基于脑电图的视觉刺激识别线性-注意-组合卷积神经网络

IF 5.3 2区 医学 Q1 ENGINEERING, BIOMEDICAL Biocybernetics and Biomedical Engineering Pub Date : 2024-04-01 DOI:10.1016/j.bbe.2024.05.001
Junjie Huang, Wanzhong Chen, Tao Zhang
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引用次数: 0

摘要

基于脑电图(EEG)的视觉刺激识别任务已成为脑机接口(BCI)研究领域的一个重要课题。虽然脑电图的底层空间特征可以有效地表示视觉刺激信息,但要探索底层脑电图的局部-全局信息以实现更好的解码性能,仍然是一项极具挑战性的任务。因此,本文针对基于视觉刺激的脑电图分类任务,提出了一种名为线性-注意力-组合卷积神经网络(LACNN)的深度学习架构。该架构结合了卷积神经网络(CNN)和线性注意(Larine Attention)模块,能有效提取脑电图的局部和全局特征进行解码,同时保持较低的计算复杂度和模型参数。我们在斯坦福大学数字资料库的公共脑电图数据集上进行了大量实验。实验结果表明,LACNN 在 6 类和 72 例分类任务中的平均解码准确率分别达到 54.13% 和 29.83%,优于最先进的方法,这表明我们的方法能有效地从脑电图中解码视觉刺激。对 LACNN 的进一步分析表明,线性注意模块提高了不同类别特征之间的可分离性,并定位了符合范式原理的关键脑区信息。
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A linear-attention-combined convolutional neural network for EEG-based visual stimulus recognition

The recognition task of visual stimuli based on EEG (Electroencephalogram) has become a major and important topic in the field of Brain–Computer Interfaces (BCI) research. Although the underlying spatial features of EEG can effectively represent visual stimulus information, it still remains a highly challenging task to explore the local–global information of the underlying EEG to achieve better decoding performance. Therefore, in this paper we propose a deep learning architecture called Linear-Attention-combined Convolutional Neural Network (LACNN) for visual stimuli EEG-based classification task. The proposed architecture combines the modules of Convolutional Neural Networks (CNN) and Linear Attention, effectively extracting local and global features of EEG for decoding while maintaining low computational complexity and model parameters. We conducted extensive experiments on a public EEG dataset from the Stanford Digital Repository. The experimental results demonstrate that LACNN achieves an average decoding accuracy of 54.13% and 29.83% in 6-category and 72-exemplar classification tasks respectively, outperforming the state-of-the-art methods, which indicates that our method can effectively decode visual stimuli from EEG. Further analysis of LACNN shows that the Linear Attention module improves the separability between different category features and localizes key brain region information that aligns with the paradigm principles.

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来源期刊
CiteScore
16.50
自引率
6.20%
发文量
77
审稿时长
38 days
期刊介绍: Biocybernetics and Biomedical Engineering is a quarterly journal, founded in 1981, devoted to publishing the results of original, innovative and creative research investigations in the field of Biocybernetics and biomedical engineering, which bridges mathematical, physical, chemical and engineering methods and technology to analyse physiological processes in living organisms as well as to develop methods, devices and systems used in biology and medicine, mainly in medical diagnosis, monitoring systems and therapy. The Journal''s mission is to advance scientific discovery into new or improved standards of care, and promotion a wide-ranging exchange between science and its application to humans.
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