Selection of EEG signal features for ERD/ERS classification using genetic algorithms
2017 18th International Conference on Computational Problems of …, 2017•ieeexplore.ieee.org
The article presents the use of genetic algorithm (GA) to select and classify ERD/ERS
patterns. One hundred twenty eight channel EEG signal was used in the experiments. The
signal was recorded for 40 people, during the process of imagining right and left hand
movements. Feature extraction was performed using frequency analysis (FFT) with the
resolution of 1Hz. So the features were spectral lines associated with particular electrodes.
The selection of features, calculated for all people, was made with GA. The fitness function …
patterns. One hundred twenty eight channel EEG signal was used in the experiments. The
signal was recorded for 40 people, during the process of imagining right and left hand
movements. Feature extraction was performed using frequency analysis (FFT) with the
resolution of 1Hz. So the features were spectral lines associated with particular electrodes.
The selection of features, calculated for all people, was made with GA. The fitness function …
The article presents the use of genetic algorithm (GA) to select and classify ERD/ERS patterns. One hundred twenty eight channel EEG signal was used in the experiments. The signal was recorded for 40 people, during the process of imagining right and left hand movements. Feature extraction was performed using frequency analysis (FFT) with the resolution of 1Hz. So the features were spectral lines associated with particular electrodes. The selection of features, calculated for all people, was made with GA. The fitness function used in GA was EEG signal classification error calculated using LDA classifier and 5-CV test. The average accuracy of the classification for all people in 8–30Hz band was 0.85, while for the top 10 results 0.92.
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