TY - JOUR T1 - Decoding flexion of individual fingers using electrocorticographic signals in humans. JF - J Neural Eng Y1 - 2009 A1 - Kubánek, J A1 - Miller, John W A1 - Ojemann, J G A1 - Jonathan Wolpaw A1 - Gerwin Schalk KW - Adolescent KW - Adult KW - Biomechanics KW - Brain KW - Electrodiagnosis KW - Epilepsy KW - Female KW - Fingers KW - Humans KW - Male KW - Microelectrodes KW - Middle Aged KW - Motor Activity KW - Rest KW - Thumb KW - Time Factors KW - Young Adult AB -

Brain signals can provide the basis for a non-muscular communication and control system, a brain-computer interface (BCI), for people with motor disabilities. A common approach to creating BCI devices is to decode kinematic parameters of movements using signals recorded by intracortical microelectrodes. Recent studies have shown that kinematic parameters of hand movements can also be accurately decoded from signals recorded by electrodes placed on the surface of the brain (electrocorticography (ECoG)). In the present study, we extend these results by demonstrating that it is also possible to decode the time course of the flexion of individual fingers using ECoG signals in humans, and by showing that these flexion time courses are highly specific to the moving finger. These results provide additional support for the hypothesis that ECoG could be the basis for powerful clinically practical BCI systems, and also indicate that ECoG is useful for studying cortical dynamics related to motor function.

VL - 6 UR - http://www.ncbi.nlm.nih.gov/pubmed/19794237 IS - 6 ER - TY - JOUR T1 - Decoding two-dimensional movement trajectories using electrocorticographic signals in humans. JF - J Neural Eng Y1 - 2007 A1 - Gerwin Schalk A1 - Kubánek, J A1 - Miller, John W A1 - Nicholas R Anderson A1 - Leuthardt, E C A1 - Ojemann, J G A1 - Limbrick, D A1 - Moran, D A1 - Lester A Gerhardt A1 - Jonathan Wolpaw KW - Adult KW - Algorithms KW - Arm KW - Brain Mapping KW - Cerebral Cortex KW - Electroencephalography KW - Evoked Potentials, Motor KW - Female KW - Humans KW - Male KW - Movement AB -

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.

VL - 4 UR - http://www.ncbi.nlm.nih.gov/pubmed/17873429 IS - 3 ER -