398 research outputs found

    Removing speech artifacts from electroencephalographic recordings during overt picture naming

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    A number of electroencephalography (EEG) studies have investigated the time course of brain activation during overt word production. The interpretation of their results is complicated by the fact that articulatory movements may mask the cognitive components of interest. The first aim of the present study was to investigate when speech artifacts occur during word production planning and what effects they have on the spatio-temporal neural activation pattern. The second aim was to propose a new method that strongly attenuates speech artifacts during overt picture naming and to compare it with existing methods. EEG and surface electromyograms (EMGs) of the lips were recorded while participants overtly named pictures in a picture-word interference paradigm. The comparison of the raw data with lip EMG and the comparison of source localizations of raw and corrected EEG data showed that speech artifacts occurred mainly from ~. 400. ms post-stimulus onset, but some earlier artifacts mean that they occur much earlier than hitherto assumed. We compared previously used methods of speech artifacts removal (SAR) with a new method, which is based on Independent Component Analysis (SAR-ICA). Our new method clearly outperformed other methods. In contrast to other methods, there was only a weak correlation between the lip EMG and the corrected data by SAR-ICA. Also, only the data corrected with our method showed activation of cerebral sources consistent with meta-analyses of word production

    Cortico-ocular coupling in the service of episodic memory formation

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    UAV object tracking by correlation filter with adaptive appearance model

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    With the increasing availability of low-cost, commercially available unmanned aerial vehicles (UAVs), visual tracking using UAVs has become more and more important due to its many new applications, including automatic navigation, obstacle avoidance, traffic monitoring, search and rescue, etc. However, real-world aerial tracking poses many challenges due to platform motion and image instability, such as aspect ratio change, viewpoint change, fast motion, scale variation and so on. In this paper, an efficient object tracking method for UAV videos is proposed to tackle these challenges. We construct the fused features to capture the gradient information and color characteristics simultaneously. Furthermore, cellular automata is introduced to update the appearance template of target accurately and sparsely. In particular, a high confidence model updating strategy is developed according to the stability function. Systematic comparative evaluations performed on the popular UAV123 dataset show the efficiency of the proposed approach
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