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Comparative Study of Neural Networks (G/C/RNN) and Traditional Machine Learning Models on EEG Datasets

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Part of the Cognitive Science and Technology book series (CSAT)


Background: EEG provides researchers with an opportunity to study neural correlates in terms of temporal connectivity. This connectivity can shed light on the possible network topology between a healthy person versus a patient or help differentiate between two different groups (experts and non-experts). Purpose: With the help of machine learning models, the difference in network topology can be used to understand the neural correlations between healthy control and a patient with ease compared to traditional EEG analysis. Further, a comparative analysis between the different spectral connectivity measures provides the best suitable measure for the study. Methods: EEG data from a meditation study (n = 31) and Parkinson's study (n = 24) containing the resting-state EEG recordings are utilized here. The EEG data is converted to spectral connectivity: coherence, which becomes the input for the machine learning models, support vector machine, k-means clustering, deep convolution neural networks, recurrent neural networks, and graph neural networks. Results: Classification accuracies of SVM and RNN are 56.585 and 56%, whereas D-CNN provides an accuracy of 59.5%. Both (~ 7%) k-means and GNN failed in the off-the-shelf approach. Conclusion: The comparative study shows the application capabilities of neural networks machine learning with commonly used machine learning models and the impact the various connectivity measures have on model accuracy.


  • EEG
  • Meditation
  • Parkinson’s disease
  • Classification
  • Machine learning
  • Support vector machine
  • k-means clustering
  • Graph neural networks

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We would like to thank OpenNeuro for providing access to the EEG datasets and citations for the datasets have been included within the main text.


The authors declare no conflict of interest.

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Correspondence to Veeky Baths .

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Baboo, G.K., Dubey, S., Baths, V. (2023). Comparative Study of Neural Networks (G/C/RNN) and Traditional Machine Learning Models on EEG Datasets. In: Kumar, A., Ghinea, G., Merugu, S., Hashimoto, T. (eds) Proceedings of the International Conference on Cognitive and Intelligent Computing. Cognitive Science and Technology. Springer, Singapore.

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  • Print ISBN: 978-981-19-2357-9

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