Adoption of Machine Learning Techniques to Enhance Classification Performance in Reactive Brain-Computer Interfaces-Reference-Cited by-同舟云学术

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Adoption of Machine Learning Techniques to Enhance Classification Performance in Reactive Brain-Computer Interfaces

Published:2022-06-22 Issue: Volume: Page:
ISSN:
Container-title:2022 IEEE International Symposium on Medical Measurements and Applications (MeMeA)
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Author:

Apicella Andrea¹,Arpaia Pasquale²,Cataldo Andrea³,De Benedetto Egidio¹,Donato Nicola⁴,Duraccio Luigi⁵,Giugliano Salvatore¹,Prevete Roberto¹

Affiliation:

1. University of Naples Federico II,Department of Information Technology and Electrical Engineering,Naples,Italy

2. University of Naples Federico II,Interdepartmental Research Center in Health Management and Innovation in Healthcare,Naples,Italy

3. University of Salento,Department of Engineering for Innovation,Lecce,Italy

4. University of Messina,Department of Engineering,Messina,Italy

5. Polytechnic University of Turin,Department of Electronics and Telecommunications,Turin,Italy

Funder

University of Naples Federico II, Italy

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9855895/9856402/09856441.pdf?arnumber=9856441

Reference26 articles.

1. High Sensitive and Selective Minisensor for Acetone Monitoring

2. Steady-state visual evoked potential (SSVEP)-based communication: impact of harmonic frequency components

3. A Dynamically Optimized SSVEP Brain–Computer Interface (BCI) Speller

4. A Wearable EEG Instrument for Real-Time Frontal Asymmetry Monitoring in Worker Stress Analysis

5. Highly wearable SSVEP-based BCI: Performance comparison of augmented reality solutions for the flickering stimuli rendering

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