# Authors Title Details Date Pdf/Links/Bibtex Keywords
21 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
20Ahmed 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
19Marochko 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.
18Singh 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
10.1371/journal.pone.0327791
17Longo 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
10.3390/s25165018
16Gomez-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
15Longo L., Reilly R.B.onEEGwaveLAD: A fully automated online EEG wavelet-based learning adaptive denoiser for artefacts identification and mitigation Plos One 2025 Electroencephalography • Isolation Forests • Computational pipelines • Trees • Probability distribution • Time domain analysis • Wavelet transforms • Denoiser • Artefacts • Signal processing
10.1371/journal.pone.0313076
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
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
10.1109/RTSI61910.2024.10761377
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
11Raufi 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
10.3390/biomedinformatics4010048
10Ahmed 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
9Gómez Tapia C., Bozic B., Longo L. Investigating the Effect of Pre-processing Methods on Model Decision-Making in EEG-Based Person Identification eXplainable Artificial Intelligence, The World Conference (xAI-2023) 2023 Electroencephalography • eXplainable Artificial Intelligence • Deep Learning • Signal processing • attribution xAI methods • Graph-Neural Network • Biometrics • signal-to-noise ratio
10.1007/978-3-031-44070-0_7
8Ahmed 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
7Chikkankod 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
6Longo 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
5Ahmed 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
4Raufi 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
10.3389/fninf.2022.861967
3Gómez-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
2Jindala 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
1Bjegojevic B., Leva M.C., Balfe N., Cromie S., Longo L.Physiological Measurements for Real-time Fatigue Monitoring in Train Drivers: Review of the State of the Art and Reframing the Problem Proceedings of the 31st European Safety and Reliability Conference 2021 Train drivers • Rail • Physiology • Fatigue • Attention • EEG • Eye-tracking • Heart rate
10.3850/978-981-18-2016-8_437-cd
# Authors Title Details Date Pdf/Links/Bibtex Keywords