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Entropy 2017, 19(1), 41; doi:10.3390/e19010041

Transfer Learning for SSVEP Electroencephalography Based Brain–Computer Interfaces Using Learn++.NSE and Mutual Information

1
Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA 15213, USA
2
Department of Electrical and Computer Engineering, University of Pittsburgh Cancer Institute Summer Academy, Pittsburgh, PA 15213, USA
*
Author to whom correspondence should be addressed.
Academic Editor: Osvaldo Anibal Rosso
Received: 30 November 2016 / Revised: 29 December 2016 / Accepted: 17 January 2017 / Published: 19 January 2017
(This article belongs to the Special Issue Entropy and Electroencephalography II)
View Full-Text   |   Download PDF [515 KB, uploaded 19 January 2017]   |  

Abstract

Brain–Computer Interfaces (BCI) using Steady-State Visual Evoked Potentials (SSVEP) are sometimes used by injured patients seeking to use a computer. Canonical Correlation Analysis (CCA) is seen as state-of-the-art for SSVEP BCI systems. However, this assumes that the user has full control over their covert attention, which may not be the case. This introduces high calibration requirements when using other machine learning techniques. These may be circumvented by using transfer learning to utilize data from other participants. This paper proposes a combination of ensemble learning via Learn++ for Nonstationary Environments (Learn++.NSE)and similarity measures such as mutual information to identify ensembles of pre-existing data that result in higher classification. Results show that this approach performed worse than CCA in participants with typical SSVEP responses, but outperformed CCA in participants whose SSVEP responses violated CCA assumptions. This indicates that similarity measures and Learn++.NSE can introduce a transfer learning mechanism to bring SSVEP system accessibility to users unable to control their covert attention. View Full-Text
Keywords: BCI; electroencephalography; SSVEP; transfer learning; mutual information; Learn++.NSE; canonical correlation analysis BCI; electroencephalography; SSVEP; transfer learning; mutual information; Learn++.NSE; canonical correlation analysis
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Sybeldon, M.; Schmit, L.; Akcakaya, M. Transfer Learning for SSVEP Electroencephalography Based Brain–Computer Interfaces Using Learn++.NSE and Mutual Information. Entropy 2017, 19, 41.

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