@article {2194, title = {Brain-computer interfacing based on cognitive control.}, journal = {Ann Neurol}, volume = {67}, year = {2010}, month = {06/2010}, pages = {809-16}, abstract = {

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.

}, keywords = {Cognition, Computers, Electrodes, Electroencephalography, Epilepsy, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Neuropsychological Tests, Oxygen, Prefrontal Cortex, Psychomotor Performance, Spectrum Analysis, Time Factors, User-Computer Interface}, issn = {1531-8249}, doi = {10.1002/ana.21985}, url = {http://www.ncbi.nlm.nih.gov/pubmed/20517943}, author = {Vansteensel, Mariska J and Hermes, Dora and Aarnoutse, Erik J and Bleichner, Martin G and Gerwin Schalk and van Rijen, Peter C and Leijten, Frans S S and Ramsey, Nick F} } @article {2190, title = {Using an EEG-based brain-computer interface for virtual cursor movement with BCI2000.}, journal = {J Vis Exp}, year = {2009}, month = {07/2009}, abstract = {

A brain-computer interface (BCI) functions by translating a neural signal, such as the electroencephalogram (EEG), into a signal that can be used to control a computer or other device. The amplitude of the EEG signals in selected frequency bins are measured and translated into a device command, in this case the horizontal and vertical velocity of a computer cursor. First, the EEG electrodes are applied to the user s scalp using a cap to record brain activity. Next, a calibration procedure is used to find the EEG electrodes and features that the user will learn to voluntarily modulate to use the BCI. In humans, the power in the mu (8-12 Hz) and beta (18-28 Hz) frequency bands decrease in amplitude during a real or imagined movement. These changes can be detected in the EEG in real-time, and used to control a BCI ([1],[2]). Therefore, during a screening test, the user is asked to make several different imagined movements with their hands and feet to determine the unique EEG features that change with the imagined movements. The results from this calibration will show the best channels to use, which are configured so that amplitude changes in the mu and beta frequency bands move the cursor either horizontally or vertically. In this experiment, the general purpose BCI system BCI2000 is used to control signal acquisition, signal processing, and feedback to the user [3].

}, keywords = {Brain, Calibration, Electrodes, Electroencephalography, Humans, User-Computer Interface}, issn = {1940-087X}, doi = {10.3791/1319}, url = {http://www.ncbi.nlm.nih.gov/pubmed/19641479}, author = {Adam J Wilson and Gerwin Schalk and Walton, L{\'e}o M and Williams, Justin C} } @conference {3438, title = {Three cases of feature correlation in an electrocorticographic BCI.}, booktitle = {Engineering in Medicine and Biology Society, 2008.}, year = {2008}, month = {08/2008}, publisher = {IEEE}, organization = {IEEE}, address = {Vancouver, BC}, abstract = {Three human subjects participated in a closed-loop brain computer interface cursor control experiment mediated by implanted subdural electrocorticographic arrays. The paradigm consisted of several stages: baseline recording, hand and tongue motor tasks as the basis for feature selection, two closed-loop one-dimensional feedback experiments with each of these features, and a two-dimensional feedback experiment using both of the features simultaneously. The two selected features were simple channel and frequency band combinations associated with change during hand and tongue movement. Inter-feature correlation and cross-correlation between features during different epochs of each task were quantified for each stage of the experiment. Our anecdotal, three subject, result suggests that while high correlation between horizontal and vertical control signal can initially preclude successful two-dimensional cursor control, a feedback-based learning strategy can be successfully employed by the subject to overcome this limitation and progressively decorrelate these control signals.}, keywords = {Adolescent, Adult, Algorithms, automated pattern recognition, control systems, decorrelation, Electrocardiography, Electrodes, Electroencephalography, evoked motor potentials, Feedback, Female, frequency, hospitals, Humans, Male, Middle Aged, Motor Cortex, Signal Processing, Statistics as Topic, Task Performance and Analysis, Tongue, User-Computer Interface}, doi = {10.1109/IEMBS.2008.4650415}, url = {http://www.ncbi.nlm.nih.gov/pubmed/19163918}, author = {Miller, Kai J and Blakely, Timothy and Gerwin Schalk and den Nijs, Marcel and Rao, Rajesh PN and Ojemann, Jeffrey G} } @article {2106, title = {Analysis of the correlation between local field potentials and neuronal firing rate in the motor cortex.}, journal = {Conf Proc IEEE Eng Med Biol Soc}, volume = {1}, year = {2006}, month = {09/2006}, pages = {6185-8}, abstract = {

Neuronal firing rate has been the signal of choice for invasive motor\ brain\ machine\ interfaces\ (BMI). The use of local field potentials (LFP) in BMI experiments may provide additional dendritic information about movement intent and may improve performance. Here we study the time-varying amplitude modulated relationship between local field potentials (LFP) and single unit activity (SUA) in the motor cortex. We record LFP and SUA in the primary motor cortex of rats trained to perform a lever pressing task, and evaluate the correlation between pairs of peri-event time histograms (PETH) and movement evoked local field potentials (mEP) at the same electrode. Three different correlation coefficients were calculated and compared between the neuronal PETH and the magnitude and power of the mEP. Correlation as high as 0.7 for some neurons occurred between the PETH and the mEP magnitude. As expected, the correlations between the single trial LFP and SUV are much lower due to the inherent variability of both signals.

}, keywords = {Action Potentials, Animals, Brain, Brain Mapping, Electric Stimulation, Electrodes, Evoked Potentials, Motor, Male, Models, Statistical, Motor Cortex, Neurons, Rats, Rats, Sprague-Dawley, Signal Processing, Computer-Assisted, Synaptic Transmission}, issn = {1557-170X}, doi = {10.1109/IEMBS.2006.260516}, url = {http://www.ncbi.nlm.nih.gov/pubmed/17946745}, author = {Wang, Yiwen and Sanchez, Justin C and Principe, Jose and Mitzelfelt, Jeremiah D and Gunduz, Aysegul} }