S Chrisilla, Shantha SelvaKumari R
Department of Electronics and Communication Engineering, Mepco Schlenk Engineering College, Sivakasi, India.
Clin EEG Neurosci. 2025 Jan 29:15500594241312450. doi: 10.1177/15500594241312450.
Motor Imagery (MI) electroencephalographic (EEG) signal classification is a pioneer research branch essential for mobility rehabilitation. This paper proposes an end-to-end hybrid deep network "Spatio Temporal Inception Transformer Network (STIT-Net)" model for MI classification. Discrete Wavelet Transform (DWT) is used to derive the alpha (8-13) Hz and beta (13-30) Hz EEG sub bands which are dominant during motor tasks to enhance the performance of the proposed work. STIT-Net employs spatial and temporal convolutions to capture spatial dependencies and temporal information and an inception block with three parallel convolutions extracts multi-level features. Then the transformer encoder with self-attention mechanism highlights the similar task. The proposed model improves the classification of the Physionet EEG motor imagery dataset with an average accuracy of 93.52% and 95.70% for binary class in the alpha and beta bands respectively, and 85.26% and 87.34% for three class, for four class 81.95% and 82.66% were obtained in the alpha and beta band respective EEG based motor signals which is better compared to the results available in the literature. The proposed methodology is further evaluated on other motor imagery datasets, both for subject-independent and cross-subject conditions, to assess the performance of the model.
运动想象（MI）脑电图（EEG）信号分类是运动康复领域至关重要的前沿研究分支。本文提出了一种用于MI分类的端到端混合深度网络“时空 inception 变压器网络（STIT-Net）”模型。离散小波变换（DWT）用于提取在运动任务中占主导地位的α（8 - 13）Hz和β（13 - 30）Hz脑电亚带，以提高所提方法的性能。STIT-Net采用空间和时间卷积来捕捉空间依赖性和时间信息，并且一个具有三个并行卷积的inception模块提取多级特征。然后，带有自注意力机制的变压器编码器突出相似任务。所提模型分别在α和β频段将Physionet脑电运动想象数据集的二分类平均准确率提高到93.52%和95.70%，三分类为85.26%和87.34%，四分类在α和β频段基于脑电的运动信号中分别达到81.95%和82.66%，与文献中的结果相比表现更优。所提方法在其他运动想象数据集上进一步针对独立受试者和跨受试者条件进行评估，以评估该模型的性能。
