Unsupervised robust detection of behavioral correlates in ECoG

Carlos A. Loza, Jose C. Principe

Producción científica: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

2 Citas (Scopus)

Resumen

Electrocorticogram (ECoG) based Brain-Computer Interfaces (BCI) provide finer spatial resolution and improved signal-to-noise ratio than its noninvasive counterpart, Electroencephalogram (EEG). This remarkable feature allows for processing in higher spectral bands in order to elucidate more spatially localized encoding mechanisms. We propose an automatic, fully data-driven method to extract relevant neuromodulation events from single-channel, single-trial traces. In particular, our scheme involves two alternating optimizations that resemble k-means; moreover, correntropy is utilized to provide robust estimation and protection against outliers. In this way, we find distinct behavioral correlates in the low-gamma band (76 - 100 Hz) that encode finger flexion movements in a cued task. The results show that correntropy should be used when working with neuronal oscillations due to the high probability of outliers.

Idioma originalInglés
Título de la publicación alojada8th International IEEE EMBS Conference on Neural Engineering, NER 2017
EditorialIEEE Computer Society
Páginas509-512
Número de páginas4
ISBN (versión digital)9781538619162
DOI
EstadoPublicada - 10 ago. 2017
Publicado de forma externa
Evento8th International IEEE EMBS Conference on Neural Engineering, NER 2017 - Shanghai, China
Duración: 25 may. 201728 may. 2017

Serie de la publicación

NombreInternational IEEE/EMBS Conference on Neural Engineering, NER
ISSN (versión impresa)1948-3546
ISSN (versión digital)1948-3554

Conferencia

Conferencia8th International IEEE EMBS Conference on Neural Engineering, NER 2017
País/TerritorioChina
CiudadShanghai
Período25/05/1728/05/17

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