# 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
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
10.1007/978-3-032-08327-2_16
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
12 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
11Lal U., Vinayak Chikkankod A., Longo L. Fractal dimensions and machine learning for detection of Parkinson’s disease in resting-state electroencephalography Neural Computing and Applications 2024 Electroencephalography • Explainable AI • Fractal dimension • Entropy • Sliding windowing • Feature extraction • Supervised learning • Machine Learning • Deep-learning
10.1007/s00521-024-09521-4
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
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
10.1109/TNSRE.2023.3246588
6Chikkankod 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
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
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
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
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