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Brain-computer interface (BCI) based motor imagery (MI) can assist stroke patients in upper limb rehabilitation and help restore motor function to a certain extent. However, the classical MI paradigm distinguishes different limbs and cannot effectively meet the needs of upper limb rehabilitation training for patients. Therefore, this paper designed a new paradigm for three motor imagery actions targeting different joints of the unilateral upper limb, and electroencephalogram (EEG) data from 20 healthy participants were collected for research analysis. A deep neural network model combining an attention mechanism for multiple frequency bands and a deep convolutional network were proposed to adaptively assign weight to the EEG data in different frequency bands. Then feature extraction was performed for each frequency band to learn further and to classify features. This model can obtain an average accuracy of 69.2% for the subject-independent case with the triple classification in the designed fine motor imagery (FMI) dataset, which is better than other controlled methods. Furthermore, ablation experiments were conducted for each module, demonstrating the effectiveness of each module. These results manifest the feasibility of our proposed method and the potential of FMI paradigm for BCI, providing a new training tool for upper limb rehabilitation after stroke.

A study of unilateral upper limb fine motor imagery decoding using frequency-band attention network