Mahnoosh Tajmirriahi
An Interpretable Convolutional Neural Network for P300 Detection: Analysis of Time Frequency Features for Limited Data
Tajmirriahi, Mahnoosh; Amini, Zahra; Rabbani, Hossein; Kafieh, Rahele
Authors
Citation
Tajmirriahi, M., Amini, Z., Rabbani, H., & Kafieh, R. (2022). An Interpretable Convolutional Neural Network for P300 Detection: Analysis of Time Frequency Features for Limited Data. IEEE Sensors Journal, 22(9), 8685-8692. https://doi.org/10.1109/jsen.2022.3159475
| Journal Article Type | Article |
|---|---|
| Publication Date | 2022 |
| Deposit Date | Jul 12, 2022 |
| Journal | IEEE SENSORS JOURNAL |
| Print ISSN | 1530-437X |
| Electronic ISSN | 1558-1748 |
| Publisher | Institute of Electrical and Electronics Engineers |
| Volume | 22 |
| Issue | 9 |
| Pages | 8685-8692 |
| DOI | https://doi.org/10.1109/jsen.2022.3159475 |
| Public URL | https://durham-repository.worktribe.com/output/1198235 |
You might also like
Application of Artificial Intelligence in Ophthalmology: An Updated Comprehensive Review.
(2024)
Journal Article
Downloadable Citations
About Durham Research Online (DRO)
Administrator e-mail: dro.admin@durham.ac.uk
This application uses the following open-source libraries:
SheetJS Community Edition
Apache License Version 2.0 (http://www.apache.org/licenses/)
PDF.js
Apache License Version 2.0 (http://www.apache.org/licenses/)
Font Awesome
SIL OFL 1.1 (http://scripts.sil.org/OFL)
MIT License (http://opensource.org/licenses/mit-license.html)
CC BY 3.0 ( http://creativecommons.org/licenses/by/3.0/)
Powered by Worktribe © 2025
Advanced Search