A viable fully on-line adaptive brain computer interface (BCI) is introduced. On-line experiments with nine naive and able-bodied subjects were carried out using a continuously adaptive BCI system. The data were analyzed and the viability of the system was studied. The BCI was based on motor imagery, the feature extraction was performed with an adaptive autoregressive model and the classifier used was an adaptive quadratic discriminant analysis. The classifier was on-line updated by an adaptive estimation of the information matrix (ADIM). The system was also able to provide continuous feedback to the subject. The success of the feedback was studied analyzing the error rate and mutual information of each session and this analysis showed a clear improvement of the subject's control of the BCI from session to session.

The design of robust and high-performance gaze-tracking systems is one of the most important objectives of the eye-tracking community. In general, a subject calibration procedure is needed to learn system parameters and be able to estimate the gaze direction accurately. In this paper, we attempt to determine if subject calibration can be eliminated. A geometric analysis of a gaze-tracking system is conducted to determine user calibration requirements. The eye model used considers the offset between optical and visual axes, the refraction of the cornea, and Donder's law. This paper demonstrates the minimal number of cameras, light sources, and user calibration points needed to solve for gaze estimation. The underlying geometric model is based on glint positions and pupil ellipse in the image, and the minimal hardware needed for this model is one camera and multiple light-emitting diodes. This paper proves that subject calibration is compulsory for correct gaze estimation and proposes a model based on a single point for subject calibration. The experiments carried out show that, although two glints and one calibration point are sufficient to perform gaze estimation (error ~ 1deg), using more light sources and calibration points can result in lower average errors.

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

Recent local stereo matching algorithms based on an adaptive-weight strategy achieve accuracy similar to global approaches. One of the major problems of these algorithms is that they are computationally expensive and this complexity increases proportionally to the window size. This paper proposes a novel cost aggregation step with complexity independent of the window size (i.e. O(1)) that outperforms state-of-the-art O(1) methods. Moreover, compared to other O(1) approaches, our method does not rely on integral histograms enabling aggregation using colour images instead of grayscale ones. Finally, to improve the results of the proposed algorithm a disparity refinement pipeline is also proposed. The overall algorithm produces results comparable to those of state-of-the-art stereo matching algorithms.