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Improving P300 Speller for Brain-Computer Interface by Using the Best Possible Classifier

چهارمین کنفرانس ملی تکنولوژی در مهندسی برق و کامپیوتر
Year: 1398
COI: ETECH04_064
Language: EnglishView: 331
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Majid Ramedani - Faculty of Electrical Engineering Tarbiat Modares University Tehran, Iran
Behzad Farzanegan - Faculty of Electrical Engineering Amirkabir University of Technology Tehran, Iran
Mohammad Bagher Menhaj - Faculty of Electrical Engineering Amirkabir University of Technology Tehran, Iran


In this paper, we discuss Brain-computer computer interface (BCI) and its problem of data transfer. The main object is to examine the classification of EEG signals whichare divided into two target and non-target classes and to improve the speed of BCI communication. Data is collected by the p300 speller, then a preprocessing treatment is performed on the recorded data. Since the EEG signals are low amplitude recording and averaging steps should be acted several times. Therefore, the BCI communication speed is slow. A new channel selection of electroencephalography is presented due to speed of communication. Due to the extraction of the P300 and distinction between the signal with P300 and signal without p300, we use the feature extraction. Then we classify signals using different classifiers such as RBF, SVM and ANN. Comparing analysisshows that SVM gives the best possible result among examining classifier for the BCI communication.


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Ramedani, Majid and Farzanegan, Behzad and Menhaj, Mohammad Bagher,1398,Improving P300 Speller for Brain-Computer Interface by Using the Best Possible Classifier,Fourth National Conference on Electrical and Computer Engineering,Tehran,

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Paper count: 34,254
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