Sinc-Windowing and Multiple Correlation Coefficients Improve SSVEP Recognition Based on Canonical Correlation Analysis

Autor: Valeria Mondini, Anna Lisa Mangia, Angelo Cappello, Luca Talevi
Přispěvatelé: Valeria Mondini, Anna Lisa Mangia, Luca Talevi, Angelo Cappello
Jazyk: angličtina
Rok vydání: 2018
Předmět:
Adult
Male
Article Subject
General Computer Science
Computer science
General Mathematics
0206 medical engineering
02 engineering and technology
Variation (game tree)
Translation (geometry)
lcsh:Computer applications to medicine. Medical informatics
Field (computer science)
Pattern Recognition
Automated
lcsh:RC321-571
Young Adult
03 medical and health sciences
0302 clinical medicine
Simple (abstract algebra)
Humans
Multiple correlation
Brain-Computer Interface
BCI
Canonical Correlation Analysis
CCA
Electroencephalography
EEG
Steady-state visually evoked potential
SSVEP
sinc-windowing
lcsh:Neurosciences. Biological psychiatry. Neuropsychiatry
Sinc function
business.industry
General Neuroscience
Brain
Electroencephalography
Signal Processing
Computer-Assisted
Pattern recognition
General Medicine
Modular design
020601 biomedical engineering
Visual Perception
Evoked Potentials
Visual
lcsh:R858-859.7
Female
Artificial intelligence
Canonical correlation
business
Algorithms
030217 neurology & neurosurgery
Research Article
Zdroj: Computational Intelligence and Neuroscience, Vol 2018 (2018)
Computational Intelligence and Neuroscience
ISSN: 1687-5273
1687-5265
Popis: Canonical Correlation Analysis (CCA) is an increasingly used approach in the field of Steady-State Visually Evoked Potential (SSVEP) recognition. The efficacy of the method has been widely proven, and several variations have been proposed. However, most CCA variations tend to complicate the method, usually requiring additional user training or increasing computational load. Taking simple procedures and low computational costs may be, however, a relevant aspect, especially in view of low-cost and high-portability devices. In addition, it would be desirable that the proposed variations are as general and modular as possible to facilitate the translation of results to different algorithms and setups. In this work, we evaluated the impact of two simple, modular variations of the classical CCA method. The variations involved (i) the number of canonical correlations used for classification and (ii) the inclusion of a prefiltering step by means of sinc-windowing. We tested ten volunteers in a 4-class SSVEP setup. Both variations significantly improved classification accuracy when they were used separately or in conjunction and led to accuracy increments up to 7-8% on average and peak of 25–30%. Additionally, variations had no (variation (i)) or minimal (variation (ii)) impact on the number of algorithm steps required for each classification. Given the modular nature of the proposed variations and their positive impact on classification accuracy, they might be easily included in the design of CCA-based algorithms that are even different from ours.
Databáze: OpenAIRE
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