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Updated: Apr 12, 2026

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EEG-VTTCNet:基于视觉变压器和时间卷积网络的损失联合训练模型,用于基于EEG的运动图像分类

Xingbin Shi1, Baojiang Li1, Wenlong Wang1

  • 1The School of Electrical Engineering, Shanghai Dianji University, Shanghai 201306, China; Intelligent Decision and Control Technology Institute, Shanghai Dianji University, Shanghai 201306, China.

Neuroscience
|August 5, 2024
PubMed
概括

本研究介绍了EEG-VTTCNet,这是一个新的脑计算机接口 (BCI) 模型,使用视觉变压器和时卷积网络来解码运动图像电脑脑图像 (MI-EEG) 信号. 该模型实现了最先进的准确性,增强了BCI应用程序.

关键词:
大脑与计算机接口 (BCI)共同的损失共同的损失运动图像 (MI)时间卷积网络 (TCN)视觉变压器 (VIT) 是一个视觉变压器.

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