复旦大学Xin Wang等研究人员合作开发出基于轻量级卷积神经网络的任务相关脑电图特征提取方法。相关论文于2024年7月2日在线发表在《神经科学通报》杂志上。

研究人员表示，揭示与任务相关的脑电图频谱对神经科学至关重要。传统的卷积神经网络（CNN）能有效提取这些特征，但由于数据集较小，因此面临过度拟合等限制。

为解决这一问题，研究人员提出了一种轻量级CNN，并通过全连接层（FCL）评估其可解释性。最初通过两个任务（任务 1：睁眼与闭眼，任务 2：发作间期与发作期）进行测试，结果表明，CNN在任务1和任务2中分别增强了α波段和θ波段的频谱特征，这与已有的神经生理学特征相吻合。

随后进行的两项脑机接口任务实验显示，δ活动（约1.55Hz）与手部运动之间存在相关性，并且在中心周围脑电图（EEG）通道中结果一致。与最近的研究相比，该方法通过FCL提供与任务相关的频谱特征，从而大大减少了可训练参数，同时保持了可解释性。这表明它可能适用于更广泛的脑电图解码场景。

附：英文原文

Title: A Method to Extract Task-Related EEG Feature Based on Lightweight Convolutional Neural Network

Author: Huang, Qi, Ding, Jing, Wang, Xin

Issue&Volume: 2024-07-02

Abstract: Unlocking task-related EEG spectra is crucial for neuroscience. Traditional convolutional neural networks (CNNs) effectively extract these features but face limitations like overfitting due to small datasets. To address this issue, we propose a lightweight CNN and assess its interpretability through the fully connected layer (FCL). Initially tested with two tasks (Task 1: open vs closed eyes, Task 2: interictal vs ictal stage), the CNN demonstrated enhanced spectral features in the alpha band for Task 1 and the theta band for Task 2, aligning with established neurophysiological characteristics. Subsequent experiments on two brain-computer interface tasks revealed a correlation between delta activity (around 1.55 Hz) and hand movement, with consistent results across pericentral electroencephalogram (EEG) channels. Compared to recent research, our method stands out by delivering task-related spectral features through FCL, resulting in significantly fewer trainable parameters while maintaining comparable interpretability. This indicates its potential suitability for a wider array of EEG decoding scenarios.

DOI: 10.1007/s12264-024-01247-6

Source: https://link.springer.com/article/10.1007/s12264-024-01247-6