A Fast Recognition Method of SSVEP Signals Based on Time-Frequency Multiscale
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摘要: 目前基于稳态视觉诱发电位(SSVEP)的脑机接口在人机协作中受到广泛关注，但较短时长 SSVEP 信号仍面临信噪比较低、特征提取不充分的问题。该文从频域、时域以及空域3个角度分析并提取SSVEP信号特征。首先该方法从由频域实部信息和虚部信息整合的3维重校正特征矩阵中提取幅值和相位特征信息。然后在时域中通过训练多个刺激时窗尺度的样本增强模型表征能力。最后利用不同尺度的1维卷积核，并行提取通道空间和频域上的多尺度特征信息。该文在两种不同的视觉刺激频率和频率间隔的公开数据集上进行实验，在时窗为1 s时的平均准确率和平均信息传输率(ITR)均优于现有的其他方法。Abstract: A brain-computer interface based on Steady-State Visual Evoked Potential (SSVEP) has recently garnered considerable interest in human-computer cooperation. Nevertheless, SSVEP signals with short time windows suffer from a low signal-to-noise ratio and insufficient feature extraction. This study examines and extracts the SSVEP signal characteristics from three perspectives: frequency domain, time domain and spatial domain. The proposed method extracts the amplitude and phase feature information from a three-dimensional recalibrated feature matrix developed by incorporating the real part and the imaginary part information in the frequency domain. Subsequently, the model’s representation ability is enhanced by training samples across multiple stimulus time window scales in the time domain. Finally, multiscale feature information in the channel space and frequency domain is extracted in parallel by using distinct scaled one-dimensional convolution kernels with. In this paper, experiments are conducted on two open datasets characterized by different visual stimulus frequencies and frequency intervals. The average accuracy and average information transfer rate at a time window of 1 s surpass the performance of existing methods.
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表 1 不同方法在数据集1(时间窗口长度为1 s)上的平均实验结果
方法 Acc(%) ITR(bit/min) TRCA 19.34 3.44 C_CNN 88.61 107.17 PLFA_Net 66.03 59.41 tCNN 85.24 98.82 EEGNet 87.25 103.72 SMS1D_CNN 89.22 108.75 MS1D_CNN 89.35 109.09 
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表 2 不同方法在数据集2(时间窗口长度为1 s)上的平均实验结果
方法 Acc(%) ITR(bit/min) TRCA 19.48 1.12 C_CNN 79.58 67.86 PLFA_Net 85.42 79.66 tCNN 76.60 62.33 EEGNet 76.51 62.16 SMS1D_CNN 87.37 83.93 MS1D_CNN 90.17 90.42
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表 3 不同方法在数据集1(跨被试)上的平均实验结果
方法 Acc(%) ITR(bit/min) TRCA 23.44 6.03 C_CNN 74.73 75.81 PLFA_Net 58.59 46.95 tCNN 74.44 75.23 EEGNet 75.39 77.14 SMS1D_CNN 76.69 79.81 MS1D_CNN 77.46 81.41
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表 4 不同方法在数据集2(跨被试)上的平均实验结果
方法 Acc(%) ITR(bit/min) TRCA 21.29 1.73 C_CNN 93.10 97.76 PLFA_Net 87.99 85.33 tCNN 82.42 73.43 EEGNet 93.67 99.28 SMS1D_CNN 94.41 101.28 MS1D_CNN 96.06 105.99
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