This paper presents a P300-based Brain Computer Interface for the environmental control in ambient assisted living (AAL) context. The system exploits a machine-learning algorithm based on spatio-temporal characterization of the P300, which realizes the binary discrimination from a multiclass classification. The BCI architecture is made up by (i) the acquisition unit, (ii) the processing unit and (iii) the home management. The acquisition unit is realized by a wireless EEG headset collecting data from 6 electrodes. The processing unit is a hybrid PC-Raspberry Pi architecture that: (i) provides stimulation, operates a Machine Learning (ML) and (iii) realizes a real-time multidimensional classification. The ML stage is based on a custom algorithm (t-RIDE) which emphasizes the subjective P300 characteristics and trains the classifiers. The extracted features are functionally reduced and, thus, used to define proper prediction boundaries. The Raspberry-based core classifies and actuates the received commands, interfacing the environment through a dedicated module that manages the home via TCP/IP. The experimental results on a dataset of 7 subjects enable a classification with an accuracy of 84.28% ± 0.87 % in less than 10ms.

User-centered Ambient Assisted Living: Brain Environment Interface / De Venuto, Daniela; Mezzina, Giovanni. - ELETTRONICO. - (2018), pp. 527-530. (Intervento presentato al convegno 7th Mediterranean Conference on Embedded Computing, MECO'2018 tenutosi a Budva, Montenegro nel June 10-14, 2018) [10.1109/MECO.2018.8405984].

User-centered Ambient Assisted Living: Brain Environment Interface

De Venuto, Daniela
;
Mezzina, Giovanni
2018-01-01

Abstract

This paper presents a P300-based Brain Computer Interface for the environmental control in ambient assisted living (AAL) context. The system exploits a machine-learning algorithm based on spatio-temporal characterization of the P300, which realizes the binary discrimination from a multiclass classification. The BCI architecture is made up by (i) the acquisition unit, (ii) the processing unit and (iii) the home management. The acquisition unit is realized by a wireless EEG headset collecting data from 6 electrodes. The processing unit is a hybrid PC-Raspberry Pi architecture that: (i) provides stimulation, operates a Machine Learning (ML) and (iii) realizes a real-time multidimensional classification. The ML stage is based on a custom algorithm (t-RIDE) which emphasizes the subjective P300 characteristics and trains the classifiers. The extracted features are functionally reduced and, thus, used to define proper prediction boundaries. The Raspberry-based core classifies and actuates the received commands, interfacing the environment through a dedicated module that manages the home via TCP/IP. The experimental results on a dataset of 7 subjects enable a classification with an accuracy of 84.28% ± 0.87 % in less than 10ms.
2018
7th Mediterranean Conference on Embedded Computing, MECO'2018
978-1-5386-5683-9
User-centered Ambient Assisted Living: Brain Environment Interface / De Venuto, Daniela; Mezzina, Giovanni. - ELETTRONICO. - (2018), pp. 527-530. (Intervento presentato al convegno 7th Mediterranean Conference on Embedded Computing, MECO'2018 tenutosi a Budva, Montenegro nel June 10-14, 2018) [10.1109/MECO.2018.8405984].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11589/127610
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