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Abstract：
CNN has demonstrated remarkable performance in EEG signal detection, yet it still faces limitations in terms of global perception. Additionally, due to individual differences in EEG signals, the generalization ability of epilepsy detection models is week. To address this issue, this paper presents a cross-patient epilepsy detection method utilizing a multi-head self-attention mechanism. This method first utilizes Short-Time Fourier Transform (STFT) to transform the original EEG signals into time-frequency features, then models local information using Convolutional Neural Network (CNN), subsequently captures global dependency relationships between features using the multi-head self-attention mechanism of Transformer, and finally performs epilepsy detection using these features. Meanwhile, this model employs a light multi-head attention mechanism module with an alternating structure, which can comprehensively extract multi-scale features while significantly reducing computational costs. Experimental results on the CHB-MIT dataset show that the proposed model achieves accuracy, sensitivity, specificity, F1 score, and AUC of 92.89%, 96.17%, 92.99%, 94.41%, and 96.77%, respectively. Compared to the existing methods, the method proposed in this paper obtains better performance along with better generalization.
摘要：
CNN在EEG信号检测方面表现出卓越的性能，然而，它仍然面临着全球认知方面的限制。此外,由于脑电信号的个体差异，癫痫检测模型的泛化能力为周。为了解决这个问题，本文提出了一种利用多头自我注意机制的跨患者癫痫检测方法。该方法首先利用短时傅里叶变换(STFT)将原始脑电信号转换为时频特征,然后使用卷积神经网络(CNN)对本地信息进行建模，随后使用Transformer的多头自注意机制捕获特征之间的全局依赖关系，最后使用这些特征进行癫痫检测。同时,该模型采用了具有交替结构的轻型多头注意机制模块，可以综合提取多尺度特征,同时显著降低计算成本。在CHB-MIT数据集上的实验结果表明，所提出的模型具有较高的准确性，灵敏度,特异性，F1得分,AUC为92.89%，96.17%,92.99%,94.41%,96.77%,分别。与现有方法相比，本文提出的方法具有较好的性能和较好的推广性。
