A Novel Deep Learning Method Based on an Overlapping Time Window Strategy for Brain–Computer Interface-Based Stroke Rehabilitation

Globally, stroke is a leading cause of death and disability. The classification of motor intentions using brain activity is an important task in the rehabilitation of stroke patients using brain–computer interfaces (BCIs). This paper presents a new method for model training in EEG-based BCI rehabili...

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Bibliographic Details
Published in:Brain sciences 2022-11, Vol.12 (11), p.1502
Main Authors: Cao, Lei, Wu, Hailiang, Chen, Shugeng, Dong, Yilin, Zhu, Changming, Jia, Jie, Fan, Chunjiang
Format: Article
Language:English
Subjects:
Algorithms
brain–computer interface
Classification
Deep learning
deep learning method
Discriminant analysis
EEG
Electroencephalography
Interfaces
Long short-term memory
Machine learning
motor attempt (MA)
Neural networks
overlapping time window
Patients
Rehabilitation
Stroke
Time series
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Summary:Globally, stroke is a leading cause of death and disability. The classification of motor intentions using brain activity is an important task in the rehabilitation of stroke patients using brain–computer interfaces (BCIs). This paper presents a new method for model training in EEG-based BCI rehabilitation by using overlapping time windows. For this aim, three different models, a convolutional neural network (CNN), graph isomorphism network (GIN), and long short-term memory (LSTM), are used for performing the classification task of motor attempt (MA). We conducted several experiments with different time window lengths, and the results showed that the deep learning approach based on overlapping time windows achieved improvements in classification accuracy, with the LSTM combined vote-counting strategy (VS) having achieved the highest average classification accuracy of 90.3% when the window size was 70. The results verified that the overlapping time window strategy is useful for increasing the efficiency of BCI rehabilitation.
ISSN:2076-3425
2076-3425
DOI:10.3390/brainsci12111502