title: A public data hub for benchmarking common brain-computer interface algorithms.
abstract: 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: Team PhyPA, Berlin Institute of Technology, Franklinstrasse 28/29, Berlin, Germany. tzander@gmail.com
authors: Thorsten O
authors: TO
authors: Zander
authors: Klas
authors: K
authors: Ihme
authors: Matti
authors: M
authors: Gärtner
authors: Matthias
authors: M
authors: Rötting
mesh_terms: D000465:Algorithms; D019985:Benchmarking; D001931:Brain Mapping / Q000379:methods; D016208:Databases, Factual; D004569:Electroencephalography / Q000379:methods*; D019054:Evoked Potentials, Motor / Q000502:physiology*; D006801:Humans; D007092:Imagination / Q000502:physiology; D033181:Information Dissemination / Q000379:methods; D020407:Internet; D009044:Motor Cortex / Q000502:physiology*; D010363:Pattern Recognition, Automated / Q000379:methods; D012984:Software; D012986:Software Validation; D014584:User-Computer Interface