Spectral Transfer Learning Using Information Geometry for a User-Independent Brain-Computer Interface

Waytowich, Nicholas R.; Lawhern, Vernon J.; Bohannon, Addison W.; Ball, Kenneth R.; Lance, Brent J.

doi:10.3389/fnins.2016.00430

Title: Spectral Transfer Learning Using Information Geometry for a User-Independent Brain-Computer Interface

Journal Article · Thu Sep 22 04:00:00 UTC 2016 · Frontiers in Neuroscience (Online)

DOI: https://doi.org/10.3389/fnins.2016.00430 · OSTI ID:1378936

Waytowich, Nicholas R. ^[1]; Lawhern, Vernon J. ^[2]; Bohannon, Addison W. ^[3]; Ball, Kenneth R. ^[4]; Lance, Brent J. ^[4]

U.S. Army Research Lab., Aberdeen Proving Ground, MD (United States); Columbia Univ., New York, NY (United States)
U.S. Army Research Lab., Aberdeen Proving Ground, MD (United States); Univ. of Texas, San Antonio, TX (United States)
U.S. Army Research Lab., Aberdeen Proving Ground, MD (United States); Univ. of Maryland, College Park, MD (United States)
U.S. Army Research Lab., Aberdeen Proving Ground, MD (United States)

Recent advances in signal processing and machine learning techniques have enabled the application of Brain-Computer Interface (BCI) technologies to fields such as medicine, industry, and recreation; however, BCIs still suffer from the requirement of frequent calibration sessions due to the intra- and inter-individual variability of brain-signals, which makes calibration suppression through transfer learning an area of increasing interest for the development of practical BCI systems. In this paper, we present an unsupervised transfer method (spectral transfer using information geometry,STIG),which ranks and combines unlabeled predictions from an ensemble of information geometry classifiers built on data from individual training subjects. The STIG method is validated in both off-line and real-time feedback analysis during a rapid serial visual presentation task (RSVP). For detection of single-trial, event-related potentials (ERPs), the proposed method can significantly outperform existing calibration-free techniques as well as out perform traditional within-subject calibration techniques when limited data is available. Here, this method demonstrates that unsupervised transfer learning for single-trial detection in ERP-based BCIs can be achieved without the requirement of costly training data, representing a step-forward in the overall goal of achieving a practical user-independent BCI system.

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Research Organization:: Columbia Univ., New York, NY (United States)

Sponsoring Organization:: USDOE

OSTI ID:: 1378936

Journal Information:: Frontiers in Neuroscience (Online), Journal Name: Frontiers in Neuroscience (Online) Vol. 10; ISSN 1662-453X

Publisher:: Frontiers Research FoundationCopyright Statement

Country of Publication:: United States

Language:: English

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Comparison of designs towards a subject-independent brain-computer interface based on motor imagery Lotte, Fabian; Guan, Cuntai; Ang, Kai Keng 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society https://doi.org/10.1109/IEMBS.2009.5334126	conference	September 2009
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Multiclass Brain–Computer Interface Classification by Riemannian Geometry Barachant, A.; Bonnet, S.; Congedo, M. IEEE Transactions on Biomedical Engineering, Vol. 59, Issue 4 https://doi.org/10.1109/TBME.2011.2172210	journal	April 2012
Kernel Methods on Riemannian Manifolds with Gaussian RBF Kernels Jayasumana, Sadeep; Hartley, Richard; Salzmann, Mathieu IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 37, Issue 12 https://doi.org/10.1109/TPAMI.2015.2414422	journal	December 2015
A Stable and Efficient Algorithm for the Rank-One Modification of the Symmetric Eigenproblem Gu, Ming; Eisenstat, Stanley C. SIAM Journal on Matrix Analysis and Applications, Vol. 15, Issue 4 https://doi.org/10.1137/S089547989223924X	journal	October 1994
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The effect of target and non-target similarity on neural classification performance: a boost from confidence Marathe, Amar R.; Ries, Anthony J.; Lawhern, Vernon J. Frontiers in Neuroscience, Vol. 9 https://doi.org/10.3389/fnins.2015.00270	journal	August 2015

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A review of classification algorithms for EEG-based brain–computer interfaces: a 10 year update Lotte, F.; Bougrain, L.; Cichocki, A. Journal of Neural Engineering, Vol. 15, Issue 3 https://doi.org/10.1088/1741-2552/aab2f2	journal	April 2018
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Applying Transfer Learning To Deep Learned Models For EEG Analysis Uran, Axel; van Gemeren, Coert; van Diepen, Rosanne arXiv https://doi.org/10.48550/arxiv.1907.01332	preprint	January 2019
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