Key result
Transformer-based models for motor imaginary EEG classification achieved superior performance compared to previous state-of-the-art models, with positional embedding improving classification accuracy.
Why the study?
Handling long-range dependencies is important for recognizing time series signals like EEG, but Transformer had not yet been used in EEG recognition.
Answers are grounded in this paper and cite their sources.
Transformer-based models combined with CNN show promise for improving the classification accuracy of EEG sequence data.
Transformer has been widely used in the field of natural language processing (NLP) with its superior ability to handle long-range dependencies in comparison with convolutional neural network (CNN) and recurrent neural network (RNN). This correlation is also important for the recognition of time series signals, such as electroencephalogram (EEG). Currently, commonly used EEG classification models are CNN, RNN, deep believe network (DBN), and hybrid CNN. Transformer has not been used in EEG recognition. In this study, we constructed multiple Transformer-based models for motor imaginary (MI) EEG classification, and obtained superior performances in comparison with the previous state-of-art. We found that the activities of the motor cortex had a great contribution to classification in our model through visualization, and positional embedding (PE) method could improve classification accuracy. These results suggest that the attention mechanism of Transformer combined with CNN might be a powerful model for the recognition of sequence data.
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Sun et al. (2021) studied Motor imaginary (MI) EEG classification. Transformer-based models vs. Previous state-of-the-art models (CNN, RNN, DBN) was evaluated on Classification accuracy. Transformer-based models for motor imaginary EEG classification achieved superior performance compared to previous state-of-the-art models, with positional embedding improving classification accuracy.
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