Name
Abstract | ||
|---|---|---|
In neuroeconomics experiments many ocular artifacts are encountered during long trial durations. In this study, results from algorithms used to remove artifacts in EEG measurements are presented. The study consists of three parts. In the first part, EEG signals were band-pass filtered to remove high frequency noise and low frequency drift. Next, the artifacts were removed by using traditional regression method and independent component analysis (ICA). Finally, the performances of the two artifact removal methods were compared. Although artifacts were suppressed better by ICA than regression, ICA caused decrease in root mean square (RMS) values of the non-artifactual parts of some channels. |
Year | Venue | Keywords |
|---|---|---|
2017 | Signal Processing and Communications Applications Conference | EEG,time-domain regression,independent component analysis,neuroeconomics |
Field | DocType | ISSN |
Computer vision,Pattern recognition,Computer science,Frequency noise,Electrooculography,Root mean square,Independent component analysis,Artificial intelligence,Neuroeconomics,Electroencephalography | Conference | 2165-0608 |
Citations | PageRank | References |
0 | 0.34 | 3 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mehmet Emin Kazanc | 1 | 0 | 0.34 |
| Yasemin P Kahya | 2 | 22 | 4.99 |
| seda ertac | 3 | 0 | 0.68 |
| Burak Güçlü | 4 | 30 | 7.65 |