Ma Yan, Tang Yiou, Zeng Yang, Ding Tao, Liu Yifu
College of Computer and Information Science, Chongqing Normal University, Chongqing, China.
Wisdom Education Research Institute, Chongqing Normal University, Chongqing, China.
Front Comput Neurosci. 2023 Feb 16;17:1120566. doi: 10.3389/fncom.2023.1120566. eCollection 2023.
As a time-domain EEG feature reflecting the semantic processing of the human brain, the N400 event-related potentials still lack a mature classification and recognition scheme. To address the problems of low signal-to-noise ratio and difficult feature extraction of N400 data, we propose a Soft-DTW-based single-subject short-distance event-related potential averaging method by using the advantages of differentiable and efficient Soft-DTW loss function, and perform partial Soft-DTW averaging based on DTW distance within a single-subject range, and propose a Transformer-based ERP recognition classification model, which captures contextual information by introducing location coding and a self-attentive mechanism, combined with a Softmax classifier to classify N400 data. The experimental results show that the highest recognition accuracy of 0.8992 is achieved on the ERP-CORE N400 public dataset, verifying the effectiveness of the model and the averaging method.
作为反映人类大脑语义处理的一种时域脑电图特征，N400事件相关电位仍缺乏成熟的分类和识别方案。为解决N400数据信噪比低和特征提取困难的问题，我们利用可微且高效的Soft-DTW损失函数的优势，提出了一种基于Soft-DTW的单受试者短距离事件相关电位平均方法，并在单受试者范围内基于DTW距离进行部分Soft-DTW平均，还提出了一种基于Transformer的ERP识别分类模型，该模型通过引入位置编码和自注意力机制来捕捉上下文信息，并结合Softmax分类器对N400数据进行分类。实验结果表明，在ERP-CORE N400公共数据集上实现了最高0.8992的识别准确率，验证了模型和平均方法的有效性。
