# Authors Title Details Date Pdf/Links/Bibtex Keywords
4Criscuolo S., Giugliano S., Apicella, A., Donnarumma F., Amato F. Tedesco A., Longo L.Exploring the Latent Space of Person-Specific Convolutional Autoencoders for Eye-Blink Artefact Mitigation in EEG Signals 2024 IEEE 8th Forum on Research and Technologies for Society and Industry Innovation (RTSI) 2024 Electroencephalography • Autoencoders • Eye-blink Artefacts Detection • Latent Space interpretation • Explainable Artificial Intelligence • Artificial Intelligence • Machine Learning • Deep learning
10.1109/RTSI61910.2024.10761377
3Chikkankod A.V., Longo L.A proposal for improving EEG microstate generation via interpretable deep clustering with convolutional autoencoders Joint Proceedings of the xAI 2024 Late-breaking Work, Demos and Doctoral Consortium co-located with the 2nd World Conference on eXplainable Artificial Intelligence (xAI 2024) 2024 EEG Microstates • Shallow clustering • Deep clustering • Convolutional autoencoders • Resting state • Machine Learning • Deep Learning • Microstate theory
2Natsiou A., Longo L., O'Leary S.An investigation of the reconstruction capacity of stacked convolutional autoencoders for log-mel-spectrograms 16th International Conference on Signal-Image Technology & Internet-Based Systems 2022 Log-mel-spectrogram • reconstruction • autoencoders • machine learning
10.1109/SITIS57111.2022.00038
1Chikkankod A.V., Longo L. On the Dimensionality and Utility of Convolutional Autoencoder’s Latent Space Trained with Topology-Preserving Spectral EEG Head-Maps Machine Learning and Knowledge Extraction 2022 electroencephalography • latent space analysis • sliding windowing • convolutional autoencoders • automatic feature extraction • dense neural network
10.3390/make4040053
# Authors Title Details Date Pdf/Links/Bibtex Keywords