Abstract
Event-Related Desynchronization (ERD) or Electroencephalogram (EEG) wavelet is essential for motor imagery (MI) classification and BMI (Brain–Machine Interface) application. However, it is difficult to recognize multiple tasks for non-trained subjects that are indispensable for the complexities of the task or the uncertainties in the environment. The subject-independent scenario, where an inter-subject trained model can be directly applied to new users without precalibration, is particularly desired. Therefore, this paper focuses on an effective attention mechanism which can be applied to a subject-independent set to learn EEG motor imagery features. Firstly, a custom form of sequence inputs with spatial and temporal dimensions is adopted for dual headed attention via deep convolution net (DHDANet). Secondly, DHDANet simultaneously learns temporal and spacial features. The features of spacial attention on each input head are divided into two parts for spatial attentional learning subsequently. The proposed model is validated based on the EEG-MI signals collected from 54 subjects in two sessions with 200 trials in each sessions. The classification of left and right hand motor imagery in this paper achieves an average accuracy of 75.52%, a significant improvement compared to state-of-the-art methods. In addition, the visualization of the frequency analysis method demonstrates that the temporal-convolution and spectral-attention is capable of identifying the ERD for EEG-MI. The proposed machine learning structure enables cross-session and cross-subject classification and makes significant progress in the BMI transfer learning problem.
Highlights
Motor imagery (MI) classification based on electroencephalogram (EEG) event-related synchronization (ERS) and event-related desynchronization (ERD) phenomena is a measure of the neuron extent when people image body movements [1,2,3,4].In recent years, two typical and general approaches make important achievements in EEG-MI recognition and brain–machine interface (BMI): optimizing the hand-crafted features and extracting the ERS/ERD features by deep learning
The comparison of dual headed attention via deep convolution net (DHDANet) to the best method in the literature is based on the classification performance through the data collection on the KU-MI data set
This paper proposed a neurophysiologically motivated DHDANet architecture for classification of motor imagery EEG data
Summary
Two typical and general approaches make important achievements in EEG-MI recognition and brain–machine interface (BMI): optimizing the hand-crafted features and extracting the ERS/ERD features by deep learning. For the former approach, common spatial pattern (CSP) filters and Riemannian Manifold [5,6,7,8] are two popular and effective methods. Li’s group used the geodesic distance of Riemannian manifold to determine the adjacency and weight in Riemannian graph, and proposed bilinear regularized locality preserving (BRLP) to address the problem of high dimensions frequently arising from BMIs [6]. Researchers in [12] utilized a scheme for transfer learning to use the Riemannian geometry of symmetric and positive definite(SPD)
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