This work details the latest advancements on a single-channel, reactive Brain-Computer Interfaces developed at the Interdepartmental Research Center in Health Management and Innovation in Healthcare (CIRMIS) of the University of Naples Federico II. The proposed instrumentation is based on Extended Reality (XR) and exploits the acquisition and classification of the Steady-State Visually Evoked Potentials (SSVEPs). In particular, an XR headset is employed for generating the flickering stimuli necessary to the SSVEP elicitation. The users brain signals are captured by means of a highly wearable and portable electroencephalografic acquisition unit, which is connected to a portable processing unit in charge of processing in real time the incoming data. In this way, a deeper interaction between users and external devices with respect to traditional architectures is guaranteed. The classification capability of the proposed instrument has been significantly improved over the years. Currently, in fact, a classification accuracy up to 90 % is obtained with at least 2 s of acquisition time.

Latest Advancements in SSVEPs Classification for Single-Channel, Extended Reality-based Brain-Computer Interfaces / Angrisani, L.; Arpaia, P.; De Benedetto, E.; Donato, N.; Duraccio, L.. - ELETTRONICO. - (2022), pp. 166-170. (Intervento presentato al convegno 25th IMEKO TC-4 International Symposium on Measurement of Electrical Quantities, IMEKO TC-4 2022 and 23rd International Workshop on ADC and DAC Modelling and Testing, IWADC 2022 tenutosi a Brescia, Italy nel September 12-14, 2022).

Latest Advancements in SSVEPs Classification for Single-Channel, Extended Reality-based Brain-Computer Interfaces

Donato N.;Duraccio L.
2022

Abstract

This work details the latest advancements on a single-channel, reactive Brain-Computer Interfaces developed at the Interdepartmental Research Center in Health Management and Innovation in Healthcare (CIRMIS) of the University of Naples Federico II. The proposed instrumentation is based on Extended Reality (XR) and exploits the acquisition and classification of the Steady-State Visually Evoked Potentials (SSVEPs). In particular, an XR headset is employed for generating the flickering stimuli necessary to the SSVEP elicitation. The users brain signals are captured by means of a highly wearable and portable electroencephalografic acquisition unit, which is connected to a portable processing unit in charge of processing in real time the incoming data. In this way, a deeper interaction between users and external devices with respect to traditional architectures is guaranteed. The classification capability of the proposed instrument has been significantly improved over the years. Currently, in fact, a classification accuracy up to 90 % is obtained with at least 2 s of acquisition time.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2975543