TY - JOUR T1 - Brain-computer interfacing based on cognitive control. JF - Ann Neurol Y1 - 2010 A1 - Vansteensel, Mariska J A1 - Hermes, Dora A1 - Aarnoutse, Erik J A1 - Bleichner, Martin G A1 - Gerwin Schalk A1 - van Rijen, Peter C A1 - Leijten, Frans S S A1 - Ramsey, Nick F KW - Cognition KW - Computers KW - Electrodes KW - Electroencephalography KW - Epilepsy KW - Humans KW - Image Processing, Computer-Assisted KW - Magnetic Resonance Imaging KW - Neuropsychological Tests KW - Oxygen KW - Prefrontal Cortex KW - Psychomotor Performance KW - Spectrum Analysis KW - Time Factors KW - User-Computer Interface AB -

OBJECTIVE: 

Brain-computer interfaces (BCIs) translate deliberate intentions and associated changes in brain activity into action, thereby offering patients with severe paralysis an alternative means of communication with and control over their environment. Such systems are not available yet, partly due to the high performance standard that is required. A major challenge in the development of implantable BCIs is to identify cortical regions and related functions that an individual can reliably and consciously manipulate. Research predominantly focuses on the sensorimotor cortex, which can be activated by imagining motor actions. However, because this region may not provide an optimal solution to all patients, other neuronal networks need to be examined. Therefore, we investigated whether the cognitive control network can be used for BCI purposes. We also determined the feasibility of using functional magnetic resonance imaging (fMRI) for noninvasive localization of the cognitive control network.

METHODS: 

Three patients with intractable epilepsy, who were temporarily implanted with subdural grid electrodes for diagnostic purposes, attempted to gain BCI control using the electrocorticographic (ECoG) signal of the left dorsolateral prefrontal cortex (DLPFC).

RESULTS: 

All subjects quickly gained accurate BCI control by modulation of gamma-power of the left DLPFC. Prelocalization of the relevant region was performed with fMRI and was confirmed using the ECoG signals obtained during mental calculation localizer tasks.

INTERPRETATION: 

The results indicate that the cognitive control network is a suitable source of signals for BCI applications. They also demonstrate the feasibility of translating understanding about cognitive networks derived from functional neuroimaging into clinical applications.

VL - 67 UR - http://www.ncbi.nlm.nih.gov/pubmed/20517943 IS - 6 ER - TY - Generic T1 - Detection of spontaneous class-specific visual stimuli with high temporal accuracy in human electrocorticography. T2 - Conf Proc IEEE Eng Med Biol Soc Y1 - 2009 A1 - Miller, John W A1 - Hermes, Dora A1 - Gerwin Schalk A1 - Ramsey, Nick F A1 - Jagadeesh, Bharathi A1 - den Nijs, Marcel A1 - Ojemann, J G A1 - Rao, Rajesh P N KW - Algorithms KW - Electrocardiography KW - Evoked Potentials, Visual KW - Humans KW - Male KW - Pattern Recognition, Automated KW - Pattern Recognition, Visual KW - Photic Stimulation KW - Reproducibility of Results KW - Sensitivity and Specificity KW - User-Computer Interface KW - Visual Cortex AB - Most brain-computer interface classification experiments from electrical potential recordings have been focused on the identification of classes of stimuli or behavior where the timing of experimental parameters is known or pre-designated. Real world experience, however, is spontaneous, and to this end we describe an experiment predicting the occurrence, timing, and types of visual stimuli perceived by a human subject from electrocorticographic recordings. All 300 of 300 presented stimuli were correctly detected, with a temporal precision of order 20 ms. The type of stimulus (face/house) was correctly identified in 95% of these cases. There were approximately 20 false alarm events, corresponding to a late 2nd neuronal response to a previously identified event. JF - Conf Proc IEEE Eng Med Biol Soc VL - 2009 ER -