Intra-subject class-incremental deep learning approach for EEG-based imagined speech recognition - Publikacja - MOST Wiedzy

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Intra-subject class-incremental deep learning approach for EEG-based imagined speech recognition

Abstrakt

Brain–computer interfaces (BCIs) aim to decode brain signals and transform them into commands for device operation. The present study aimed to decode the brain activity during imagined speech. The BCI must identify imagined words within a given vocabulary and thus perform the requested action. A possible scenario when using this approach is the gradual addition of new words to the vocabulary using incremental learning methods. An issue with incremental learning methods is degradation of the decoding capacity of the original model when new classes are added. In this study, a class-incremental neural network method is proposed to increase the vocabulary of imagined speech. The results indicate a stable model that did not degenerate when a new word was integrated. The proposed method allows for the inclusion of newly imagined words without a significant loss of total accuracy for the two datasets.

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Autorzy (4)

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Informacje szczegółowe

Kategoria:
Publikacja w czasopiśmie
Typ:
artykuły w czasopismach
Opublikowano w:
Biomedical Signal Processing and Control nr 81,
ISSN: 1746-8094
Język:
angielski
Rok wydania:
2023
Opis bibliograficzny:
Garcia Salinas J., Torres-García A. A., Reyes-Garćia C. A., Villaseñor-Pineda L.: Intra-subject class-incremental deep learning approach for EEG-based imagined speech recognition// Biomedical Signal Processing and Control -Vol. 81, (2023), s.104433-
DOI:
Cyfrowy identyfikator dokumentu elektronicznego (otwiera się w nowej karcie) 10.1016/j.bspc.2022.104433
Źródła finansowania:
  • Publikacja bezkosztowa
Weryfikacja:
Politechnika Gdańska

wyświetlono 141 razy

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