Enhancing the Capability and Accuracy of Motor Imagery Classification: A Deep Neural Network-Powered Multifaceted Strategy Model

Authors:
Publisher website
Google Scholar
Abstract
Motor imagery (MI) is a popular noninvasive brain computer interface (BCI) paradigm, yet its decoding accuracy remains hindered by the inherent nonstationarity and low signal-to-noise ratio of electroencephalogram (EEG) signals. Current decoding frameworks often fail to fully exploit the intricate spatial-temporal dependencies, leading to suboptimal feature representation and the omission of latent discriminative cues. To address these challenges, we introduce a deep neural network-powered multifaceted strategy (DPMS-Net) model, a novel approach that employs dynamic convolution to unearth effective discriminative cues across multiple dimensions, including the temporal, spatial, and frequency domains. This model synergizes channel and temporal attention mechanisms to adeptly capture the salient features of EEG signals across diverse spatial-temporal dimensions, thereby mitigating the risk of omitting critical information. Furthermore, we introduce a spectral-domain analysis component that unearths subtle oscillatory signatures hidden within the EEG spectrum, providing enriched evidence for classification. We evaluated the performance of DPMS-Net on two publicly available datasets and a self-collected dataset from stroke patients. On the BCI Competition IV 2a and BCI Competition IV 2b datasets, DPMS-Net achieved subject-dependent classification accuracies of 83.93% and 88.38%, respectively, alongside subject-independent classification accuracies of 65.88% and 76.01%. In the stroke patient dataset, DPMS-Net attained a subject-dependent classification accuracy of 67.67% and a subject-independent classification accuracy of 57.58%. Experimental results indicate that DPMS-Net possesses efficient decoding capabilities and robust stability, reflecting its potential for deployment in neurorehabilitation BCI systems.
Funding Information
  • Brain Science and Brain-like Intelligence Technology-National Science and Technology Major Project (2022ZD0208900)
  • National Natural Science Foundation of China (62176090)
  • in part by Shanghai Municipal Science and Technology Major Project (2021SHZDZX)
  • Project of Jiangsu Province Science and Technology Plan Special Fund in 2022 (JKH01241605)
  • Project of Jiangsu Province Science and Technology Plan Special Fund in 2022 (BE2022064-1)
  • Lingang Laboratory (LGL8998)

This publication has 65 references indexed in Scilit: