The Promotɶr is an all-in-one Brain Computer Interface (BCI)-system developed at Fondazione Santa Lucia (Rome, Italy) to support hand motor imagery practice after stroke. In this paper we focus on the optimization of control parameters for the BCI training. We compared two procedures for the feature selection: in the first, features were selected by means of a manual procedure (requiring “skilled users”), in the second a semiautomatic method, developed by us combining physiological and machine learning approaches, guided the feature selection. EEG-based BCI data set collected from 13 stroke patients were analyzed to the aim. No differences were found between the two procedures (paired-samples t-test, p=0.13). Results suggest that the semiautomatic procedure could be applied to support the manual feature selection, allowing no-skilled users to approach BCI technology for motor rehabilitation of stroke patients.

Neurophysiological constraints of control parameters for a brain computer interface system to support post-stroke motor rehabilitation / Colamarino, Emma; Pichiorri, Floriana; Mattia, Donatella; Cincotti, Febo. - ELETTRONICO. - (2017), pp. 6-7. (Intervento presentato al convegno School & Symposium on Advanced Neurorehabilitation 2017 tenutosi a Baiona; Spain nel 17-22/09/2017).

Neurophysiological constraints of control parameters for a brain computer interface system to support post-stroke motor rehabilitation

COLAMARINO, EMMA
;
PICHIORRI, FLORIANA
;
CINCOTTI, FEBO
2017

Abstract

The Promotɶr is an all-in-one Brain Computer Interface (BCI)-system developed at Fondazione Santa Lucia (Rome, Italy) to support hand motor imagery practice after stroke. In this paper we focus on the optimization of control parameters for the BCI training. We compared two procedures for the feature selection: in the first, features were selected by means of a manual procedure (requiring “skilled users”), in the second a semiautomatic method, developed by us combining physiological and machine learning approaches, guided the feature selection. EEG-based BCI data set collected from 13 stroke patients were analyzed to the aim. No differences were found between the two procedures (paired-samples t-test, p=0.13). Results suggest that the semiautomatic procedure could be applied to support the manual feature selection, allowing no-skilled users to approach BCI technology for motor rehabilitation of stroke patients.
2017
School & Symposium on Advanced Neurorehabilitation 2017
BCI; Stroke Rehabilitation; EEG; Motor Imagery
04 Pubblicazione in atti di convegno::04b Atto di convegno in volume
Neurophysiological constraints of control parameters for a brain computer interface system to support post-stroke motor rehabilitation / Colamarino, Emma; Pichiorri, Floriana; Mattia, Donatella; Cincotti, Febo. - ELETTRONICO. - (2017), pp. 6-7. (Intervento presentato al convegno School & Symposium on Advanced Neurorehabilitation 2017 tenutosi a Baiona; Spain nel 17-22/09/2017).
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1010833
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