Low-channel EEG devices are crucial for portable and entertainment applications. However, the low spatial resolution of EEG presents challenges in decoding low-channel motor imagery. This study introduces TSFF-Net, a novel network architecture that integrates time-space-frequency features, effectively compensating for the limitations of single-mode feature extraction networks based on time-series or time-frequency modalities. TSFF-Net comprises four main components: time-frequency representation, time-frequency feature extraction, time-space feature extraction, and feature fusion and classification. Time-frequency representation and feature extraction transform raw EEG signals into time-frequency spectrograms and extract relevant features. The time-space network processes time-series EEG trials as input and extracts temporal-spatial features. Feature fusion employs MMD loss to constrain the distribution of time-frequency and time-space features in the Reproducing Kernel Hilbert Space, subsequently combining these features using a weighted fusion approach to obtain effective time-space-frequency features. Moreover, few studies have explored the decoding of three-channel motor imagery based on time-frequency spectrograms. This study proposes a shallow, lightweight decoding architecture (TSFF-img) based on time-frequency spectrograms and compares its classification performance in low-channel motor imagery with other methods using two publicly available datasets. Experimental results demonstrate that TSFF-Net not only compensates for the shortcomings of single-mode feature extraction networks in EEG decoding, but also outperforms other state-of-the-art methods. Overall, TSFF-Net offers considerable advantages in decoding low-channel motor imagery and provides valuable insights for algorithmically enhancing low-channel EEG decoding.
翻译:低通道数的脑电设备对于便携和娱乐应用至关重要。然而,脑电信号的低空间分辨率导致了解码低通道数运动意向的挑战。本研究引入了TSFF-Net,这是一种新的网络结构,它集成了时空频特征,有效弥补了基于时序或时频模态的单模特征提取网络的局限性。TSFF-Net由四个主要组件组成:时频表示、时频特征提取、时空特征提取以及特征融合和分类。时频表示和特征提取将原始脑电信号转换为时频谱图,并提取相关特征。时空网络将时序脑电试验作为输入,提取时空特征。特征融合利用MMD损失在再生核希尔伯特空间内约束时频和时空特征的分布,随后使用加权融合方法将这些特征结合起来以获得有效的时空频特征。此外,很少有研究探讨基于时频谱图的三通道运动意向的解码。本研究提出了一种基于时频谱图的浅层轻量级解码架构(TSFF-img),并将其与其他方法在两个公开数据集上的低通道数运动意向分类表现进行了比较。实验结果表明,TSFF-Net不仅弥补了脑电解码中单模特征提取网络的缺点,而且优于其他现有方法。总的来说,TSFF-Net在解码低通道数运动意向方面具有显着优势,并为算法化增强低通道数脑电解码提供了宝贵的指导。