@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 {2182, title = {Decoding two-dimensional movement trajectories using electrocorticographic signals in humans.}, journal = {J Neural Eng}, volume = {4}, year = {2007}, month = {09/2007}, pages = {264-75}, abstract = {

Signals from the brain could provide a non-muscular communication and control system, a brain-computer interface (BCI), for people who are severely paralyzed. A common BCI research strategy begins by decoding kinematic parameters from brain signals recorded during actual arm movement. It has been assumed that these parameters can be derived accurately only from signals recorded by intracortical microelectrodes, but the long-term stability of such electrodes is uncertain. The present study disproves this widespread assumption by showing in humans that kinematic parameters can also be decoded from signals recorded by subdural electrodes on the cortical surface (ECoG) with an accuracy comparable to that achieved in monkey studies using intracortical microelectrodes. A new ECoG feature labeled the local motor potential (LMP) provided the most information about movement. Furthermore, features displayed cosine tuning that has previously been described only for signals recorded within the brain. These results suggest that ECoG could be a more stable and less invasive alternative to intracortical electrodes for BCI systems, and could also prove useful in studies of motor function.

}, keywords = {Adult, Algorithms, Arm, Brain Mapping, Cerebral Cortex, Electroencephalography, Evoked Potentials, Motor, Female, Humans, Male, Movement}, issn = {1741-2560}, doi = {10.1088/1741-2560/4/3/012}, url = {http://www.ncbi.nlm.nih.gov/pubmed/17873429}, author = {Gerwin Schalk and Kub{\'a}nek, J and Miller, John W and Nicholas R Anderson and Leuthardt, E C and Ojemann, J G and Limbrick, D and Moran, D and Lester A Gerhardt and Jonathan Wolpaw} }