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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 ; Müller, Klaus-Robert ; Kunzmann, Volker ; Losch, Florian ; Curio, Gabriel

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society, June 2003, Vol.11(2), pp.127-31 [Tạp chí có phản biện]

ISSN: 1534-4320 ; PMID: 12899253 Version:1

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  • Nhan đề:
    Boosting bit rates and error detection for the classification of fast-paced motor commands based on single-trial EEG analysis
  • Tác giả: Blankertz, Benjamin ; Dornhege, Guido ; Schäfer, Christin ; Krepki, Roman ; Kohlmorgen, Jens ; Müller, Klaus-Robert ; Kunzmann, Volker ; Losch, Florian ; Curio, Gabriel
  • Chủ đề: Algorithms ; User-Computer Interface ; Brain -- Physiology ; Electroencephalography -- Methods ; Evoked Potentials -- Physiology ; Movement -- Physiology
  • Là 1 phần của: IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society, June 2003, Vol.11(2), pp.127-31
  • Mô tả: 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.
  • Ngôn ngữ: English
  • Số nhận dạng: ISSN: 1534-4320 ; PMID: 12899253 Version:1

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