Restricted Boltzmann Machines in Sensory Motor Rhythm Brain-Computer Interfacing: A Study on Inter-Subject Transfer and Co-Adaptation

Reinmar Kobler, Reinhold Scherer

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Naive users often perceive calibration of a Sensory Motor Rhythm (SMR) based Brain-Computer Interfaces (BCI) as tedious and lengthy. The lack of feedback during training is assumed to be a major cause. I.e. if one had already a reasonable model to start with, feedback training could be started immediately. One concept to address this issue is learning a general model and adapting it to new observations. In this study we applied this concept by utilizing a generative model entitled Restricted Boltzmann Machine (RBM). We investigated its feature extraction capabilities by fitting a RBM to recordings of 9 subjects. Generalization was assessed in an online coadaptive study, covering 12 volunteers (10 naive). An overall median accuracy of 88.9% (83.5% naive) with a standard-error of 6.5% (6.6% naive) was achieved for a classical hand versus feet motor imagery task. The online co-adaptive training itself lasted approximately 25 minutes. Feedback was already presented after a one minute setup run, whose purpose was to estimate initial statistics and to train an online artifact detection system.
Original languageEnglish
Title of host publicationSystems, Man, and Cybernetics (SMC), 2016 IEEE International Conference on
Pages 000469 - 000474
ISBN (Electronic)978-1-5090-1897-0
DOIs
Publication statusPublished - 2016
Event2016 IEEE International Conference on Systems, Man, and Cybernetics - Budapest, Hungary
Duration: 9 Oct 201612 Oct 2016

Conference

Conference2016 IEEE International Conference on Systems, Man, and Cybernetics
CountryHungary
CityBudapest
Period9/10/1612/10/16

Fields of Expertise

  • Human- & Biotechnology

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