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A brain controlled wheelchair based on common spatial pattern

机译：基于常见空间格局的脑控轮椅

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This paper was proposed for the feature extraction problem in Brain Computer Interface (BCI) which was based on the motor imagery. Common Spatial Pattern (CSP) was used to extract useful features from the Electroencephalograph (EEG) signals. Firstly, a preprocessing step was applied to remove noises. Secondly, CSP was used to analyze with EEG signals. Support Vector Machine (SVM) was investigated to classify motor imagery state. The EEG signals of motor imagery provided by dataset I of 2004 BCI Competition III were used for the validation. The results showed that the algorithm can extract the obvious characteristics efficiently. Finally, the proposed method was used in a wheelchair application. Experimental results showed that the proposed approach was promising for implementing human-computer interaction, especially for EEG-based brain controlled wheelchair.
机译：本文针对基于运动图像的脑计算机接口（BCI）中的特征提取问题提出了建议。通用空间模式（CSP）用于从脑电图（EEG）信号中提取有用的功能。首先，进行预处理步骤以去除噪声。其次，使用CSP分析脑电信号。研究了支持向量机（SVM）对运动图像状态进行分类。由2004 BCI竞赛III的数据集I提供的运动图像的EEG信号用于验证。结果表明，该算法可以有效地提取明显特征。最后，所提出的方法被用于轮椅应用中。实验结果表明，该方法有望实现人机交互，特别是基于脑电图的脑控轮椅。

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