Name
Abstract | ||
|---|---|---|
Brain-computer interface (BCI) have recently entered the research limelight. In many such systems, external computers and machines are controlled by brain activity signals measured using near-infrared spectroscopy (NIRS) or electroencephalograph (EEG) devices. In this paper, we propose a probabilistic data interpolation-boosting algorithm for BCI, where we adopt three evaluation criterions to decide the class of interpolated data around the misclassified data. By using the interpolated data with classes, the discriminated boundary is shown to control the external machine effectively. We verify our boosting method with numerical examples, and discuss the results. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/FUZZ-IEEE.2013.6622499 | Fuzzy Systems |
Keywords | Field | DocType |
brain-computer interfaces,electroencephalography,infrared spectroscopy,interpolation,medical signal processing,pattern classification,probability,EEG devices,NIRS,adaptive ensemble model,brain activity signals,brain-computer interfaces,electroencephalograph devices,external computer control,interpolated data,machine control,misclassified data,near-infrared spectroscopy,probabilistic data interpolation-boosting algorithm,Boosting Algorithm,Brain-Computer Interface,Probabilistic Data Interpolation | Ensemble forecasting,Computer science,Brain–computer interface,Interpolation,Brain activity and meditation,Boosting (machine learning),Artificial intelligence,Probabilistic logic,Machine learning,Electroencephalography | Conference |
ISSN | ISBN | Citations |
1098-7584 | 978-1-4799-0020-6 | 0 |
PageRank | References | Authors |
0.34 | 5 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Isao Hayashi | 1 | 276 | 85.75 |
| Shinji Tsuruse | 2 | 0 | 0.34 |