This work addresses an innovative processing strategy to improve the classification of Steady-State Visually Evoked Potentials (SSVEPs). This strategy resorts to the combined use of fast Fourier transform and Canonical Correlation Analysis in time domain, and manages to outperform by over 5% previous results obtained for highly wearable, single-channel Brain-Computer Interfaces. In fact, a classification accuracy of 90% is reached with only 2-s time response. Then, the proposed algorithm is employed for an experimental characterization of three different Augmented Reality (AR) devices (namely, Microsoft Hololens I, Epson Moverio BT-350, and Oculus Rift S). These devices are used to generate the flickering stimuli necessary to the SSVEP induction. Also, in the three pieces of instrumentation under test, the number of simultaneous visual stimuli was increased with respect to the state-of-art solutions. The aim of the experimental characterization was to evaluate the influence of different AR technologies on the elicitation of user’s SSVEPs. Classification accuracy, time response, and information transfer rate were used as figures of merit on nine volunteers for each piece of instrumentation. Experimental results show that choosing an adequate AR headset is crucial for obtaining satisfying performance: in fact, it can be observed that the classification accuracy obtained with Microsoft Hololens is about 20% greater than Epson Moverio one.

Performance enhancement of wearable instrumentation for AR-based SSVEP BCI / Arpaia, Pasquale; De Benedetto, Egidio; De Paolis, Lucio; D’Errico, Giovanni; Donato, Nicola; Duraccio, Luigi. - In: MEASUREMENT. - ISSN 0263-2241. - 196:(2022), p. 111188. [10.1016/j.measurement.2022.111188]

Performance enhancement of wearable instrumentation for AR-based SSVEP BCI

Arpaia, Pasquale
;
De Benedetto, Egidio;
2022

Abstract

This work addresses an innovative processing strategy to improve the classification of Steady-State Visually Evoked Potentials (SSVEPs). This strategy resorts to the combined use of fast Fourier transform and Canonical Correlation Analysis in time domain, and manages to outperform by over 5% previous results obtained for highly wearable, single-channel Brain-Computer Interfaces. In fact, a classification accuracy of 90% is reached with only 2-s time response. Then, the proposed algorithm is employed for an experimental characterization of three different Augmented Reality (AR) devices (namely, Microsoft Hololens I, Epson Moverio BT-350, and Oculus Rift S). These devices are used to generate the flickering stimuli necessary to the SSVEP induction. Also, in the three pieces of instrumentation under test, the number of simultaneous visual stimuli was increased with respect to the state-of-art solutions. The aim of the experimental characterization was to evaluate the influence of different AR technologies on the elicitation of user’s SSVEPs. Classification accuracy, time response, and information transfer rate were used as figures of merit on nine volunteers for each piece of instrumentation. Experimental results show that choosing an adequate AR headset is crucial for obtaining satisfying performance: in fact, it can be observed that the classification accuracy obtained with Microsoft Hololens is about 20% greater than Epson Moverio one.
2022
Performance enhancement of wearable instrumentation for AR-based SSVEP BCI / Arpaia, Pasquale; De Benedetto, Egidio; De Paolis, Lucio; D’Errico, Giovanni; Donato, Nicola; Duraccio, Luigi. - In: MEASUREMENT. - ISSN 0263-2241. - 196:(2022), p. 111188. [10.1016/j.measurement.2022.111188]
File in questo prodotto:
File Dimensione Formato  
Performance enhancement of wearable instrumentation for AR-based SSVEP.pdf

solo utenti autorizzati

Descrizione: Paper
Tipologia: Versione Editoriale (PDF)
Licenza: Copyright dell'editore
Dimensione 2.64 MB
Formato Adobe PDF
2.64 MB Adobe PDF   Visualizza/Apri   Richiedi una copia

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11588/890456
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 10
  • ???jsp.display-item.citation.isi??? 8
social impact