Dik Kin Wong

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While magnetoencephalography (MEG) is widely used to identify spatial locations of brain activations associated with various tasks, classification of single trials in stimulus-locked experiments remains an open subject. Very significant single-trial classification results have been published using electroencephalogram (EEG) data, but in the MEG case, the(More)
The idea of a hierarchical structure of language constituents of phonemes, syllables, words, and sentences is robust and widely accepted. Empirical similarity differences at every level of this hierarchy have been analyzed in the form of confusion matrices for many years. By normalizing such data so that differences are represented by conditional(More)
In brain-imaging research, we are often interested in making quantitative claims about effects across subjects. Given that most imaging data consist of tens to thousands of spatially correlated time series, inter-subject comparisons are typically accomplished with simple combinations of inter-subject data, for example methods relying on group means.(More)
We have previously shown that classification of single-trial electroencephalographic (EEG) recordings is improved by the use of either a niultichannel classifier or the best independent component over a single channel classifier. In this paper, we introduce a classifier that makes explicit use of multiple independent components. Two models are compared. The(More)
The need to classify text documents within topic hierarchies has given rise to techniques that use the hierarchical structure to improve classification performance. We propose two methods, each utilizing information in a confusion matrix, which apply hierarchical concepts to problems where no a priori hierarchy exists. One method involves learning a(More)
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