Abstract
Brain-computer interfaces (BCIs) involve two coupled adapting systems - the human subject and the computer. In developing our BCI, our goal was to minimize the need for subject training and to impose the major learning load on the computer. To this end, we use behavioral paradigms that exploit single-trial EEG potentials preceding voluntary finger movements. Here, we report recent results on the basic physiology of such premovement event-related potentials (ERP). 1) We predict the laterality of imminent left- versus right-hand finger movements in a natural keyboard typing condition and demonstrate that a single-trial classification based on the lateralized Bereitschaftspotential (BP) achieves good accuracies even at a pace as fast as 2 taps/s. Results for four out of eight subjects reached a peak information transfer rate of more than 15 b/min; the four other subjects reached 6-10 b/min. 2) We detect cerebral error potentials from single false-response trials in a forced-choice task, reflecting the subject's recognition of an erroneous response. Based on a specifically tailored classification procedure that limits the rate of false positives at, e.g., 2%, the algorithm manages to detect 85% of error trials in seven out of eight subjects. Thus, concatenating a primary single-trial BP-paradigm involving finger classification feedback with such secondary error detection could serve as an efficient online confirmation/correction tool for improvement of bit rates in a future BCI setting. As the present variant of the Berlin BCI is designed to achieve fast classifications in normally behaving subjects, it opens a new perspective for assistance of action control in time-critical behavioral contexts; the potential transfer to paralyzed patients will require further study.
| Original language | English |
|---|---|
| Pages (from-to) | 127-131 |
| Number of pages | 5 |
| Journal | IEEE Transactions on Neural Systems and Rehabilitation Engineering |
| Volume | 11 |
| Issue number | 2 |
| DOIs | |
| Publication status | Published - 2003 Jun 1 |
| Externally published | Yes |
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Keywords
- Bereitschaftspotential (BP)
- Brain-computer interface (BCI)
- Error potential
- Fisher's discriminant
- Linear classification
- Multichannel EEG
- Single-trial analysis
ASJC Scopus subject areas
- Rehabilitation
- Biophysics
- Bioengineering
- Health Professions(all)
Cite this
Boosting bit rates and error detection for the classification of fast-paced motor commands based on single-trial EEG analysis. / Blankertz, Benjamin; Dornhege, Guido; Schäfer, Christin; Krepki, Roman; Kohlmorgen, Jens; Muller, Klaus; Kunzmann, Volker; Losch, Florian; Curio, Gabriel.
In: IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol. 11, No. 2, 01.06.2003, p. 127-131.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Boosting bit rates and error detection for the classification of fast-paced motor commands based on single-trial EEG analysis
AU - Blankertz, Benjamin
AU - Dornhege, Guido
AU - Schäfer, Christin
AU - Krepki, Roman
AU - Kohlmorgen, Jens
AU - Muller, Klaus
AU - Kunzmann, Volker
AU - Losch, Florian
AU - Curio, Gabriel
PY - 2003/6/1
Y1 - 2003/6/1
N2 - Brain-computer interfaces (BCIs) involve two coupled adapting systems - the human subject and the computer. In developing our BCI, our goal was to minimize the need for subject training and to impose the major learning load on the computer. To this end, we use behavioral paradigms that exploit single-trial EEG potentials preceding voluntary finger movements. Here, we report recent results on the basic physiology of such premovement event-related potentials (ERP). 1) We predict the laterality of imminent left- versus right-hand finger movements in a natural keyboard typing condition and demonstrate that a single-trial classification based on the lateralized Bereitschaftspotential (BP) achieves good accuracies even at a pace as fast as 2 taps/s. Results for four out of eight subjects reached a peak information transfer rate of more than 15 b/min; the four other subjects reached 6-10 b/min. 2) We detect cerebral error potentials from single false-response trials in a forced-choice task, reflecting the subject's recognition of an erroneous response. Based on a specifically tailored classification procedure that limits the rate of false positives at, e.g., 2%, the algorithm manages to detect 85% of error trials in seven out of eight subjects. Thus, concatenating a primary single-trial BP-paradigm involving finger classification feedback with such secondary error detection could serve as an efficient online confirmation/correction tool for improvement of bit rates in a future BCI setting. As the present variant of the Berlin BCI is designed to achieve fast classifications in normally behaving subjects, it opens a new perspective for assistance of action control in time-critical behavioral contexts; the potential transfer to paralyzed patients will require further study.
AB - Brain-computer interfaces (BCIs) involve two coupled adapting systems - the human subject and the computer. In developing our BCI, our goal was to minimize the need for subject training and to impose the major learning load on the computer. To this end, we use behavioral paradigms that exploit single-trial EEG potentials preceding voluntary finger movements. Here, we report recent results on the basic physiology of such premovement event-related potentials (ERP). 1) We predict the laterality of imminent left- versus right-hand finger movements in a natural keyboard typing condition and demonstrate that a single-trial classification based on the lateralized Bereitschaftspotential (BP) achieves good accuracies even at a pace as fast as 2 taps/s. Results for four out of eight subjects reached a peak information transfer rate of more than 15 b/min; the four other subjects reached 6-10 b/min. 2) We detect cerebral error potentials from single false-response trials in a forced-choice task, reflecting the subject's recognition of an erroneous response. Based on a specifically tailored classification procedure that limits the rate of false positives at, e.g., 2%, the algorithm manages to detect 85% of error trials in seven out of eight subjects. Thus, concatenating a primary single-trial BP-paradigm involving finger classification feedback with such secondary error detection could serve as an efficient online confirmation/correction tool for improvement of bit rates in a future BCI setting. As the present variant of the Berlin BCI is designed to achieve fast classifications in normally behaving subjects, it opens a new perspective for assistance of action control in time-critical behavioral contexts; the potential transfer to paralyzed patients will require further study.
KW - Bereitschaftspotential (BP)
KW - Brain-computer interface (BCI)
KW - Error potential
KW - Fisher's discriminant
KW - Linear classification
KW - Multichannel EEG
KW - Single-trial analysis
UR - http://www.scopus.com/inward/record.url?scp=0043244916&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=0043244916&partnerID=8YFLogxK
U2 - 10.1109/TNSRE.2003.814456
DO - 10.1109/TNSRE.2003.814456
M3 - Article
C2 - 12899253
AN - SCOPUS:0043244916
VL - 11
SP - 127
EP - 131
JO - IEEE Transactions on Neural Systems and Rehabilitation Engineering
JF - IEEE Transactions on Neural Systems and Rehabilitation Engineering
SN - 1534-4320
IS - 2
ER -