Abstract
Interest in developing a new method of man-to-machine communication-a brain-computer interface (BCI)-has grown steadily over the past few decades. BCIs create a new communication channel between the brain and an output device by bypassing conventional motor output pathways of nerves and muscles. These systems use signals recorded from the scalp, the surface of the cortex, or from inside the brain to enable users to control a variety of applications including simple word-processing software and orthotics. BCI technology could therefore provide a new communication and control option for individuals who cannot otherwise express their wishes to the outside world. Signal processing and classification methods are essential tools in the development of improved BCI technology. We organized the BCI Competition 2003 to evaluate the current state of the art of these tools. Four laboratories well versed in EEG-based BCI research provided six data sets in a documented format. We made these data sets (i.e., labeled training sets and unlabeled test sets) and their descriptions available on the Internet. The goal in the competition was to maximize the performance measure for the test labels. Researchers worldwide tested their algorithms and competed for the best classification results. This paper describes the six data sets and the results and function of the most successful algorithms.
| Original language | English |
|---|---|
| Pages (from-to) | 1044-1051 |
| Number of pages | 8 |
| Journal | IEEE Transactions on Biomedical Engineering |
| Volume | 51 |
| Issue number | 6 |
| DOIs | |
| Publication status | Published - 2004 Jun 1 |
| Externally published | Yes |
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Keywords
- Augmentative communication
- Beta rhythm
- Brain-computer interface (BCI)
- EEG
- Event-related potentials (ERPs)
- Imagined hand movements
- Lateralized readiness potential
- Mu rhythm
- P300
- Rehabilitation
- Single-trial classification
- Slow cortical potentials
ASJC Scopus subject areas
- Biomedical Engineering
Cite this
The BCI competition 2003 : Progress and perspectives in detection and discrimination of EEG single trials. / Blankertz, Benjamin; Muller, Klaus; Curio, Gabriel; Vaughan, Theresa M.; Schalk, Gerwin; Wolpaw, Jonathan R.; Schlögl, Alois; Neuper, Christa; Pfurtscheller, Gert; Hinterberger, Thilo; Schröder, Michael; Birbaumer, Niels.
In: IEEE Transactions on Biomedical Engineering, Vol. 51, No. 6, 01.06.2004, p. 1044-1051.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - The BCI competition 2003
T2 - Progress and perspectives in detection and discrimination of EEG single trials
AU - Blankertz, Benjamin
AU - Muller, Klaus
AU - Curio, Gabriel
AU - Vaughan, Theresa M.
AU - Schalk, Gerwin
AU - Wolpaw, Jonathan R.
AU - Schlögl, Alois
AU - Neuper, Christa
AU - Pfurtscheller, Gert
AU - Hinterberger, Thilo
AU - Schröder, Michael
AU - Birbaumer, Niels
PY - 2004/6/1
Y1 - 2004/6/1
N2 - Interest in developing a new method of man-to-machine communication-a brain-computer interface (BCI)-has grown steadily over the past few decades. BCIs create a new communication channel between the brain and an output device by bypassing conventional motor output pathways of nerves and muscles. These systems use signals recorded from the scalp, the surface of the cortex, or from inside the brain to enable users to control a variety of applications including simple word-processing software and orthotics. BCI technology could therefore provide a new communication and control option for individuals who cannot otherwise express their wishes to the outside world. Signal processing and classification methods are essential tools in the development of improved BCI technology. We organized the BCI Competition 2003 to evaluate the current state of the art of these tools. Four laboratories well versed in EEG-based BCI research provided six data sets in a documented format. We made these data sets (i.e., labeled training sets and unlabeled test sets) and their descriptions available on the Internet. The goal in the competition was to maximize the performance measure for the test labels. Researchers worldwide tested their algorithms and competed for the best classification results. This paper describes the six data sets and the results and function of the most successful algorithms.
AB - Interest in developing a new method of man-to-machine communication-a brain-computer interface (BCI)-has grown steadily over the past few decades. BCIs create a new communication channel between the brain and an output device by bypassing conventional motor output pathways of nerves and muscles. These systems use signals recorded from the scalp, the surface of the cortex, or from inside the brain to enable users to control a variety of applications including simple word-processing software and orthotics. BCI technology could therefore provide a new communication and control option for individuals who cannot otherwise express their wishes to the outside world. Signal processing and classification methods are essential tools in the development of improved BCI technology. We organized the BCI Competition 2003 to evaluate the current state of the art of these tools. Four laboratories well versed in EEG-based BCI research provided six data sets in a documented format. We made these data sets (i.e., labeled training sets and unlabeled test sets) and their descriptions available on the Internet. The goal in the competition was to maximize the performance measure for the test labels. Researchers worldwide tested their algorithms and competed for the best classification results. This paper describes the six data sets and the results and function of the most successful algorithms.
KW - Augmentative communication
KW - Beta rhythm
KW - Brain-computer interface (BCI)
KW - EEG
KW - Event-related potentials (ERPs)
KW - Imagined hand movements
KW - Lateralized readiness potential
KW - Mu rhythm
KW - P300
KW - Rehabilitation
KW - Single-trial classification
KW - Slow cortical potentials
UR - http://www.scopus.com/inward/record.url?scp=2442716489&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=2442716489&partnerID=8YFLogxK
U2 - 10.1109/TBME.2004.826692
DO - 10.1109/TBME.2004.826692
M3 - Article
VL - 51
SP - 1044
EP - 1051
JO - IEEE Transactions on Biomedical Engineering
JF - IEEE Transactions on Biomedical Engineering
SN - 0018-9294
IS - 6
ER -