Decoding the User’s Movements Preparation From EEG Signals Using Vision Transformer Architecture

Abstract

Electroencephalography (EEG) signals have a major impact on how well assistive rehabilitation devices work. These signals have become a common technique in recent studies to investigate human motion functions and behaviors. However, incorporating EEG signals to investigate motor planning or movement intention could benefit all patients who can plan motion but are unable to execute it. In this paper, the movement planning of the lower limb was investigated using EEG signal and bilateral movements were employed, including dorsiflexion and plantar flexion of the right and left ankle joint movements. The proposed system uses Continuous Wavelet Transform (CWT) to generate a time–frequency (TF) map of each EEG signal in the motor cortex and then uses the extracted images as input to a deep learning model for classification. Deep Learning (DL) models are created based on vision transformer architecture (ViT) which is the state-of-the-art of image classification and also the proposed models were compared with residual neural network (ResNet). The proposed technique reveals a significant classification performance for the multiclass problem ( $p < 0.0001$ ) where the classification accuracy was $97.33~\pm ~1.86$ % and the F score, recall and precision were $97.32~\pm ~1.88$ %, $97.30~\pm ~1.90$ % and $97.36~\pm ~1.81$ % respectively. These results show that DL is a promising technique that can be applied to investigate the user’s movements intention from EEG signals and highlight the potential of the proposed model for the development of future brain-machine interface (BMI) for neurorehabilitation purposes.

Keywords

• Continuous wavelet transform
• deep learning
• electroencephalography
• motor-related cortical potentials
• vision transformers architecture