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提出将独立分量分析(ICA)和小波分析结合的方法提取诱发电位(ERPs)信号。首先采用扩展信息最大化ICA算法处理脑电信号,得到独立成分;然后将小波阀值收缩算法作为中间步骤处理独立成分;对新的独立成分进行ICA的逆变换以重建脑电数据;少次叠加消噪后的脑电数据,提取出ERPs信号。真实数据的处理结果表明:ICA算法和结合方法都可以有效去除混合在ERPs信号中的眼电噪声和肌电噪声,但结合算法可以更好地保留噪声独立成分中的脑神经活动,实现微弱ERPs信号的有效提取。
The method of combining independent component analysis (ICA) with wavelet analysis is proposed to extract evoked potentials (ERPs) signals. Firstly, the ICA algorithm was used to process the EEG signals to obtain the independent components. Secondly, the wavelet threshold shrinkage algorithm was used as an intermediate step to process the independent components. ICA was transformed to reconstruct the EEG data of the new independent components. Noise after EEG data, extracted ERPs signal. The real data processing results show that both ICA algorithm and combined method can effectively remove the ophthalmic noise and electromyographic noise mixed in the ERPs signal, but the combination algorithm can better retain the brain neural activity in the noise independent component and realize the weak ERPs Effective signal extraction.