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
Online-Zugang:	Volltext
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