Rough set-based classification of EEG signals related to real and imagery motion
作者： Piotr Szczuko
关键词: Pattern recognition 、 Feature extraction 、 Computer science 、 Artificial intelligence 、 Electroencephalography 、 Rough set 、 Computer vision 、 Classifier (UML)
摘要: A rough set-based approach to classification of EEG signals registered while subjects were performing real and imagery motions is presented in the paper. The appropriate subset channels selected, recordings are segmented, features extracted, based on time-frequency decomposition signal. Rough set classifier trained several scenarios, comparing accuracy for motion. Results commented further research proposed.
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