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Full-Text Articles in Databases and Information Systems
High Performance P300 Speller For Brain-Computer Interface, Cuntai Guan, Manoj Thulasidas, Jiankang Wu
High Performance P300 Speller For Brain-Computer Interface, Cuntai Guan, Manoj Thulasidas, Jiankang Wu
Research Collection School Of Computing and Information Systems
P300 speller is a communication tool with which one can input texts or commands to a computer by thought. The amplitude of the P300 evoked potential is inversely proportional to the probability of infrequent or task-related stimulus. In existing P300 spellers, rows and columns of a matrix are intensified successively and randomly, resulting in a stimulus frequency of 1/N (N is the number of rows or columns of the matrix). We propose a new paradigm to display each single character randomly and individually (therefore reducing the stimulus frequency to 1/(N*N)). On-line experiments showed that this new speller significantly improved the …
Clip-Based Similarity Measure For Hierarchical Video Retrieval, Yuxin Peng, Chong-Wah Ngo
Clip-Based Similarity Measure For Hierarchical Video Retrieval, Yuxin Peng, Chong-Wah Ngo
Research Collection School Of Computing and Information Systems
This paper proposes a new approach and algorithm for the similarity measure of video clips. The similarity is mainly based on two bipartite graph matching algorithms: maximum matching (MM) and optimal matching (OM). MM is able to rapidly filter irrelevant video clips, while OM is capable of ranking the similarity of clips according to the visual and granularity factors. Based on MM and OM, a hierarchical video retrieval framework is constructed for the approximate matching of video clips. To allow the matching between a query and a long video, an online clip segmentation algorithm is also proposed to rapidly locate …
Robust Classification Of Event-Related Potential For Brain-Computer Interface, Manoj Thulasidas
Robust Classification Of Event-Related Potential For Brain-Computer Interface, Manoj Thulasidas
Research Collection School Of Computing and Information Systems
We report the implementation of a text input application (speller) based on the P300 event related potential. We obtain high accuracies by using an SVM classifier and a novel feature. These techniques enable us to maintain fast performance without sacrificing the accuracy, thus making the speller usable in an online mode. In order to further improve the usability, we perform various studies on the data with a view to minimizing the training time required. We present data collected from nine healthy subjects, along with the high accuracies (of the order of 95% or more) measured online. We show that the …
Gesture Tracking And Recognition For Lecture Video Editing, Feng Wang, Chong-Wah Ngo, Ting-Chuen Pong
Gesture Tracking And Recognition For Lecture Video Editing, Feng Wang, Chong-Wah Ngo, Ting-Chuen Pong
Research Collection School Of Computing and Information Systems
This paper presents a gesture based driven approach for video editing. Given a lecture video, we adopt novel approaches to automatically detect and synchronize its content with electronic slides. The gestures in each synchronized topic (or shot) are then tracked and recognized continuously. By registering shots and slides and recovering their transformation, the regions where the gestures take place can be known. Based on the recognized gestures and their registered positions, the information in slides can be seamlessly extracted, not only to assist video editing, but also to enhance the quality of original lecture video.
High Accuracy Classification Of Eeg Signal, Wenjie Xu, Cuitai Guan, Chng Eng Siong, S. Ranganatha, Manoj Thulasidas, Jiankang Wu
High Accuracy Classification Of Eeg Signal, Wenjie Xu, Cuitai Guan, Chng Eng Siong, S. Ranganatha, Manoj Thulasidas, Jiankang Wu
Research Collection School Of Computing and Information Systems
Improving classification accuracy is a key issue to advancing brain computer interface (BCI) research from laboratory to real world applications. This article presents a high accuracy EEC signal classification method using single trial EEC signal to detect left and right finger movement. We apply an optimal temporal filter to remove irrelevant signal and subsequently extract key features from spatial patterns of EEG signal to perform classification. Specifically, the proposed method transforms the original EEG signal into a spatial pattern and applies the RBF feature selection method to generate robust feature. Classification is performed by the SVM and our experimental result …