Multi-Classification of Electroencephalogram Signals, for Motor Imagination, using Statistical Signal Processing and Deep Learning
DOI:
https://doi.org/10.59681/2175-4411.v15.iEspecial.2023.1107Keywords:
Signal Processing, Deep Learning, ImaginationAbstract
Objectives: The classification of electroencephalogram (EEG) signals is the basis for building systems with a brain-computer interface. Its development is faced with the complexity of EEG signals, which differ from subject to subject, making their classification complex. Therefore, this work aims to compare the performance of an artificial neural network using different signal processing techniques, in the classification of a resting state and two states of motor imagination (MI). Methods: For this work, we used three statistical techniques of signal processing and a Convolutional Neural Network. The database used for the classification consists of the EEG recording of 109 volunteers, made available by Physionet. Result and Conclusion: It was observed that Principal Component Analysis reduced the computational cost without loss of performance in accuracy. However, Independent Component Analysis and Singular Spectral Analysis did not obtain promising results.
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