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ItemBioplausible multiscale filtering in retinal to cortical processing as a model of computer vision(SCITEPRESS, 2015-01)Visual illusions emerge as an attractive field of research with the discovery over the last century of a variety of deep and mysterious mechanisms of visual information processing in the human visual system. Among many classes of visual illusion relating to shape, brightness, colour and motion, “geometrical illusions” are essentially based on the misperception of orientation, size, and position. The main focus of this paper is on illusions of orientation, sometimes referred to as “tilt illusions”, where parallel lines appear not to be parallel, a straight line is perceived as a curved line, or angles where lines intersect appear larger or smaller. Although some low level and high level explanations have been proposed for geometrical tilt illusions, a systematic explanation based on model predictions of both illusion magnitude and local tilt direction is still an open issue. Here a neurophysiological model is expounded based on Difference of Gaussians implementing a classical receptive field model of retinal processing that predicts tilt illusion effects.
ItemMultiplying the Mileage of Your Dataset with Subwindowing(Springer Berlin Heidelberg, 2011)This study is focused on improving the classification performance of EEG data through the use of some data restructuring methods. In this study, the impact of having more training instances/samples vs. using shorter window sizes is investigated. The BCI2003 IVa dataset is used to examine the results. The results not surprisingly indicate that, up to a certain point, having higher numbers of training instances significantly improves the classification performance while the use of shorter window sizes tends to worsen performance in a way that usually cannot fully be compensated for by the additional instances, but tends to provide useful gain in overall performance for small divisors into two or three subepochs. We have moreover determined that use of an incomplete set of overlapping windows can have little effect, and is inapplicable for the smallest divisors, but that use of overlapping subepochs from three specific non-overlapping areas (start, middle and end) of a superepoch tends to contribute significant additional information. Examination of a division into five equal non-overlapping areas indicates that for some subjects the first or last fifth contributes significantly less information than the middle three fifths.
ItemTowards a brain-controlled Wheelchair Prototype(BCS, 2010)In this project, a design for a non-invasive, EEG-based braincontrolled wheelchair has been developed for use by completely paralyzed patients. The proposed design includes a novel approach for selecting optimal electrode positions, a series of signal processing algorithms and an interface to a powered wheelchair. In addition, a 3D virtual environment has been implemented for training, evaluating and testing the system prior to establishing the wheelchair interface. Simulation of a virtual scenario replicating the real world gives subjects an opportunity to become familiar with operating the device prior to engaging the wheelchair.
ItemMultiplication of EEG Samples through Replicating, Biasing, and Overlapping(Springer Berlin Heidelberg, 2012)EEG recording is a time consuming operation during which the subject is expected to stay still for a long time performing tasks. It is reasonable to expect some uctuation in the level of focus toward the performed task during the task period. This study is focused on investi- gating various approaches for emphasizing regions of interest during the task period. Dividing the task period into three segments of beginning, middle and end, is expectable to improve the overall classi cation per- formance by changing the concentration of the training samples toward regions in which subject had better concentration toward the performed tasks. This issue is investigated through the use of techniques such as i) replication, ii) biasing, and iii) overlapping. A dataset with 4 motor imagery tasks (BCI Competition III dataset IIIa) is used. The results il- lustrate the existing variations within the potential of di erent segments of the task period and the feasibility of techniques that focus the training samples toward such regions.
ItemA computationally and cognitively plausible model of supervised and unsupervised learning(Springer-Verlag, 2013-01-01)The issue of chance correction has been discussed for many decades in the context of statistics, psychology and machine learning, with multiple measures being shown to have desirable properties, including various definitions of Kappa or Correlation, and the psychologically validated ΔP measures. In this paper, we discuss the relationships between these measures, showing that they form part of a single family of measures, and that using an appropriate measure can positively impact learning.