Neurologic disease with loss of motor function is a major health burden. Brain-computer interfaces (BCI) are systems that use brain signals to power an external device, such as a communication board or a prosthetic device, which may help people with loss of motor function. Electrocorticography (ECoG) has been used to decode hand movements and as a control signal for brain-computer interface (BCI). This study hopes to use a smaller spacing of ECoG to see if a better motor signal can be found and used as a BCI control signal.

Stroke, spinal cord injury, extremity injury and degenerative/locked-in syndromes are among those conditions that may benefit from sustainable neuroprosthetic options. The investigators have studied human motor cortex and related cortical areas with direct brain recording (electrocorticography or ECoG) as a signal for motor neuroprosthetics. Completing exciting studies in humans with local fields using intracortical electrodes and long-term working brain-computer interfaces with EEG, electrocorticography surveys an intermediate level of spatial specificity and may have durability in long-term recordings. ECoG signals could ultimately be obtained epidurally or even more superficially if the exact signals were better understood. To date, the investigators have demonstrated that ECoG signals from motor cortex can be used to decode movement and have a precision using clinic arrays (1 cm resolution) that can decode hand movement and allow for the separation of digit movement. These signals have been used for brain-computer interface and can be used to control a prosthetic hand in humans.

Electrocorticography (ECoG) is the recording of brain signals directly from the cortical surface. In patients undergoing surgical treatment of epilepsy, these signals have been available and have shown to be rich sources of motor-related signals that can drive a hand neuroprosthetic as part of a brain-computer interface (BCI). Though the clinically available resolution of 1 cm allows for separation of different types of finger movement by using the high-frequency characteristics of the ECoG recording (70-100Hz), higher spatial resolutions (3mm) increases the ability to decode finger movements and more complicated hand movements, such as grasping of different objects. Ideal resolution is one of the several gaps in knowledge limit pursuing implementation of ECoG-based BCI along with uncertainty about the longevity of ECoG signals and human implementation of feedback directly to cortex through electrical stimulation.

Specific Aim:

Higher resolution arrays over subacute (1 week) time frame to allow for adaptation and BCI use of the higher resolution signals. An 8x8 array of 3mm resolution will be placed over sensorimotor cortex. Grasp synergies will be determined and mapped onto the electrodes to determine control channels for each synergy. Control of multiple synergies will move a simulated robotic hand to a visually cued target shape

1. Hand synergies will be independently mapped onto the 3mm x 3mm (microarray) with at least one independent electrode for each of the first three synergies
2. Using the signals from the microarray, participants will correctly move the robotic hand into one of 6 target postures with 50% accuracy.

Inclusion Criteria:

• Must be able to speak and read English
• Cognitive ability to follow study directions
• Patients that are scheduled to undergo grid placement clinically for treatment of
• intractable epilepsy
• 18 years of age or older

Exclusion Criteria:

• Patients who are not considered candidates for epilepsy surgery
• Individuals who have a diagnosis that would not allow them to participate in research procedures. For example, a physical disability that would limit hand movements and range of motion.

Patients with intractable epilepsy ages 18 and above scheduled to undergo grid placement for epilepsy treatment.