# Authors Title Details Date Pdf/Links/Bibtex Keywords
9 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
8Ahmed 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
7Longo 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
6 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
5Lal 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
4Longo 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
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