PPGCCM PÓS-GRADUAÇÃO EM CIÊNCIA DA COMPUTAÇÃO FUNDAÇÃO UNIVERSIDADE FEDERAL DO ABC Telefone/Ramal: 11 4996-8337 http://propg.ufabc.edu.br/ppgccm

Banca de QUALIFICAÇÃO: LUCAS HECK DOS SANTOS

Uma banca de QUALIFICAÇÃO de DOUTORADO foi cadastrada pelo programa.
STUDENT : LUCAS HECK DOS SANTOS
DATE: 09/12/2024
TIME: 09:00
LOCAL: por participação remota
TITLE:

Cross-Dataset Motor Imagery Classification with Deep Learning and Riemannian Geometry


PAGES: 70
BIG AREA: Ciências Exatas e da Terra
AREA: Ciência da Computação
SUBÁREA: Metodologia e Técnicas da Computação
SPECIALTY: Sistemas de Informação
SUMMARY:

Brain-Computer Interface (BCI) systems have recently gained attention due to their applications in the medicinal and entertainment fields. Frequently, for these tasks, classification is done from signals recorded from brain activity to enable application control without the usual interactions. However, brain signals, such as that from electroencephalography (EEG), are of high complexity and subject to noise and artifacts, exacerbated by recording systems. Traditional methods, which require manual adjustments and domain knowledge, encounter obstacles due to the large variability of these signals. Lately, to interpret those signals, many authors have been using Deep Neural Networks (DNNs) in an end-to-end approach. In this context, EEGNet, a Convolutional Neural Networks (CNNs), achieved impressive results, both in the paradigms of selective attention and motor imagery, surpassing the accuracy of traditional methods. In parallel, some works employ Riemannian Geometry (RG) as an alternative, achieving relevant accuracy while training without a gradient optimization method, contrary to DNNs. However, both approaches have limitations, suffering in the cross-subject and cross-dataset perspectives. The current PhD research aims to build a hybrid architecture that can take advantage of both strategies. This architecture will be applied to a set of motor imagery datasets, using features of each other, hoping to achieve state-of-the-art performance.


COMMITTEE MEMBERS:
Presidente - Interno ao Programa - 342.927.028-61 - DENIS GUSTAVO FANTINATO - UNICAMP
Membro Titular - Examinador(a) Externo ao Programa - 1761107 - RICARDO SUYAMA
Membro Titular - Examinador(a) Externo à Instituição - SARAH NEGREIROS DE CARVALHO LEITE - ITA
Membro Suplente - Examinador(a) Interno ao Programa - 1676329 - RAPHAEL YOKOINGAWA DE CAMARGO
Membro Suplente - Examinador(a) Externo à Instituição - LEVY BOCCATO - UNICAMP
Notícia cadastrada em: 25/11/2024 06:56
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