I am planning to conduct a cluster analysis of EEG PSD values and respective synchronicity estimates (e.g. alpha/theta) using deep learning methods. I am still in the literature review stage and I would like to know the latest deep learning algorithms used for EEG clustering.
I see most papers using K-means and feed forward neural network.
You can check this publication where I use MLP (DL). Adagrad give good result .
@article{islam2022explainable, title={Explainable machine learning methods for classification of brain states during visual perception}, author={Islam, Robiul and Andreev, Andrey V and Shusharina, Natalia N and Hramov, Alexander E}, journal={Mathematics}, volume={10}, number={15}, pages={2819}, year={2022}, publisher={MDPI} }
The most important is representation learning. Deep learning can also be used as an effective tool to extract features, and then use clustering methods to analyze the features after dimension reduction. As for unsupervised feature extraction, a deep learning model such as autoencoder (AE) can be considered.
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