A Brain–Computer Interface (BCI) uses measurements of the voluntary brain activity for driving a communication system; it requires the activation of mental tasks. In the last few years, a new paradigm of activation has been used, consisting in the autonomous brain activation through self-induced emotions, remembered on autobiographical basis. In the present paper, such paradigm is implemented and the resulting BCI system is described, from the classification strategy to the graphic user interface necessary for synchronising mental tasks and collecting EEG signals derived by emotions. Moreover, the proposed BCI is used for collecting and classifying signals, from 10 healthy subjects, of two different emotional states: the disgust produced by remembering a bad odour and the good sensation produced by remembering the odour of a good fragrance, with respect to relax. The classifications are performed in a binary mode, by recognising disgust from relax and good sensation from relax, yielding an accuracy greater than 85%.

Self-induced emotions as alternative paradigm for driving brain–computer interfaces

Placidi G.
;
Polsinelli M.;Spezialetti M.;
2019

Abstract

A Brain–Computer Interface (BCI) uses measurements of the voluntary brain activity for driving a communication system; it requires the activation of mental tasks. In the last few years, a new paradigm of activation has been used, consisting in the autonomous brain activation through self-induced emotions, remembered on autobiographical basis. In the present paper, such paradigm is implemented and the resulting BCI system is described, from the classification strategy to the graphic user interface necessary for synchronising mental tasks and collecting EEG signals derived by emotions. Moreover, the proposed BCI is used for collecting and classifying signals, from 10 healthy subjects, of two different emotional states: the disgust produced by remembering a bad odour and the good sensation produced by remembering the odour of a good fragrance, with respect to relax. The classifications are performed in a binary mode, by recognising disgust from relax and good sensation from relax, yielding an accuracy greater than 85%.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11697/140407
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