Movement-related potentials during the performance of a motor task II: cerebral areas activated during learning of the task

On the effects of adaptation to changing loads on movement-related EEG potentials

Movement-related potentials (MRP), a component of the electroencephalogram (EEG) generated during voluntary movements, are known to vary during adaptation to changing loads and to different load types. This study attempts to reveal these changes. A novel denoising algorithm based on iterative approximation was applied to the MRPs recorded from four subjects while performing simple movements against changing loads. The results show that when subjects perform a repetitive task under a constant load there appears a significant peak in the activity of several MRP components recorded over the prefrontal cortex during the third and fourth repetition of the task. Furthermore, different types of loads do not affect the shape of the MRP but different force intensities do.

An improved P300-based brain-computer interface

A brain-computer interface (BCI) is a system for direct communication between brain and computer. The BCI developed in this work is based on a BCI described by Farwell and Donchin in 1988, which allows a subject to communicate one of 36 symbols presented on a 6 x 6 matrix. The system exploits the P300 component of event-related brain potentials (ERP) as a medium for communication. The processing methods distinguish this work from Donchin's work. In this work, independent component analysis (ICA) was used to separate the P300 source from the background noise. A matched filter was used together with averaging and threshold techniques for detecting the existence of P300s. The processing method was evaluated offline on data recorded from six healthy subjects. The method achieved a communication rate of 5.45 symbols/min with an accuracy of 92.1% compared to 4.8 symbols/min with an accuracy of 90% in Donchin's work. The online interface was tested with the same six subjects. The average communication rate achieved was 4.5 symbols/min with an accuracy of 79.5 % as apposed to the 4.8 symbols/min with an accuracy of 56 % in Donchin's work. The presented BCI achieves excellent performance compared to other existing BCIs, and allows a reasonable communication rate, while maintaining a low error rate.

Detection of movement-related potentials from the electro-encephalogram for possible use in a brain-computer interface

Brain-computer interfaces are devices for enabling patients with severe motor disorders to communicate with the world. One method for operating such devices is to use movement-related potentials that are generated in the brain when the patient moves, or imagines a movement of, one of his limbs. An algorithm for detecting movement-related potentials using a small number of EEG channels was developed. This algorithm is a combination of the matched filter, a non-linear transformation previously developed as part of a similar detector, and a classifier. The algorithm was compared with previous designs of similar detectors by both theoretic analysis and off-line evaluation of performance on data recorded from five subjects. It is shown that the performance of the algorithm was superior to that of previous methods, improving the area under the receiver operating characteristic curve to 87.8%, an improvement of 25% compared with the best previously suggested detection method. Finally, the probable sources for false detections were identified, and possible ways to minimise them are proposed.