# Authors Title Details Date Pdf/Links/Bibtex Keywords
10Ahmed T., Biecek P. Longo L. Latent Space Interpretation and Mechanistic Clipping of Subject-Specific Variational Autoencoders of EEG Topographic Maps for Artefacts Reduction eXplainable Artificial Intelligence, The World Conference (xAI-2025) 2025 Electroencephalography • Spectral topographic maps • Subject-specific • Variational autoencoder • Latent space • interpretability • Artefacts removal • Deep learning • full automation • explainable AI
10.1007/978-3-032-08327-2_16
9Criscuolo S., Apicella A., Prevete R., Longo L.Interpreting the latent space of a Convolutional Variational Autoencoder for semi-automated eye blink artefact detection in EEG signals Computer Standards & Interfaces 2025 Electroencephalography • Variational autoencoders • Convolution • Ocular artefacts • detection • Latent space interpretation
10.1016/j.csi.2024.103897
8Criscuolo 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
7Chikkankod 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
6Ahmed T., Longo L.Latent Space Interpretation and Visualisation for Understanding the Decisions of Convolutional Variational Autoencoders Trained with EEG Topographic Maps Joint Proceedings of the xAI-2023 Late-breaking Work, Demos and Doctoral Consortium co-located with the 1st World Conference on eXplainable Artificial Intelligence (xAI-2023) 2023 Electroencephalography • Convolutional variational autoencoders • latent space interpretation • deep learning • spectral topographic maps • Machine Learning
5Natsiou A., O’Leary S., Longo L. An Exploration of the Latent Space of a Convolutional Variational Autoencoder for the Generation of Musical Instrument Tones eXplainable Artificial Intelligence, The World Conference (xAI-2023) 2023 Explainable Artificial Intelligence • Variational Autoencoders • Audio Representations • Audio Synthesis • Latent Feature Importance • Deep Learning • Machine Learning
10.1007/978-3-031-44070-0_24
4Ahmed T., Longo L.Interpreting Disentangled Representations of Person-Specific Convolutional Variational Autoencoders of Spatially Preserving EEG Topographic Maps via Clustering and Visual Plausibility Information 2023 electroencephalography • convolutional variational autoencoder • latent space interpretation • deep learning • spectral topographic maps
10.3390/info14090489
3Natsiou 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
2Chikkankod 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
1Ahmed T., Longo L. Examining the Size of the Latent Space of Convolutional Variational Autoencoders Trained With Spectral Topographic Maps of EEG Frequency Bands IEEE Access 2022 Electroencephalography • convolutional variational autoencoder • latent space • deep learning • frequency bands • spectral topographic maps • neural networks
10.1109/ACCESS.2022.3212777
# Authors Title Details Date Pdf/Links/Bibtex Keywords