TY - JOUR T1 - Real-time detection of event-related brain activity. JF - Neuroimage Y1 - 2008 A1 - Gerwin Schalk A1 - Leuthardt, E C A1 - Peter Brunner A1 - Ojemann, J G A1 - Lester A Gerhardt A1 - Jonathan Wolpaw KW - Adult KW - Algorithms KW - Brain Mapping KW - Computer Systems KW - Diagnosis, Computer-Assisted KW - Electroencephalography KW - Epilepsy KW - Evoked Potentials KW - Female KW - Humans KW - Male KW - Pattern Recognition, Automated KW - Reproducibility of Results KW - Sensitivity and Specificity AB -

The complexity and inter-individual variation of brain signals impedes real-time detection of events in raw signals. To convert these complex signals into results that can be readily understood, current approaches usually apply statistical methods to data from known conditions after all data have been collected. The capability to provide meaningful visualization of complex brain signals without the requirement to initially collect data from all conditions would provide a new tool, essentially a new imaging technique, that would open up new avenues for the study of brain function. Here we show that a new analysis approach, called SIGFRIED, can overcome this serious limitation of current methods. SIGFRIED can visualize brain signal changes without requiring prior data collection from all conditions. This capacity is particularly well suited to applications in which comprehensive prior data collection is impossible or impractical, such as intraoperative localization of cortical function or detection of epileptic seizures.

VL - 43 UR - http://www.ncbi.nlm.nih.gov/pubmed/18718544 IS - 2 ER - TY - JOUR T1 - Electrocorticography-based brain computer interface--the Seattle experience. JF - IEEE Trans Neural Syst Rehabil Eng Y1 - 2006 A1 - Leuthardt, E C A1 - Miller, John W A1 - Gerwin Schalk A1 - Rao, Rajesh P N A1 - Ojemann, J G KW - Cerebral Cortex KW - Electroencephalography KW - Epilepsy KW - Evoked Potentials KW - Humans KW - Therapy, Computer-Assisted KW - User-Computer Interface KW - Washington AB -

Electrocorticography (ECoG) has been demonstrated to be an effective modality as a platform for brain-computer interfaces (BCIs). Through our experience with ten subjects, we further demonstrate evidence to support the power and flexibility of this signal for BCI usage. In a subset of four patients, closed-loop BCI experiments were attempted with the patient receiving online feedback that consisted of one-dimensional cursor movement controlled by ECoG features that had shown correlation with various real and imagined motor and speech tasks. All four achieved control, with final target accuracies between 73%-100%. We assess the methods for achieving control and the manner in which enhancing online control can be accomplished by rescreening during online tasks. Additionally, we assess the relevant issues of the current experimental paradigm in light of their clinical constraints.

VL - 14 UR - http://www.ncbi.nlm.nih.gov/pubmed/16792292 IS - 2 ER - TY - JOUR T1 - A brain-computer interface using electrocorticographic signals in humans. JF - J Neural Eng Y1 - 2004 A1 - Leuthardt, E C A1 - Gerwin Schalk A1 - Jonathan Wolpaw A1 - Ojemann, J G A1 - Moran, D KW - Adult KW - Brain KW - Communication Aids for Disabled KW - Computer Peripherals KW - Diagnosis, Computer-Assisted KW - Electrodes, Implanted KW - Electroencephalography KW - Evoked Potentials KW - Female KW - Humans KW - Imagination KW - Male KW - Movement Disorders KW - User-Computer Interface AB -

Brain-computer interfaces (BCIs) enable users to control devices with electroencephalographic (EEG) activity from the scalp or with single-neuron activity from within the brain. Both methods have disadvantages: EEG has limited resolution and requires extensive training, while single-neuron recording entails significant clinical risks and has limited stability. We demonstrate here for the first time that electrocorticographic (ECoG) activity recorded from the surface of the brain can enable users to control a one-dimensional computer cursor rapidly and accurately. We first identified ECoG signals that were associated with different types of motor and speech imagery. Over brief training periods of 3-24 min, four patients then used these signals to master closed-loop control and to achieve success rates of 74-100% in a one-dimensional binary task. In additional open-loop experiments, we found that ECoG signals at frequencies up to 180 Hz encoded substantial information about the direction of two-dimensional joystick movements. Our results suggest that an ECoG-based BCI could provide for people with severe motor disabilities a non-muscular communication and control option that is more powerful than EEG-based BCIs and is potentially more stable and less traumatic than BCIs that use electrodes penetrating the brain.

VL - 1 UR - http://www.ncbi.nlm.nih.gov/pubmed/15876624 IS - 2 ER -