Restricted Boltzmann Machines in Sensory Motor Rhythm Brain Computer Interfacing: A study on inter subject transfer and co adaptation
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 co adaptive 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.
2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC)