions. However, the change in these rhythmic patterns varies from one subject to another. This causes an unavoidable time-consuming fine-tuning process in building a BCI for every subject. To address this issue, we propose a new method called sub-band common spatial pattern (SBCSP) to solve the problem. First, we decompose the EEG signals into sub-bands using a filter bank. Subsequently, we apply a discriminative analysis to extract SBCSP features. The SBCSP features are then fed into linear discriminant analyzers (LDA) to obtain scores which reflect the classification capability of each frequency band. Finally, the scores are fused to make decision. We evaluate two fusion methods: recursive band elimination (RBE) and meta-classifier (MC). We assess our approaches on a standard database from BCI Competition III. We also compare our method with two other approaches that address the same issue. The results show that our method outperforms the other two approaches and achieves similar result as compared to the best one in the literature which was obtained by a time-consuming fine-tuning process.
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