Multichannel MI-EEG Feature Decoding Based on Deep Learning
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摘要:
脑电(EEG)是一种在临床上广泛应用的脑信息记录形式，其反映了脑活动中神经细胞放电产生的电场变化情况。脑电广泛应用于脑-机接口(BCI)系统。然而，研究表明脑电信息空间分辨率较低，这种缺陷可以综合分析多通道电极的脑电数据来弥补。为了从多通道数据中高效地获取到与运动想象任务相关的辨识特征，该文提出一种针对多通道脑电信息的卷积神经网络(MC-CNN)解码方法，先对预先选取好的多通道数据预处理后送入2维卷积神经网络(CNN)进行时间-空间特征提取，然后利用自动编码(AE)器把这些特征映射为具有辨识度的特征子空间，最后指导识别网络进行分类识别。实验结果表明，该文所提多通道空间特征提取和构建方法在运动想象脑电任务识别性能和效率上都具有较大优势。
Abstract:Regarding as the measure of the electrical fields produced by the active brain, ElectroEncephaloGraphy (EEG) is a brain mapping and neuroimaging technique widely used inside and outside of the clinical domain, which is also widely used in Brain–Computer Interfaces (BCI). However, low spatial resolution is regarded as the deficiency of EEG signified from researches, which can fortunately be made up by synthetic analysis of data from different channels. In order to efficiently obtain subspace features with discriminant characteristics from EEG channel information, a Multi-Channel Convolutional Neural Networks (MC-CNN) model is proposed for MI-EEG decoding. Firstly input data is pre-processed form selected multi-channel signals, then the time-spatial features are extracted using a novel 2D Convolutional Neural Networks (CNN). Finally, these features are transformed to discriminant sub-space of information with Auto-Encoder (AE) to guide the identification network. The experimental results show that the proposed multi-channel spatial feature extraction method has certain advantages in recognition performance and efficiency.
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Key words:
- Decoding of MI-EEG /
- Multi-channel feature fusion /
- Subspace features
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表 1 数据经过各卷积层和神经网络层后的映射变化情况
网络结构层 滤波器 滤波器数 输入 输出 参数数量 卷积层1 (10, 1, 1) 50 (1024, 35) (1015, 35, 25) 500 批量归一化处理 (1015, 35, 25) (1015, 35, 25) 最大池化 (1, 5, 35) – (1015, 35, 25) (1015, 7, 1) 卷积层2 (5, 2, 1) 100 (1015, 7, 1) (1011, 6, 50) 1000 批量归一化处理 (1011, 6, 50) (1011, 6, 50) 最大池化 (10, 1, 50) – (1011, 6, 50) (101, 6, 1) 全连接层 (101, 6, 1) 606 
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表 3 不同被试者和方法下的准确率(采集数据D1)
方法 被试者 平均值 A B C D E F CNN 0.826 0.844 0.852 0.862 0.871 0.819 0.8457 LSTM 0.835 0.871 0.872 0.852 0.858 0.848 0.8560 本文MC-CNN 0.851 0.879 0.922 0.863 0.900 0.868 0.8805
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表 4 不同被试者和方法下的准确率(公共数据D2,D3)
方法 S2 S3 A B C D E F G H CNN 0.814 0.825 0.829 0.874 0.803 0.825 0.843 0.804 LSTM 0.882 0.829 0.828 0.858 0.886 0.832 0.868 0.820 本文MC-CNN 0.911 0.921 0.869 0.853 0.870 0.851 0.891 0.860
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表 5 不同多通道MI-EEG解码方法的准确率对比(%)
方法 最差 最优 平均准确率 RC-SFS 80.4 92.3 81.96 RC-SBS 60.7 91.7 79.52 SVM-GA 67.9 94.8 83.86 RC-GA 75.1 98.5 88.20 MC-CNN 86.8 91.9 89.15
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表 6 不同通道对识别结果的影响分布
序号 影响程度 通道 1 极大 C3, C4, Cz 2 大 CP3, FC3, FC4, FC5, CP4, F5 3 中 F3, CP5, FC6, 4 弱 P3, P4, F4, F6, CP6, 5 微弱 C5, C1, C2, C6, FC1, F1, F2, P1, P2, P5, P6, FC2, CP1, CP2, FCz, CPz, Pz, Fz
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