Yufeng Ke

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OBJECTIVE As one of the most popular and extensively studied paradigms of brain-computer interfaces (BCIs), event-related potential-based BCIs (ERP-BCIs) are usually built and tested in ideal laboratory settings in most existing studies, with subjects concentrating on stimuli and intentionally avoiding possible distractors. This study is aimed at examining(More)
Electroencephalographic (EEG) has been believed to be a potential psychophysiological measure of mental workload. There however remain a number of challenges in building a generalized mental workload recognition model, one of which includes the inability of an EEG-based workload classifier trained on a specific task to handle other tasks. The primary goal(More)
In order to test the effectiveness of multi-dimensional N-back task for inducing deeper brain fatigue, we conducted a series of N*L-back experiments: 1*1-back, 1*2-back, 2*1-back and 2*2-back tasks. We analyzed and compared the behavioral results, EEG variations and mutual information among these four different tasks. There was no significant difference in(More)
Mental workload (MW)-based adaptive system has been found to be an effective approach to enhance the performance of human-machine interaction and to avoid human error caused by overload. However, MW estimated from the spontaneously generated electroencephalogram (EEG) was found to be task-specific. In existing studies, EEG-based MW classifier can work well(More)
Varieties of neurophysiological measures have been utilized in visual attention studies. The linear parameters like power spectrum are the most commonly used features in the existing studies. In this paper, however, nonlinear parameters including approximate entropy, sample entropy and multiscale entropy were tested. All subjects were instructed to perform(More)
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