The Department of Neurological Surgery is currently conducting the following studies in Brain-Computer Interface:
Computer Interfaces for Functional Recovery from Brain Injury
Principal Investigators: Jeff Ojemann, MD and Eberhard Fetz, PhD
Funded By: Life Sciences Discovery Fund
Focus: To increase functional recovery in damaged areas of the brain using implantable electrical stimulators.
Recovery from brain damage due to a stroke, traumatic brain injury or epilepsy presents a struggle for thousands of patients in Washington State and millions of people throughout the world. Electrical stimulation of the brain, which promotes long-term changes in nerve activity, could potentially be a new treatment for advancing recovery in individuals suffering from brain impairment.
The investigators used implantable electric stimulators to more effectively and continuously stimulate the brain's outer region or cortex. The team worked to develop and test minimally invasive techniques meant to enhance the clinical application of the implantable electric stimulators. Parallel studies were conducted in animal models and human subjects using a neurochip to develop more effective delivery of therapeutic brain stimulation. The potential for translation and use in clinical settings could have a wide-ranging impact for Washingtonians with neurological disorders and those recovering from, or managing, chronic brain-deterioration disease.
Electrocorticography Signals for Human Hand Prosthetics
Principal Investigator: Jeffrey Ojemann, MD
Funded By: National Institutes of Health (NIH)
Neurological injury (such as from stroke, traumatic brain injury, and spinal cord injury) is a major cause of permanent disability. Major advances have been made in the field of neuroprosthetics, including the development of brain-machine interfaces that hold enormous potential for restoring neurological function. Interfaces based directly from brain signals may allow for direct decoding of control signals for maximally efficient prosthetics.
In this project, we examine the brain signals underlying hand movment using recordings directly from the surface of the brain (electrocorticography, or ECoG). We have previously shown that high frequency (>75Hz) components carry information about local activity. In the first aim, we will show high-frequency signals that correlate with individual finger movements. We will extract broadband changes in ECoG from non-specific alpha and beta rhythms using PCA and enhance finger classification with machine learning algorithms. In the second aim, we will examine whether the ideal control signals are organized by different hand functions, rather than movement of different fingers. For instance, we will examine if pinch and grasp behaviors give more separable high-frequency ECoG signals.
We will also examine the correlation between power across premotor and motor areas with different behaviors. We will use non-linear signal analyses to discover novel interactions between motor areas. In the third aim, we will measure ECoG changes associated with planning of movement and imagined movement. In the final aim, we will apply these signals to a robotic hand and provide the subjects with both visual and tactile feedback to optimize ECoG-based control of a hand prosthesis. By increasingly advancing the complexity of the control signal, and the complexity of the robotic hand output, we will establish if ECoG is a viable source of control signal for a hand neuroprosthetic device.