Classification of motor imagery (MI) using non-invasive electroencephalographic (EEG) signals is a critical objective as it is used to predict the intention of limb movements of a subject. In recent research, convolutional neural network (CNN) based methods have been widely utilized for MI-EEG classification. The challenges of training neural networks for MI-EEG signals classification include low signal-to-noise ratio, non-stationarity, non-linearity, and high complexity of EEG signals. The features computed by CNN-based networks on the highly noisy MI-EEG signals contain irrelevant information. Subsequently, the feature maps of the CNN-based network computed from the noisy and irrelevant features contain irrelevant information. Thus, many non-contributing features often mislead the neural network training and degrade the classification performance. Hence, a novel feature reweighting approach is proposed to address this issue. The proposed method gives a noise reduction mechanism named feature reweighting module that suppresses irrelevant temporal and channel feature maps. The feature reweighting module of the proposed method generates scores that reweight the feature maps to reduce the impact of irrelevant information. Experimental results show that the proposed method significantly improved the classification of MI-EEG signals of Physionet EEG-MMIDB and BCI Competition IV 2a datasets by a margin of 9.34% and 3.82%, respectively, compared to the state-of-the-art methods.

翻译：利用非侵入式脑电图（EEG）信号进行运动想象（MI）分类是一项关键目标，因为它用于预测受试者的肢体运动意图。近年来，基于卷积神经网络（CNN）的方法已被广泛用于MI-EEG分类。针对MI-EEG信号分类的神经网络训练面临的挑战包括低信噪比、非平稳性、非线性以及EEG信号的高度复杂性。基于CNN的网络在高度噪声的MI-EEG信号上计算的特征包含无关信息。随后，基于CNN的网络根据这些噪声和无关特征计算出的特征图也包含无关信息。因此，许多非贡献性特征常常误导神经网络的训练，降低分类性能。为此，提出了一种新颖的特征重加权方法来解决这一问题。该方法引入了一种名为特征重加权模块的降噪机制，该机制抑制无关的时间维度和通道维度特征图。所提方法的特征重加权模块生成分数，通过重新加权特征图来减少无关信息的影响。实验结果表明，与现有最先进方法相比，所提方法在Physionet EEG-MMIDB和BCI Competition IV 2a数据集上的MI-EEG信号分类性能分别显著提升了9.34%和3.82%。