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

• Published in: Frontiers in neuroscience Vol. 10; p. 430
• Main Authors: Waytowich, Nicholas R., Lawhern, Vernon J., Bohannon, Addison W., Ball, Kenneth R., Lance, Brent J.
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Research Foundation 22.09.2016; Frontiers Media S.A
• Subjects: 60 APPLIED LIFE SCIENCES; Adaptive Learning; Artificial intelligence; Brain; Brain research; brain-computer interface (BCI); Calibration; calibration-free BCI; Classification; Computer applications; Computer science; EEG/ERP; ensemble learning; Event-related potentials; event-related potentials (ERPs); Geometry; Implants; information geometry; Laboratories; Learning algorithms; Machine learning; MATHEMATICS AND COMPUTING; Neuroscience; P300; Paradigms; Pattern recognition; rapid serial visual presentation; RSVP; Signal processing; Transfer learning; unsupervised learning; Visualization; United States > US; ensemble learning; calibration-free BCI; RSVP; P300; unsupervised learning
• ISSN: 1662-453X; 1662-4548; 1662-453X
• DOI: 10.3389/fnins.2016.00430

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 outperform traditional within-subject calibration techniques when limited data is available. 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.