| # | Authors | Title | Details | Date | Pdf/Links/Bibtex | Keywords |
|---|---|---|---|---|---|---|
| 4 | Marochko 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 | @inproceedings{MarochkoLongo2025, title={Integrated Gradients for Enhanced Interpretation of P3b-ERP Classifiers Trained with EEG-superlets in Traditional and Virtual Environments}, author={Marochko, Vladimir and Rogala, Jacek and Longo, Luca}, year={2025}, booktitle = {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), Istanbul, Turkey, 9-11 July, 2025}, publisher = {CEUR-WS.org}, volume = {4017}, series = {{CEUR} Workshop Proceedings}, editor = {Przemys?aw Biecek, Slawomir Nowaczyk, Gitta Kutyniok, Luca Longo}, pages={49-56}, url={https://ceur-ws.org/Vol-4017/paper_07.pdf} } [Close]
| Event-related potentials • Deep learning • Convolutional neural networks • Explainable Artificial Intelligence •
Integrated Gradients • P3b • Oddball paradigm • time-frequency super-resolution • Superlets. |
| 3 | Marochko 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 |
@inproceedings{Marochko2024, title={Enhancing the analysis of the P300 event-related potential with integrated gradients on a convolutional neural network trained with superlets}, author={Marochko, Vladimir, and Longo, Luca}, year={2024}, booktitle = {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), Valletta, Malta, 17-19 July, 2024}, publisher = {CEUR-WS.org}, url = {https://ceur-ws.org/Vol-3793/paper_19.pdf}, volume = {3793}, series = {{CEUR} Workshop Proceedings}, editor = {Luca Longo, Weiru Liu, Grégoire Montavon}, pages={145-152} } [Close]
| Event-related potentials • Deep learning • Convolutional neural networks • Explainable Artificial Intelligence •
Integrated gradients • P3b • Oddball paradigm • time-frequency super-resolution • Superlets |
| 2 | Longo L. | Modeling Cognitive Load as a Self-Supervised Brain Rate with Electroencephalography and Deep Learning | Brain Sciences | 2022 |
@Article{LongoBrainsci12101416, AUTHOR = {Longo, Luca}, TITLE = {Modeling Cognitive Load as a Self-Supervised Brain Rate with Electroencephalography and Deep Learning}, JOURNAL = {Brain Sciences}, VOLUME = {12}, YEAR = {2022}, NUMBER = {10}, ARTICLE-NUMBER = {1416}, URL = {https://www.mdpi.com/2076-3425/12/10/1416}, ISSN = {2076-3425}, DOI = {10.3390/brainsci12101416} } [Close]
| cognitive load • deep learning • self-supervision • brain rate • convolutional neural network • recurrent neural network • mental workload • EEG bands • electroencephalography • spectral topology-preserving head-maps |
| 1 | Jindala 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 |
@incollection{JINDAL2022145, title = {6 - Bi-LSTM-deep CNN for schizophrenia detection using MSST-spectral images of EEG signals}, editor = {Varun Bajaj and G.R. Sinha}, booktitle = {Artificial Intelligence-Based Brain-Computer Interface}, publisher = {Academic Press}, pages = {145-162}, year = {2022}, isbn = {978-0-323-91197-9}, doi = {https://doi.org/10.1016/B978-0-323-91197-9.00011-4}, url = {https://www.sciencedirect.com/science/article/pii/B9780323911979000114}, author = {Komal Jindal and Rahul Upadhyay and Prabin Kumar Padhy and Luca Longo}, keywords = {Schizophrenia, Electroencephalography, Multisynchrosqueezing transform, Bi-directional long short-term memory, Convolutional neural network, Classification, Deep learning} } [Close]
| bi-directional LSTM • Long-Short Term Memory • Deep Learning • Schizophrenia • Spectral analysis • Convolutional neural network • Electroencephalography |
| # | Authors | Title | Details | Date | Pdf/Links/Bibtex | Keywords |