Spectral Transfer Learning Using Information Geometry for a User-Independent Brain-Computer Interface
Abstract
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.
- Authors:
-
- 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)
- Publication Date:
- Research Org.:
- Columbia Univ., New York, NY (United States)
- Sponsoring Org.:
- USDOE
- OSTI Identifier:
- 1378936
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Frontiers in Neuroscience (Online)
- Additional Journal Information:
- Journal Name: Frontiers in Neuroscience (Online); Journal Volume: 10; Journal ID: ISSN 1662-453X
- Publisher:
- Frontiers Research Foundation
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 97 MATHEMATICS AND COMPUTING; 60 APPLIED LIFE SCIENCES; unsupervised learning; ensemble learning; calibration-free BCI; P300; RSVP
Citation Formats
Waytowich, Nicholas R., Lawhern, Vernon J., Bohannon, Addison W., Ball, Kenneth R., and Lance, Brent J. Spectral Transfer Learning Using Information Geometry for a User-Independent Brain-Computer Interface. United States: N. p., 2016.
Web. doi:10.3389/fnins.2016.00430.
Waytowich, Nicholas R., Lawhern, Vernon J., Bohannon, Addison W., Ball, Kenneth R., & Lance, Brent J. Spectral Transfer Learning Using Information Geometry for a User-Independent Brain-Computer Interface. United States. https://doi.org/10.3389/fnins.2016.00430
Waytowich, Nicholas R., Lawhern, Vernon J., Bohannon, Addison W., Ball, Kenneth R., and Lance, Brent J. Thu .
"Spectral Transfer Learning Using Information Geometry for a User-Independent Brain-Computer Interface". United States. https://doi.org/10.3389/fnins.2016.00430. https://www.osti.gov/servlets/purl/1378936.
@article{osti_1378936,
title = {Spectral Transfer Learning Using Information Geometry for a User-Independent Brain-Computer Interface},
author = {Waytowich, Nicholas R. and Lawhern, Vernon J. and Bohannon, Addison W. and Ball, Kenneth R. and Lance, Brent J.},
abstractNote = {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.},
doi = {10.3389/fnins.2016.00430},
journal = {Frontiers in Neuroscience (Online)},
number = ,
volume = 10,
place = {United States},
year = {Thu Sep 22 00:00:00 EDT 2016},
month = {Thu Sep 22 00:00:00 EDT 2016}
}
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