Abstract
Electroencephalography (EEG) has been used for quite some time as a diagnostic technique in neurology. The goal of this publication is to serve as a resource for researchers interested in applying deep learning methods to EEG data. This paper proposes a unique Hybrid Machine-Deep Learning model that can learn and classify EEG signals on its own. This method allows the model to classify EEG signals of varied sampling frequencies and durations automatically. The proposed model used feature extraction methods from artificial design and performed extensive tests with EEG data collected at varying sample rates to determine how well our suggested model performed. The results show that the Hybrid Machine-Deep Learning strategy significantly improves performance, leading to a remarkable 99.97% classification accuracy. Notably, this method performs exceptionally well when labeling lower-frequency EEG signals (less than 4 Hz). The proposed model has improved consistency and robustness, as shown by this study.
Recommended Citation
Abdulaziz, Osama Mohsin and Saltykova, O A.
(2023)
"CNN-PS: Electroencephalogram Classification of Brain States Using Hybrid Machine -Deep Learning Approach,"
Iraqi Journal for Computer Science and Mathematics: Vol. 4:
Iss.
4, Article 6.
DOI: https://doi.org/10.52866/ijcsm.2023.04.04.006
Available at:
https://ijcsm.researchcommons.org/ijcsm/vol4/iss4/6
