The functional analysis of a novel instrumentation for Brain-Computer Interfaces (BCI) is carried out. This consists of a wireless wearable helmet with only 8 dry electrodes. The brain signals to be measured through an electroencephalography are related to the sensorimotor cortex. The final aim is to distinguish between different motor imagery tasks. Furthermore, this analysis also takes into account the discrimination between two executed movements. Features are extracted from the brain signals by means of a Common Spatial Pattern algorithm. Then, two different classifiers are employed to process the brain signals, namely the Random Forest, and the Support Vector Machine with Gaussian kernel. Their performance was compared in terms of classification accuracy and the best accuracy resulted equal to about 80% when distinguishing between left and right imagined movement, classified by means of the Random Forest. The results of this study aim at giving a contribution to the building of wearable BCIs for daily life applications.

Instrumentation for Motor Imagery-based Brain Computer Interfaces relying on dry electrodes: a functional analysis / Angrisani, Leopoldo; Arpaia, Pasquale; Donnarumma, Francesco; Esposito, Antonio; Frosolone, Mirco; Improta, Giovanni; Moccaldi, Nicola; Natalizio, Angela; Parvis, Marco. - ELETTRONICO. - (2020), pp. 1-6. (Intervento presentato al convegno IEEE International Instrumentation and Measurement Technology Conference - I2MTC tenutosi a Dobrovnik, Croatia nel 25-28 May 2020) [10.1109/I2MTC43012.2020.9129244].

Instrumentation for Motor Imagery-based Brain Computer Interfaces relying on dry electrodes: a functional analysis

Esposito, Antonio;Natalizio, Angela;Parvis, Marco
2020

Abstract

The functional analysis of a novel instrumentation for Brain-Computer Interfaces (BCI) is carried out. This consists of a wireless wearable helmet with only 8 dry electrodes. The brain signals to be measured through an electroencephalography are related to the sensorimotor cortex. The final aim is to distinguish between different motor imagery tasks. Furthermore, this analysis also takes into account the discrimination between two executed movements. Features are extracted from the brain signals by means of a Common Spatial Pattern algorithm. Then, two different classifiers are employed to process the brain signals, namely the Random Forest, and the Support Vector Machine with Gaussian kernel. Their performance was compared in terms of classification accuracy and the best accuracy resulted equal to about 80% when distinguishing between left and right imagined movement, classified by means of the Random Forest. The results of this study aim at giving a contribution to the building of wearable BCIs for daily life applications.
2020
978-1-7281-4460-3
File in questo prodotto:
File Dimensione Formato  
IMTC2020_Esposito.pdf

non disponibili

Tipologia: 2a Post-print versione editoriale / Version of Record
Licenza: Non Pubblico - Accesso privato/ristretto
Dimensione 1.15 MB
Formato Adobe PDF
1.15 MB Adobe PDF   Visualizza/Apri   Richiedi una copia
Pubblicazioni consigliate

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/11583/2841454