Study of On-Line Adaptive Discriminant Analysis for EEG-Based Brain Computer Interfaces

Abstract

A study of different on-line adaptive classifiers, using various feature types is presented. Motor imagery brain computer interface (BCI) experiments were carried out with 18 naive able-bodied subjects. Experiments were done with three two-class, cue-based, electroencephalogram (EEG)-based systems. Two continuously adaptive classifiers were tested: adaptive quadratic and linear discriminant analysis. Three feature types were analyzed, adaptive autoregressive parameters, logarithmic band power estimates and the concatenation of both. Results show that all systems are stable and that the concatenation of features with continuously adaptive linear discriminant analysis classifier is the best choice of all. Also, a comparison of the latter with a discontinuously updated linear discriminant analysis, carried out in on-line experiments with six subjects, showed that on-line adaptation performed significantly better than a discontinuous update. Finally a static subject-specific baseline was also provided and used to compare performance measurements of both types of adaptation.

Psychological state estimation from physiological recordings during robot-assisted gait rehabilitation

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“…An idea proposed by Vidaurre et al suggests use of a classifier in combination with a Kalman filter to train the classifier at run time [45]. The classifier, such as a neural network, could be trained on data from previous experiments and a Kalman filter would adapt the classifier parameters to the patient.…”

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“…They require an a priori choice of the structure and order of the signal generation mechanism model. (Anderson & Sijercic, 1996;Schlogl et al, 1997;Anderson et al, 1998;Roberts & Penny, 2000;Burke et al, 2005;Vidaurre et al, 2007). AR methods assume that a signal X(t), measured at time t, can be modeled as a weighted sum of the values of this signal at previous time steps, to which we can add a noise term E t (generally a Gaussian white noise):…”

Signal Processing for Non-Invasive Brain Biomarkers of Sensorimotor Performance and Brain Monitoring

“…Although helpful, the requirement of such a priori information actually represents a major pitfall for practical BCI applications since the user should decide when and where to direct his/her intentions. In other words, no information of external targets is available to the decoding algorithm (Blankertz et al, 2006;Vidaurre et al, 2007). The complexity of using two adaptive controllers (the user's brain and the decoding algorithm) is not new and has been already raised (McFarland et al, 2006;Vaughan et al, 1996); however, it continues to be an issue, and no satisfying solutions of this problem have been provided (McFarland et al, 2006).…”