# Authors Title Details Date Pdf/Links/Bibtex Keywords
20 Nakanishi T. Longo L.Approximate-Inverse Explainability of beta–VAE Latents for Multichannel EEG Participant-generalised Topographical Representation Learning IEEE Access 2025 Electroencephalography • Brain modeling • Spatial coherence • Scalp • Perturbation methods • Computational modeling • Explainable AI • Deep learning • Electroencephalography • VAE • topographic mapping • approximate inverse model explanations • generative deep learning • representation learning
10.1109/ACCESS.2025.3635543
19Ephrem Tibebe Mekonnen; Longo L., Dondio P.LOMATCE: LOcal Model-Agnostic Time-series Classification Explanations IEEE Access 2025 Time series analysis • Adaptation models • Explainable AI • Predictive models • Data models • Closed box • Perturbation methods • Computational modeling • Deep learning • Kernel • Explainable Artificial Intelligence • Model-agnostic • Time series • Post hoc • Deep learning • XAI
10.1109/ACCESS.2025.3625442
18Vilone G., Longo L. Evaluating Argumentation Graphs as Global Explainable Surrogate Models for Dense Neural Networks and Their Comparison with Decision Trees eXplainable Artificial Intelligence, The World Conference (xAI-2025) 2025 Logical Analysis • Graph Theory • Graph Theory in Probability • Machine Learning • Reasoning • Symbolic AI • Explainable AI • Surrogate models • Computational Argumentation • Rule-based systems • Decision-trees • Dense Neural Networks • Deep learning
10.1007/978-3-032-08333-3_5
17Ahmed 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
16Marochko V., Rogala J., Longo L. Integrated Gradients for Enhanced Interpretation of P3b-ERP Classifiers Trained with EEG-superlets in Traditional and Virtual Environments Joint Proceedings of the xAI 2025 Late-breaking Work, Demos and Doctoral Consortium co-located with the 3rd World Conference on eXplainable Artificial Intelligence (xAI 2025) 2025 Event-related potentials • Deep learning • Convolutional neural networks • Explainable Artificial Intelligence • Integrated Gradients • P3b • Oddball paradigm • time-frequency super-resolution • Superlets.
15Criscuolo 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
14Marochko V., and Longo L.Enhancing the analysis of the P300 event-related potential with integrated gradients on a convolutional neural network trained with superlets 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 Event-related potentials • Deep learning • Convolutional neural networks • Explainable Artificial Intelligence • Integrated gradients • P3b • Oddball paradigm • time-frequency super-resolution • Superlets
13Mekonnen E. T., Longo L., Dondio P. Interpreting Black-Box Time Series Classifiers using Parameterised Event Primitives 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 Explainable Artificial Intelligence • Model-Agnostic • Time Series • Post-hoc • Deep Learning • Machine Learning • Event primitives • Time-series
12Chikkankod 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
11 Lal U, Chikkankod V. A, Longo L.A Comparative Study on Feature Extraction Techniques for the Discrimination of Frontotemporal Dementia and Alzheimer’s Disease with Electroencephalography in Resting-State Adults Brain Sciences 2024 electroencephalography • neural signal processing • feature extraction techniques • supervised learning • deep learning • machine learning
10.3390/brainsci14040335
10Mekonnen E.T., Dondio P., Longo L. Explaining Deep Learning Time Series Classification Models using a Decision Tree-Based Post-Hoc XAI Method 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 Explainable Artificial Intelligence • Deep Learning • Time Series • Classification • Decision-Trees • Machine Learning • Post-hoc
9Ahmed 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
8Vilone G., Longo L.Development of a Human-Centred Psychometric Test for the Evaluation of Explanations Produced by XAI Methods eXplainable Artificial Intelligence, The World Conference (xAI-2023) 2023 Explainable Artificial Intelligence • Human-centred evaluation • Psychometrics • Machine Learning • Deep Learning • Explainability
10.1007/978-3-031-44070-0_11
7Ahmed 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
6Grover N., Chharia A., Upadhyay R., Longo L.Schizo-Net: A novel Schizophrenia Diagnosis framework using late fusion multimodal deep learning on Electroencephalogram-based Brain connectivity indices IEEE Transactions on Neural Systems and Rehabilitation Engineering 2023 Schizophrenia • Deep Learning • Brain Connectivity features • Feature fusion • Classification • Machine Learning
10.1109/TNSRE.2023.3237375
5Longo L.Modeling Cognitive Load as a Self-Supervised Brain Rate with Electroencephalography and Deep Learning Brain Sciences 2022 cognitive load • deep learning • self-supervision • brain rate • convolutional neural network • recurrent neural network • mental workload • EEG bands • electroencephalography • spectral topology-preserving head-maps
10.3390/brainsci12101416
4Ahmed 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
3Hamilton K., Nayak A., Bozic B., Longo L.Is Neuro-Symbolic AI Meeting its Promise in Natural Language Processing? A Structured Review Semantic Web Journal 2022 Neuro-Symbolic • Artificial Intelligence • Natural Language Processing • Deep Learning • Knowledge Representation • Reasoning • Structured Review
10.3233/SW-223228
2Gómez-Tapia C., Bozic B., Longo L.On the Minimal Amount of EEG Data Required for Learning Distinctive Human Features for Task-Dependent Biometric Applications Frontiers Neuroinformatics 2022 biometrics • EEG • feature extraction • machine learning • deep learning • graph neural networks • Electroencephalography
10.0.13.61/fninf.2022.844667
1Jindala K., Upadhyaya R., Padhyb P.K., Longo L.Bi-LSTM-deep CNN for schizophrenia detection using MSST-spectral images of EEG signals Artificial Intelligence-Based Brain-Computer Interface 2022 bi-directional LSTM • Long-Short Term Memory • Deep Learning • Schizophrenia • Spectral analysis • Convolutional neural network • Electroencephalography
10.1016/B978-0-323-91197-9.00011-4
# Authors Title Details Date Pdf/Links/Bibtex Keywords