Browsing Australian Research Council (ARC) by Author "Atyabi, Adham"
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ItemMultiplication of EEG Samples through Replicating, Biasing, and Overlapping(Springer Berlin Heidelberg, 2012) Atyabi, Adham; Fitzgibbon, Sean Patrick; Powers, David MartinEEG 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. ItemMultiplying the Mileage of Your Dataset with Subwindowing(Springer Berlin Heidelberg, 2011) Atyabi, Adham; Fitzgibbon, Sean Patrick; Powers, David MartinThis 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. ItemPSO-based dimension reduction of EEG recordings: Implications for subject transfer in BCI(Elsevier, 2013-11) Atyabi, Adham; Luerssen, Martin Holger; Powers, David Martin