基于独立成分分析的优选N200和P300特征通道算法

针对脑电信号存在个体差异性并易受噪声、伪迹干扰的特点,提出一种基于独立成分分析ICA的优选特征通道算法。采用ICA将通道的数据分解为N200、P300、眼电伪迹以及其他生理信号,根据这些信号对每个通道的影响程度,判定各通道是否适合进行特征提取。分别采用本方法和三种常用方法对12个被试的脑电数据进行特征通道选择,并进行N200和P300电位的辨识,经比对发现,本文方法取得了93.10%的平均分类准确率,比其他三种方法下的准确率分别高出7.27%、1.07%和75.96%。为预测任意被试的最优通道,采用最小二乘法对ICA权值和通道选择阈值之间的关系进行拟合,对三个新被试进行最优通道预测和电位的辨识,得到较高的分类准确率,说明此预测方法具有一定普适性。

Independent component analysis-based channel selection for high-accuracy classification of N200 and P300

Since EEG signals have individual difference and are vulnerable to noise and artifacts, we propose an independent component analysis (ICA)-based method for the selection of optimal feature channels. This method applies the ICA to decompose channels’ data to N200, P300, ocular artifacts and other physiological signals. Whether a channel is suitable for feature extraction is decided by the influence of those signals that mentioned above to this channel. We apply our method and three other commonly used methods for feature channel selection to twelve subjects’ brain signals, and recognize N200 and P300 potentials. We find that our method achieves a 93.10% accuracy on average and it is 7.27%, 1.07% and 75.96% higher than the average accuracy of the other three methods respectively. We fit a relation curve between ICA weight and channel selection threshold based on the least square method, and obtain a high classification accuracy when predicting the optimal channels and recognizing the potentials from another three new subjects' data, which means that this prediction method has universality.