Name
Abstract | ||
|---|---|---|
In this study, pattern recognition based brain computer interface is designed using EEG p300 component elicited by visual stimuli. A novel EEG database obtained from 19 subjects is constructed with EMOTIV EPOC+ amplifier and OPENVIBE software. Extreme Learning Machine, a type of single layer neural network, k-nearest neighbour, Bayesian network and Multi-Layer Perceptron classifiers arc compared for classification task in terms of training duration and performance measurements. As a result of subject-based classification, it is observed that Extreme Learning Machine classifier is more efficient and useful. |
Year | Venue | Keywords |
|---|---|---|
2017 | Signal Processing and Communications Applications Conference | brain computer interface,extreme learning machine,p300 component |
Field | DocType | ISSN |
Extreme learning machine,Computer science,Brain–computer interface,Software,OpenVibe,Artificial intelligence,Artificial neural network,Visual perception,Computer vision,Pattern recognition,Speech recognition,Bayesian network,Perceptron | Conference | 2165-0608 |
Citations | PageRank | References |
0 | 0.34 | 4 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Apdullah Yayik | 1 | 2 | 2.14 |
| Yakup Kutlu | 2 | 15 | 4.86 |