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为了满足瘫痪人士和虚拟现实的需求,提出基于小波分析和SVM的P300脑电信号处理算法研究,并通过实验数据论证算法的可行性。本算法首先使用工频陷波器和小波分析去噪,然后使用小波分解和teager能量算子分别提取时域特征量和能量特征量,并基于SVM判断特征量是否含有P300脑电信号。实验数据表明,本算法比单一特征量判别算法有较好的判别精度,符合需求标准。
In order to meet the needs of paralyzed people and virtual reality, P300 EEG signal processing algorithm based on wavelet analysis and SVM is proposed, and the feasibility of the algorithm is verified through experimental data. The algorithm first uses frequency notch filter and wavelet analysis to denoise, then uses wavelet decomposition and teager energy operator to extract time-domain feature and energy feature respectively, and based on SVM to determine whether the feature contains P300 EEG. The experimental data show that the proposed algorithm has better discrimination accuracy than the single feature quantity discriminating algorithm and meets the requirement standard.