A survey that classifies Transformer-based EEG decoding models into backbone, hybrid, and customized categories and reviews their applications and limitations.
EEG based Continuous Speech Recognition using Transformers
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abstract
In this paper we investigate continuous speech recognition using electroencephalography (EEG) features using recently introduced end-to-end transformer based automatic speech recognition (ASR) model. Our results demonstrate that transformer based model demonstrate faster training compared to recurrent neural network (RNN) based sequence-to-sequence EEG models and better performance during inference time for smaller test set vocabulary but as we increase the vocabulary size, the performance of the RNN based models were better than transformer based model on a limited English vocabulary.
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Transformer-based EEG Decoding: A Survey
A survey that classifies Transformer-based EEG decoding models into backbone, hybrid, and customized categories and reviews their applications and limitations.