published_or_final_version / abstract / toc / Computer Science and Information Systems / Master / Master of Philosophy

published_or_final_version / Computer Science / Master / Master of Philosophy

published_or_final_version / Chinese / Master / Master of Philosophy

No description available.

Self-paced Brain computer interface (SBCI) systems allow individuals with motor disabilities to use their brain signals to control devices, whenever they wish. These systems are required to identify the user’s “intentional control (IC)” commands and they must remain inactive during all periods in which users do not intend control (called “no control (NC)” periods). This dissertation addresses three issues related to the design of SBCI systems: 1) their presently high false positive (FP) rates, 2) the presence of artifacts and 3) the identification of a suitable evaluation metric. To improve the performance of SBCI systems, the following are proposed: 1) a method for the automatic user-customization of a 2-state SBCI system, 2) a two-stage feature reduction method for selecting wavelet coefficients extracted from movement-related potentials (MRP), 3) an SBCI system that classifies features extracted from three neurological phenomena: MRPs, changes in the power of the Mu and Beta rhythms; 4) a novel method that effectively combines methods developed in 2) and 3 ) and 5) generalizing the system developed in 3) for detecting a right index finger flexion to detecting the right hand extension. Results of these studies using actual movements show an average true positive (TP) rate of 56.2% at the FP rate of 0.14% for the finger flexion study and an average TP rate of 33.4% at the FP rate of 0.12% for the hand extension study. These FP results are significantly lower than those achieved in other SBCI systems, where FP rates vary between 1-10%. We also conduct a comprehensive survey of the BCI literature. We demonstrate that many BCI papers do not properly deal with artifacts. We show that the proposed BCI achieves a good performance of TP=51.8% and FP=0.4% in the presence of eye movement artifacts. Further tests of the performance of the proposed system in a pseudo-online environment, shows an average TP rate =48.8% at the FP rate of 0.8%. Finally, we propose a framework for choosing a suitable evaluation metric for SBCI systems. This framework shows that Kappa coefficient is more suitable than other metrics in evaluating the performance during the model selection procedure.

The time required to identify a common object depends on several factors, especially pre-existing knowledge and episodic representations newly established as a result of a prior study. My research examined how these factors contribute to identification of objects (both studied and non-studied) and to performance on explicit memory tests. The overall goal was to explore the link between memory and object perception. One series of experiments examined influences due to object orientation in the plane of the page. Subjects were shown color photos of objects, and memory was assessed either with an old/new recognition test or with a test that required them to identify objects that were slowly faded in on a computer monitor. The critical variables were the type of photo — each showing either an object with a predominant or cardinal orientation (e.g., helicopter) or a non-cardinal object (e.g., pencil), and the orientation at which the photos were displayed at study and at test (e.g., rotated 0°, 120°, or 240°). For non-studied targets, identification test performance showed a large effect due to display orientation, but only for cardinal objects. For studied targets, study-to-test changes in orientation influenced priming for both non-cardinal and cardinal objects, but orientation specific priming effects (larger priming when study and test orientations matched rather than mismatched) were much larger with cardinal than non-cardinal objects, especially, when their display orientation, at test was unusual (i.e., 120°, 240°). A second series of experiments examined influences due to object size (size of an object presented alone) and relative size (size of an object relative to another object). Size manipulations had a large effect on identification of non-studied objects but study-to- test changes in size had only a minimal effect on priming. In contrast, study1to-test changes in relative size influenced recognition decision speed which is an index of priming. The combined findings suggest that both semantic and episodic representations behave as if they coded orientation but only for cardinal objects. They also suggest that episodic representations code relative size but not size information. The findings are explained by the instance views of memory.

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In this thesis it is shown that several pattern recognition problems can be solved efficiently by exploiting the geometrical structure of the problems. The problems considered are in the area of clustering and classification. These problems are: (i) computing the diameter of a finite planar set, (ii) computing the maximum and minimum distance between two finite planar sets of points, (iii) testing for point inclusion in a convex polyhedron in d-dimensional space, and (iv) exact and inexact reference set thinning for the nearest neighbor decision rule. / Algorithms to solve the above problems are presented and analyzed for worst-case and average-case situations. These algorithms are implemented and experimentally compared with the existing algorithms. / In solving the above problems, a geometrical construct, known as the Voronoi diagram is used extensively. However, there exists no practical algorithm to construct the Voronoi diagram in d dimensional spaces when d > 2. In this thesis an efficient algorithm to construct the Voronoi diagram in d-space is presented.

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No description available.