运动想象Physics-Informed Attention Temporal Convolutional Network源码

上传者: Nan_Feng_ya | 上传时间: 2025-02-08 18:36:13 | 文件大小: 8.53MB | 文件类型: ZIP
脑机接口(BCI)是一项有可能改变世界的前沿技术。脑电图(EEG)运动图像(MI)信号已被广泛用于许多BCI应用中以协助残疾人控制设备或环境、甚至增强人的能力。然而大脑信号解码的有限性能限制了BCI行业的广泛发展。在这篇文章中,我们提出了一个基于注意力的时间卷积网络(ATCNet)用于基于EEG的运动图像分类。该ATCNet模型利用多种技术来提高MI分类的性能,参数数量相对较少。ATCNet采用了科学的机器学习来设计一个特定领域的深度学习模型,具有可解释和可说明的特征,多头自我关注来突出MI-EEG数据中最有价值的特征,时间卷积网络来提取高层次的时间特征,以及基于卷积的滑动特征。颞部卷积网络提取高层次的时间特征,基于卷积的滑动窗口有效地增强了MI-EEG数据。所提出的模型在BCI中的表现优于目前最先进的技术。在IV-2a数据集中,提议的模型优于目前最先进的技术,准确率为85.38%和70.97%。

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