Boosting bit rates and error detection for the classification of fast-paced motor commands based on single-trial EEG analysis

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.

Linear and nonlinear methods for brain-computer interfaces

“…These slightly stronger assumptions have been fulfilled in several of our BCI experiments, e.g. in [2] and [3]: Fig. 3 clearly shows that the covariance structure is very similar for both classes such that we can safely use Fisher's discriminant.…”

“…In the lower panel, we see also that the class covariances coincide. Thus, the assumptions for using Fisher's discriminant are ideally fulfilled (from [3]). Fig.…”

Towards adaptive classification for BCI

“…We investigate data from a BCI study consisting of experiments with six subjects 4 . For one subject, no effective separation of brain pattern distributions could be achieved.…”

“…A 1 s window of data was used to estimate the features, which were classified over overlapping windows every 40 ms (see figure 1). 4 Three of the authors participated as subjects in the experiments.…”

Brain connectivity in continuous error tasks

“…These potentials are generated as a result of a mismatch in the user's expected outcome of a task event and the actual one [2]. Since the first work suggesting that they could be detected in single trials [5], they have been successfully used within several BMI applications, such as for the correction of BMI outputs [6], classifier adaptation [7] or for learning sequential tasks [8], [9].…”