Jin-an Guan

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A new machine learning method referred to as F-score_ELM was proposed to classify the lying and truth-telling using the electroencephalogram (EEG) signals from 28 guilty and innocent subjects. Thirty-one features were extracted from the probe responses from these subjects. Then, a recently-developed classifier called extreme learning machine (ELM) was(More)
We constructed a Brain-computer interface-based mental speller which realizes user-computer interaction. The feature signals of user's intention are embedded in spontaneous EEG background. Single-trial feature estimation should be used on this online occasion instead of the grand average usually used in cognitive or clinical experiments. To demonstrate this(More)
Using Imitating-Natural-Reading Induced Potentials as communication carriers, we are constructing a Brain-computer interface based mental speller which enable users to interaction with computers. The potentials were induced in this way: In a trial, strings consisted of target and non-target symbols were moving smoothly from right to left through a little(More)
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