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

Sybeldon, Matthew and Schmit, Lukas and Akcakaya, Murat (2017) Transfer Learning for SSVEP Electroencephalography Based Brain–Computer Interfaces Using Learn++.NSE and Mutual Information. Entropy, 19 (1). p. 41. ISSN 1099-4300

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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.


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Details

Item Type: Article
Status: Published
Creators/Authors:
CreatorsEmailPitt UsernameORCID
Sybeldon, Matthew
Schmit, Lukas
Akcakaya, Muratakcakaya@pitt.edu
Date: 19 January 2017
Date Type: Publication
Journal or Publication Title: Entropy
Volume: 19
Number: 1
Publisher: MDPI AG
Page Range: p. 41
DOI or Unique Handle: 10.3390/e19010041
Schools and Programs: Swanson School of Engineering > Electrical and Computer Engineering
Refereed: Yes
ISSN: 1099-4300
Official URL: http://dx.doi.org/10.3390/e19010041
Article Type: Research Article
Date Deposited: 08 Jun 2020 13:08
Last Modified: 08 Jun 2020 13:08
URI: http://d-scholarship.pitt.edu/id/eprint/39143

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