Phase relationships between different subdural electrode recordings in man.

Almost all brain-computer interfaces (BCIs) ignore information related to the phase coupling between electroencephalogram (EEG) or electrocorticogram (ECoG) recordings from different electrodes. This paper investigates whether additional information can be found when calculating the amount of synchronization between two electrode channels by using a phase locking measurement called the phase locking value (PLV). Special emphasis is put on the beta band (around 20 Hz) as well as the gamma band (high frequencies up to 95 Hz), which can only be used when subdural electrode recordings are available.

Voice control of a powered wheelchair.

Several researchers have described voice control mechanisms for a power wheelchair, but voice control has yet to become a commercially viable control alternative. One problem with voice control is that the voice's limited bandwidth renders it impossible to make frequent small adjustments to the wheelchair's velocity. One possible solution is to utilize voice control in combination with the navigation assistance provided by "smart wheelchairs," which use sensors to identify and avoid obstacles in the wheelchair's path. This paper describes an experiment that compares the performance of able-bodied subjects using voice control to operate a power wheelchair both with and without navigation assistance.

Detection of movement-related desynchronization patterns in ongoing single-channel electrocorticogram.

Adaptive autoregressive parameters and a linear classifier were used to detect movement related desynchronization and synchronization patterns in single-channel electrocorticogram (ECoG) obtained from implanted electrode grids. The best classification accuracies found had more than 90% hits and less than 10% false positives. The findings show that the detection of event-related desynchronization and synchronization in ECoG data can be used to reliably provide switch control directly by the brain and is therefore very suitable as the basis of a direct brain interface.

Toward a direct brain interface based on human subdural recordings and wavelet-packet analysis.

Highly accurate asynchronous detection of movement related patterns in individual electrocorticogram channels has been shown using detection based on either event-related potentials (ERPs) or event-related desynchronization and synchronization (ERD/ERS). A method using wavelet-packet features selected with a genetic algorithm was proposed to simultaneously detect ERP and ERD/ERS and was tested on data from seven subjects and four motor tasks. The proposed wavelet method performed better than previous methods with perfect detection for four subject/task combinations and hit percentages greater than 90% with false positive percentages less than 15% for at least one task for all seven subjects.

Quality estimation of subdurally recorded, event-related potentials based on signal-to-noise ratio.

Our goal is to develop a direct brain interface (DBI) that will provide communication and environmental control to persons who are "locked-in" (or nearly so) as a consequence of brainstem stroke, amyotrophic lateral sclerosis (ALS), or other etiologies. Previously we demonstrated that templates constructed from trigger averaged event-related potentials (ERPs) can be cross-correlated with ongoing electrocorticograms (ECoGs) to detect ERPs associated with the performance of simple motor actions. However, it was difficult to predict a priori which of many candidate ECoG recording site(s) could provide signals that would provide adequate motor action detection. We present here a measure of ERP quality based on an estimate of the signal to noise ratio (SNR) associated with the formation of an ERP template from the performance of consecutive voluntary actions. Detection-theory-based receiver operator characteristics (ROCs) and a database of ECoGs (6000+) recorded from the cortical surface of awake human subjects were used to assess the usefulness of the SNR technique. The SNR method was found to predict the detection efficacy of ERPs when characterized over a wide parameter range, with the majority of ROC curve areas greater than 90%. This method was compared with our previously developed quality measure (the peak-to-baseline ratio) and found to provide significantly better performance (ROC area differences from 4.4% to 13.7%). Thus, the SNR estimate of the ERP is a useful tool to predict the efficacy of ERP templates for cross-correlation-based detection and assist in the selection of viable ERP templates for DBI applications.