4.6 Article Proceedings Paper

A public data hub for benchmarking common brain-computer interface algorithms

Journal

JOURNAL OF NEURAL ENGINEERING
Volume 8, Issue 2, Pages -

Publisher

IOP PUBLISHING LTD
DOI: 10.1088/1741-2560/8/2/025021

Keywords

-

Ask authors/readers for more resources

Methods of statistical machine learning have recently proven to be very useful in contemporary brain-computer interface (BCI) research based on the discrimination of electroencephalogram (EEG) patterns. Because of this, many research groups develop new algorithms for both feature extraction and classification. However, until now, no large-scale comparison of these algorithms has been accomplished due to the fact that little EEG data is publicly available. Therefore, we at Team PhyPA recorded 32-channel EEGs, electromyograms and electrooculograms of 36 participants during a simple finger movement task. The data are published on our website www.phypa.org and are freely available for downloading. We encourage BCI researchers to test their algorithms on these data and share their results. This work also presents exemplary benchmarking procedures of common feature extraction methods for slow cortical potentials and event-related desynchronization as well as for classification algorithms based on these features.

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.6
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

No Data Available
No Data Available