IncepA-EEGNet: 融合Inception网络和注意力机制的P300信号检测方法

许萌,王丹,李致远,陈远方

IncepA-EEGNet: P300 signal detection method based on fusion of Inception network and attention mechanism

Meng XU,Dan WANG,Zhi-yuan LI,Yuan-fang CHEN

表 6 IncepA-EEGNet 模型与其他方法的字符识别率

Tab.6 Character recognition rate of IncepA-EEGNet model and other methods

方法 受试者 Pc/% n = 1 n = 2 n = 3 n = 4 n = 5 n = 6 n = 7 n = 8 n = 9 n = 10 n = 11 n = 12 n = 13 n = 14 n = 15 CNN-1[10] A 16 33 47 52 61 65 77 78 85 86 90 91 91 93 97 CNN-1[10] B 35 52 59 68 79 81 82 89 92 91 91 90 91 92 92 MCNN-1[10] A 18 31 50 54 61 68 76 76 79 82 89 92 91 93 97 MCNN-1[10] B 39 55 62 64 77 79 86 92 91 92 95 95 95 94 94 MCNN-3[10] A 17 35 50 55 63 67 78 79 84 85 91 90 92 94 97 MCNN-3[10] B 34 56 60 68 74 80 82 89 90 90 91 88 90 91 92 BN3[17] A 22 39 58 67 73 75 79 81 82 86 89 92 94 96 98 BN3[17] B 47 59 70 73 76 82 84 91 94 95 95 95 94 94 95 EEGNet[20] A 18 33 46 60 68 70 82 82 83 85 88 90 91 96 99 EEGNet[20] B 39 49 56 65 76 80 85 87 89 89 90 90 90 92 93 1D-CapsNet-64[18] A 21 32 45 53 60 68 76 83 85 84 82 88 94 96 98 1D-CapsNet-64[18] B 48 54 60 66 75 81 81 86 87 93 93 93 92 93 94 CM-CW-CNN-ESVM[19] A 22 32 55 59 64 70 74 78 81 86 86 90 91 94 99 CM-CW-CNN-ESVM[19] B 37 58 70 72 80 86 86 89 93 95 95 97 97 98 99 IncepA-EEGNet A 19 34 47 62 70 71 84 83 85 89 92 93 94 96 100 IncepA-EEGNet B 41 59 73 77 81 85 88 90 92 95 95 95 95 95 95