Abstract:
The recent advancements in natural language processing (NLP) have introduced novel artificial intelligence models for data classification, extending their scope to analyzing brain signals acquired via electroencephalogram (EEG). Among these developments, the transformer architecture, which has become available in recent years, has provided researchers with a powerful model to explore and evaluate its capabilities in various EEG-related studies, including developing new assistive devices tailored for individuals with impaired communication skills. This work leverages the transformer model to classify P300 event-related potentials on publicly available EEG data, aiming to benchmark its accuracy against established algorithms documented in literature. Upon conducting the case study, the results reveal that the transformer achieves a noteworthy accuracy rate of 95%, indicating its viability as a classifier for P300-based speller.
Referência:
NOVAIS, Victor Hugo Gonçalves Gomes de; LEAL, Adriano Galindo. Classificação de sinais cerebrais sob a ótica dos Large Language Models: aplicação na reabilitação clínica. In: COMPUTER ON THE BEACH, 15., 2024, Camboriu. Proceedings… 8p.
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