Hierarchical Feature Recalibration Network for Motor Imagery Electroencephalogram (EEG) Classification

Time–frequency–spatial (TFS) features play a crucial role in motor imagery electroencephalogram (EEG) classification. However, effectively leveraging these multidimensional features to enhance classification accuracy remains a significant challenge. Although feature selection techniques are widely used to extract informative TFS representations, most existing methods treat each dimension independently, overlooking the intrinsic grouping and hierarchical relationships among them. To address this limitation, this paper proposes a hierarchical feature recalibration network (HFRNet) that explicitly models intergroup dependencies and the hierarchical structure of TFS features, thereby substantially improving motor imagery EEG classification performance. HFRNet employs a two-layer weighting mechanism for hierarchical feature recalibration, followed by a classification module. In the first weighting layer, spatial features within each time–frequency segment are grouped and represented as feature maps. Channel-wise dependencies are captured through squeeze-and-excitation operations to learn channel weights, which are then used to rescale each feature map. In the second weighting layer, the recalibrated features are reorganized across time windows and further refined through a similar recalibration process. Finally, in the classification block, the refined features are flattened, concatenated into a single feature vector, and passed through dropout and fully connected (FC) layers for classification. Extensive experiments conducted on five motor imagery datasets demonstrate that the proposed HFRNet achieves the best overall performance, with an average accuracy (F1 score) of 81.03% (0.7931). Comparative evaluations against 30 feature selection methods and recent state-of-the-art approaches further confirm the superior effectiveness and robustness of the proposed model.