Key result
A CNN-LSTM neural network model achieved higher average accuracy and kappa coefficient for two-class and four-class EEG signal classification compared to two other classification algorithms.
Why the study?
Brain-machine interfaces have improved living conditions for patients with motor disorders, making EEG signal recognition of great significance for lower limb rehabilitation robots and exoskeletons.
A CNN-LSTM neural network model demonstrates improved accuracy for classifying EEG signals in motion recognition tasks.
Does not support clinical adoption of CNN-LSTM for EEG motion recognition; leaves open its research utility pending validation.
BMI has attracted widespread attention in the past decade, which has greatly improved the living conditions of patients with motor disorders. The application of EEG signals in lower limb rehabilitation robots and human exoskeleton has also been gradually applied by researchers. Therefore, the recognition of EEG signals is of great significance. In this paper, a CNN-LSTM neural network model is designed to study the two-class and four-class motion recognition of EEG signals. In this paper, a brain-computer interface experimental scheme is designed. Combining the characteristics of EEG signals, the time-frequency characteristics of EEG signals and event-related potential phenomena are analyzed, and the ERD/ERS characteristics are obtained. Pre-process EEG signals, and propose a CNN-LSTM neural network model to classify the collected binary and four-class EEG signals. The experimental results show that the CNN-LSTM neural network model has a good effect, and its average accuracy and kappa coefficient are higher than the other two classification algorithms, which also shows that the classification algorithm selected in this paper has a good classification effect.
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Chang et al. (2023) studied Motor disorders (EEG signal recognition). CNN-LSTM neural network model vs. Two other classification algorithms was evaluated on Average accuracy and kappa coefficient for two-class and four-class motion recognition. A CNN-LSTM neural network model achieved higher average accuracy and kappa coefficient for two-class and four-class EEG signal classification compared to two other classification algorithms.
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