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This work is aimed at improving the quality of the classification of EEG signals, and in particular of Steady-State Visual Evoked Potentials (SSVEP), captured ...
This paper presents Magnitude Squared coherence(MSC) technique and Support Vector Machines (SVM) using kernel function for the classification of Inferior ...
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Oct 11, 2020 · This work is aimed at improving the quality of the classification of EEG signals, and in particular of Steady-State Visual Evoked Potentials ( ...
Hence, the goal of the paper is to show the benefits of the SVM application in classifying SSVEPs in single-channel BCI systems with respect to the state-of-the ...
This study aims to develop a Steady State Visual Evoked Potential (SSVEP)-based Brain Computer Interface (BCI) system to control a wheelchair, with improving ...
In this paper, the classification of SSVEP signals with visual stimuli in BCI systems will be done by combining the FoCCA method with several machine learning ...
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The aim of this paper is to improve the performance of signal classification tasks of SSVEPs captured by single-channel EEG devices with dry sensors by ...
The aim of this paper is to improve the performance of signal classification tasks of SSVEPs captured by single-channel EEG devices with dry sensors by logistic ...
Jun 6, 2021 · In this paper, seven features were extracted using wavelet transform and five features was selected using the genetic algorithm.
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For every electrode we have 128 frequency channels. The classification algorithm that we used is the Support Vector Machine (SVM).