Name
Title | ||
|---|---|---|
On-Chip Machine Learning for Portable Systems: Application to Electroencephalography-based Brain-Computer Interfaces |
Abstract | ||
|---|---|---|
The improvement of hardware for the acquisition and processing of electroencephalography (EEG) has made its portability become a reality. This allows for studies to be carried outside lab settings, as well as many commercial applications. As recordings are done over extended periods, these devices generate large volumes of data, mainly if the neuronal activity is recorded through multiple channels. Machine learning (ML) techniques allow to effectively analyse and use this data for a wide range of applications. However the portability of these techniques can be challenging. In this article, we set out to review over 40 relevant articles where ML techniques in a diverse set of EEG applications that have successfully been incorporated into portable systems. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/IJCNN52387.2021.9533413 | 2021 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN) |
Keywords | DocType | ISSN |
EEG, embedded systems, hardware processing, neural networks, signal processing | Conference | 2161-4393 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Marcos Fabietti | 1 | 1 | 3.40 |
| Mufti Mahmud | 2 | 89 | 20.03 |
| Ahmad Lotfi | 3 | 88 | 20.21 |