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- W4313444731 abstract "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." @default.
- W4313444731 created "2023-01-06" @default.
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- W4313444731 date "2023-01-01" @default.
- W4313444731 modified "2023-10-16" @default.
- W4313444731 title "Comparative Study of Neural Networks (G/C/RNN) and Traditional Machine Learning Models on EEG Datasets" @default.
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- W4313444731 doi "https://doi.org/10.1007/978-981-19-2358-6_17" @default.
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