# 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 • Neuroscience
10.1109/ACCESS.2025.3635543
19Ahmed 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 • Neuroscience
10.1007/978-3-032-08327-2_16
18Marochko 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 • Neuroscience
17Singh G., Chharia A., Upadhyay R., Kumar V., Longo L. PyNoetic: A modular python framework for no-code development of EEG brain-computer interfaces PlosOne 2025 Electroencephalography • Man-computer interface • Signal processing • Programming languages • Signal filtering • Algorithms • Vision • Event-related potentials • Neuroscience
10.1371/journal.pone.0327791
16Longo L., Reilly R.Instantiating the onEEGwaveLAD Framework for Real-Time Muscle Artefact Identification and Mitigation in EEG Signals Sensors 2025 electroencephalography • muscle artefacts • real-time denoiser • discrete wavelet transform • Isolation Forest • machine learning • signal processing and restoration • sliding moving buffer • Neuroscience
10.3390/s25165018
15Gomez-Tapia C., Bozic B, Longo L. Evaluation of EEG pre-processing and source localization in ecological research Frontiers Neuroimaging 2025 Electroencephalography • source localization • ecological settings • inverse modeling • source imaging • eLORETA • pipeline • Neurocomputing • Neuroscience • Ecological Research
10.3389/fnimg.2025.1479569
14Criscuolo 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 • Neuroscience
10.1016/j.csi.2024.103897
13Criscuolo 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 • Neuroscience
10.1109/RTSI61910.2024.10761377
12Marochko 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 • Neuroscience
11Chikkankod 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 • Neuroscience
10 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 • Neuroscience
10.3390/brainsci14040335
9Raufi B., Longo L.Comparing ANOVA and PowerShap Feature Selection Methods via Shapley Additive Explanations of Models of Mental Workload Built with the Theta and Alpha EEG Band Ratios BioMedInformatics 2024 model explainability • mental workload • statistical feature selection • Shapley-based feature selection • alpha and theta EEG band ratios • machine learning • Deep-learning • Neuroscience
10.3390/biomedinformatics4010048
8Ahmed 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 • Neuroscience
7Kalra J., Mittal P., Mittal N., Arora A., Tewari U., Chharia A., Upadhyay R., Kumar V., Longo L.How Visual Stimuli evoked P300 is transforming the Brain-Computer Interface Landscape: A PRISMA Compliant Systematic Review IEEE Transactions On Neural Systems and Rehabilitation Engineering 2023 Electroencephalography • Visualization • Market research • Recording • Brain modeling • Task analysis • Medical diagnostic imaging • Neuroscience
10.1109/TNSRE.2023.3246588
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 • Neuroscience
10.1109/TNSRE.2023.3237375
5Chikkankod 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 • Neuroscience
10.3390/make4040053
4Marochko V.A., Reilly R., McDonnell R., Longo L.A Survey on the Application of Virtual Reality in Event-Related Potential Research Machine Learning and Knowledge Extraction 2022 Event-related potentials • Virtual reality • Survey • Neuroscience
10.1007/978-3-031-14463-9_17
3Raufi B., Longo L.An Evaluation of the EEG Alpha-to-Theta and Theta-to-Alpha Band Ratios as Indexes of Mental Workload Frontiers Neuroinformatics 2022 human mental workload • EEG band ratios • alpha-to-theta ratios • theta-to-alpha ratios • machine learning • classification • Electroencephalography • Neuroscience
10.3389/fninf.2022.861967
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 • Neuroscience
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 • Neuroscience
10.1016/B978-0-323-91197-9.00011-4
# Authors Title Details Date Pdf/Links/Bibtex Keywords