Name
Title | ||
|---|---|---|
Neural Network-based Artifact Detection in Local Field Potentials Recorded from Chronically Implanted Neural Probes. |
Abstract | ||
|---|---|---|
The neural recordings known as Local Field Potentials (LFPs) provide important information on how neural circuits operate and relate. Due to the involvement of complex electronic apparatuses in the recording setups, these signals are often significantly contaminated by artifacts generated by a number of internal and external sources. To make the best use of these signals, it is imperative to detect and remove the artifacts from these signals. Hence, this work proposes a pattern recognition neural network based single-channel automatic artifact detection tool. The tool is capable of detecting the artifacts with an 93.2% of overall accuracy and requires an average computing time of 2.57 seconds to analyse LFPs of one minute duration, making it a strong candidate for online deployment without the need for employing high performance computing equipment. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/IJCNN48605.2020.9207320 | IJCNN |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Marcos Fabietti | 1 | 1 | 3.40 |
| Mufti Mahmud | 2 | 89 | 20.03 |
| Ahmad Lotfi | 3 | 88 | 20.21 |
| Alberto Avarua | 4 | 0 | 0.34 |
| David Gugganmos | 5 | 0 | 0.34 |
| Randolph Xudo | 6 | 0 | 0.34 |
| Michela Chiappalone | 7 | 61 | 10.06 |