Determination of the common electrodes for users and increasing the classification accuracy of motor imagery EEG

• Published in: Neural computing & applications Vol. 37; no. 6; pp. 5057 - 5076
• Main Authors: Özkahraman, Ali, Ölmez, Tamer, Dokur, Zümray
• Format: Journal Article
• Language: English
• Published: London Springer London 01.02.2025; Springer Nature B.V
• Subjects: Accuracy; Artificial Intelligence; Artificial neural networks; Classification; Computational Biology/Bioinformatics; Computational Science and Engineering; Computer Science; Continuous wavelet transform; Data Mining and Knowledge Discovery; Datasets; Electrodes; Electroencephalography; Fourier transforms; Human-computer interface; Image Processing and Computer Vision; Independent component analysis; Neural networks; Original Article; Probability and Statistics in Computer Science; Wavelet transforms; EEG channel selection; EEG classification; Deep neural network; Reduction of number of electrodes; Artificial EEG dataset

In recent studies, it is observed that the success rates for the classification of the EEG (electroencephalogram) data have increased by using deep neural networks. However, the portability and practicability of the brain computer interface systems are still not good since too many electrodes are us...