Time-Frequency-Space EEG Decoding Model Based on Dense Graph Convolutional Network for Stroke

Stroke, a sudden cerebrovascular ailment resulting from brain tissue damage, has prompted the use of motor imagery (MI)-based Brain-Computer Interface (BCI) systems in stroke rehabilitation. However, analyzing EEG signals from stroke patients is challenging because of their low signal-to-noise ratio and high variability. Therefore, we propose a novel approach that combines the modified S-transform (MST) and a dense graph convolutional network (DenseGCN) algorithm to enhance the MI-BCI performance across time, frequency, and space domains. MST is a time-frequency analysis method that efficiently concentrates energy in EEG signals, while DenseGCN is a deep learning model that uses EEG feature maps from each layer as inputs for subsequent layers, facilitating feature reuse and hyper-parameters optimization. Our approach outperforms conventional networks, achieving a peak classification accuracy of 90. 22% and an average information transfer rate (ITR) of 68. 52 bits per minute. Moreover, we conduct an in-depth analysis of the event-related desynchronization/event-related synchronization (ERD/ERS) phenomenon in the deep-level EEG features of stroke patients. Our experimental results confirm the feasibility and efficacy of the proposed approach for MI-BCI rehabilitation systems.

Authors

• Jiancai Leng
• Han Li Kuaishou Technology
• Weiyou Shi
• Licai Gao
• Chengyan Lv
• Chen Wang Northwest Polytechnical University
• Fangzhou Xu
• Yang Zhang
• Tzyy-Ping Jung

Keywords

• Electroencephalography
• Stroke (medical condition)
• Motors
• Signal processing algorithms
• Electrodes
• Task analysis
• Brain modeling
• Convolutional Network
• Graph Convolutional Network
• EEG Decoding
• Deep Learning
• Feature Maps
• Stroke Patients
• EEG Signals
• Stroke Rehabilitation
• Subsequent Layers
• Time-frequency Analysis
• Motor Imagery
• EEG Features
• Brain-computer Interface System
• Event-related Synchronization
• Time-frequency Analysis Method
• Electrode
• Healthy Subjects
• Convolutional Neural Network
• Support Vector Machine
• Motor Imagery Tasks
• Right Hand Movement
• EEG Data
• Phenomenon In Patients
• Motor Cortex
• Gradient Boosting
• Premotor Cortex
• EEG Data Collection
• Frontal Cortex
• Conv Layer
• Electroencephalogram
• brain-computer interface
• dense graph convolutional network
• stroke
• Humans
• Brain-Computer Interfaces
• Signal Processing, Computer-Assisted
• Algorithms
• Neural Networks, Computer
• Brain
• Male
• Female
• Middle Aged
• Adult