EEG-Transformer: Self-attention from Transformer Architecture for Decoding EEG of Imagined Speech

Transformers are groundbreaking architectures that have changed a flow of deep learning, and many high-performance models are developing based on transformer architectures. Transformers implemented only with attention with encoder-decoder structure following seq2seq without using RNN, but had better...

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Veröffentlicht in:The ... International Winter Conference on Brain-Computer Interface S. 1 - 4
Hauptverfasser: Lee, Young-Eun, Lee, Seo-Hyun
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 21.02.2022
Schlagworte:
attention module
brain-computer interface
Communication systems
Deep learning
Electroencephalography
Hardware
imagined speech
Speech recognition
Statistical analysis
transformer
Transformers
ISSN:2572-7672
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Zusammenfassung:Transformers are groundbreaking architectures that have changed a flow of deep learning, and many high-performance models are developing based on transformer architectures. Transformers implemented only with attention with encoder-decoder structure following seq2seq without using RNN, but had better performance than RNN. Herein, we investigate the decoding technique for electroencephalography (EEG) composed of self-attention module from transformer architecture during imagined speech and overt speech. We performed classification of nine subjects using convolutional neural network based on EEGNet that captures temporal-spectral-spatial features from EEG of imagined speech and overt speech. Furthermore, we applied the self-attention module to decoding EEG to improve the performance and lower the number of parameters. Our results demonstrate the possibility of decoding brain activities of imagined speech and overt speech using attention modules. Also, only single channel EEG or ear-EEG can be used to decode the imagined speech for practical BCIs.
ISSN:2572-7672
DOI:10.1109/BCI53720.2022.9735124