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Brain-Computer Interfaces

& Neuroprostheses
Noninvasive EEG
By modulating changes in their electroencephalographic (EEG) activity, BCI users have demonstrated two-dimensional cursor control and the ability to type out messages on a virtual keyboard. Our work looks at how an EEG-based BCI can also lead to effective closed-loop neural control of advanced prosthetic devices.Our EEG-based BCI uses motor imagery (i.e. imagined motor movements), which result in an event-related desynchronization (ERD) in spectral power over the Mu band (8-12 Hz) over the sensorimotor cortex.
Subjects learn to modulate their Mu-band power to create a 1-D control signal, and can voluntarily use left hand motor imagery to open the prosthetic hand, right hand motor imagery to close the prosthetic hand, and relaxation to keep the hand still.
While the impetus behind BCI research has been toward assistance for quadriplegics, our application is unique in its focus on Spino-Cerebellar Ataxia (SCA) sufferers, who are able to perform motor movements, but due to selective degeneration of the cerebellum are deficient in precise control of motor movements. Due to their capabilities to perform movements, the importance in restoring motor function is obscured, but the quality of this control is seriously deficient in sufficiently progressed patients, necessitating a solution outside the typical motor control pathway.
Ataxia patients (middle) have selective degeneration of their cerebellum, resulting in an inability to execute precise motor movements. While traditional application of BCI has focused on those unable to perform motor movements at all due to failure to send motor commands to the muscles entirely (left), in ataxia patients these commands are still present, but incorrect. Subjects performing imagined movements during an online BCI task are compared, revealing that while ataxia patients can control a BCI, their method of control is different in both localization of mu signal, as well as duration and power (right).
We are also developing algorithms to improve accuracy, decrease training time, and provide seamless real-time control.
Geoffrey Newman, BSE
Sarah Ying, MD, Johns Hopkins University Department of Radiology
Dankmeyer Orthotics and Prosthetics
Martin Bionics, LLC (acquired by OrthoCare Innovations, LLC)
Defense Advanced Project and Research Agency (DARPA) - contract N66001-06-C-8005
US National Science Foundation, Cyber Enabled Discovery and Innovation (CDI) program, grant numbers ECCS 0835632 and ECCS 0835554

Ying S, Newman G, Choi Y-S, Kim H-N, Presacco A, Kothare M, Thakor N, Cerebellar Ataxia Patients are Able to Use Motor Imagery to Modulate Mu-Band Power in a Pilot Study of EEG-based Brain-Computer Interface Control, Conf Proc IEEE Eng Med Biol Soc Neural Eng, in press, 2011

Liu R, Newman GI, Ying SH, Thakor NV, Improved BCI Performance with Sequential Hypothesis Testing, Conf Proc IEEE Eng Med Biol Soc, in press, 2011

Chatterjee A, Aggarwal V, Ramos A, Acharya S, Thakor NV, A brain-computer interface with vibrotactile biofeedback for haptic information, J Neuroeng Rehabil, 4(40), 2007

Ramos Murguialday A, Aggarwal V, Chatterjee A, Cho Y, Rasmussen R, O’Rourke B, Acharya S, Thakor NV, Brain-Computer Interface for a prosthetic hand using local machine control and haptic feedback, Conf Proc Int Conf on Rehab Robotic, 1:609-613, 2007

Chatterjee A, Aggarwal V, Ramos A, Acharya S, Thakor NV, Operation of a Brain-Computer Interface using vibrotactile biofeedback, Conf Proc IEEE Eng Med Biol Soc on Neural Eng, 1:171-174, 2007
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