Name
Title | ||
|---|---|---|
Automated, Scalable and Generalizable Deep Learning for Tracking Cortical Spreading Depression Using EEG |
Abstract | ||
|---|---|---|
We present a non-invasive deep learning approach for tracking cortical spreading depressions (CSDs) in scalp electroencephalography (EEG) signals. Our method, which we refer to as CSD spatially aware convolutional network or CSD-SpArC, combines a convolutional neural network, which extracts temporal features from the EEG signal of each electrode, with a graph neural network, which exploits the spa... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/NER49283.2021.9441333 | 2021 10th International IEEE/EMBS Conference on Neural Engineering (NER) |
Keywords | DocType | ISSN |
Electrodes,Deep learning,Scalability,Propagation,Scalp,Neural engineering,Feature extraction | Conference | 1948-3546 |
ISBN | Citations | PageRank |
978-1-7281-4337-8 | 0 | 0.34 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Alireza Chamanzar | 1 | 0 | 0.68 |
| Xujin Liu | 2 | 0 | 0.34 |
| Lavender Y. Jiang | 3 | 0 | 0.34 |
| Kimon A. Vogt | 4 | 0 | 0.34 |
| José M. F. Moura | 5 | 5137 | 426.14 |
| Pulkit Grover | 6 | 557 | 65.99 |