@article {2191, title = {Evolution of brain-computer interfaces: going beyond classic motor physiology.}, journal = {Neurosurg Focus}, volume = {27}, year = {2009}, month = {07/2009}, pages = {E4}, abstract = {

The notion that a computer can decode brain signals to infer the intentions of a human and then enact those intentions directly through a machine is becoming a realistic technical possibility. These types of devices are known as brain-computer interfaces (BCIs). The evolution of these neuroprosthetic technologies could have significant implications for patients with motor disabilities by enhancing their ability to interact and communicate with their environment. The cortical physiology most investigated and used for device control has been brain signals from the primary motor cortex. To date, this classic motor physiology has been an effective substrate for demonstrating the potential efficacy of BCI-based control. However, emerging research now stands to further enhance our understanding of the cortical physiology underpinning human intent and provide further signals for more complex brain-derived control. In this review, the authors report the current status of BCIs and detail the emerging research trends that stand to augment clinical applications in the future.

}, keywords = {Brain, Cerebral Cortex, Humans, Man-Machine Systems, Motor Cortex, Movement, Movement Disorders, Neuronal Plasticity, Prostheses and Implants, Research, Signal Processing, Computer-Assisted, User-Computer Interface}, issn = {1092-0684}, doi = {10.3171/2009.4.FOCUS0979}, url = {http://www.ncbi.nlm.nih.gov/pubmed/19569892}, author = {Leuthardt, E C and Gerwin Schalk and Roland, Jarod and Rouse, Adam and Moran, D} } @article {2179, title = {Electrocorticographic Frequency Alteration Mapping: A Clinical Technique for Mapping the Motor Cortex.}, journal = {Neurosurgery}, volume = {60}, year = {2007}, month = {04/2007}, pages = {260-70; discussion 270-1}, abstract = {

OBJECTIVE:\ 

Electrocortical stimulation (ECS) has been well established for delineating the eloquent cortex. However, ECS is still coarse and inefficient in delineating regions of the functional cortex and can be hampered by after-discharges. Given these constraints, an adjunct approach to defining the motor cortex is the use of electrocorticographic signal changes associated with active regions of the cortex. The broad range of frequency oscillations are categorized into two main groups with respect to the sensorimotor cortex: low and high frequency bands. The low frequency bands tend to show a power reduction with cortical activation, whereas the high frequency bands show power increases. These power changes associated with the activated cortex could potentially provide a powerful tool in delineating areas of the motor cortex. We explore electrocorticographic signal alterations as they occur with activated regions of the motor cortex, as well as its potential in clinical brain mapping applications.

METHODS:\ 

We evaluated seven patients who underwent invasive monitoring for seizure localization. Each patient had extraoperative ECS mapping to identify the motor cortex. All patients also performed overt hand and tongue motor tasks to identify associated frequency power changes in regard to location and degree of concordance with ECS results that localized either hand or tongue motor function.

RESULTS:\ 

The low frequency bands had a high sensitivity (88.9-100\%) and a lower specificity (79.0-82.6\%) for identifying electrodes with either hand or tongue ECS motor responses. The high frequency bands had a lower sensitivity (72.7-88.9\%) and a higher specificity (92.4-94.9\%) in correlation with the same respective ECS positive electrodes.

CONCLUSION:\ 

The concordance between stimulation and spectral power changes demonstrate the possible utility of electrocorticographic frequency alteration mapping as an adjunct method to improve the efficiency and resolution of identifying the motor cortex.

}, keywords = {Adult, Biological Clocks, Brain Mapping, Electric Stimulation, Electrodes, Implanted, Electroencephalography, Female, Hand, Humans, Male, Middle Aged, Motor Cortex, Oscillometry, Signal Processing, Computer-Assisted, Tongue}, issn = {1524-4040}, doi = {10.1227/01.NEU.0000255413.70807.6E}, url = {http://www.ncbi.nlm.nih.gov/pubmed/17415162}, author = {Leuthardt, E C and Miller, John W and Nicholas R Anderson and Gerwin Schalk and Dowling, Joshua and Miller, John W and Moran, D and Ojemann, J G} }