Neural control of computer cursor velocity by decoding motor cortical spiking activity in humans with tetraplegia

By Sung-Phil Kim, John D. Simeral, Leigh R. Hochberg, John P. Donoghue, Michael J. Black. Journal of Neural Engineering, 2008

423 citations recorded by the source corpus.

Computer-mediated connections between human motor cortical neurons and assistive devices promise to improve or restore lost function in people with paralysis. Recently, a pilot clinical study of an intracortical neural interface system demonstrated that a tetraplegic human was able to obtain continuous two-dimensional control of a computer cursor using neural activity recorded from his motor cortex. This control, however, was not sufficiently accurate for reliable use in many common computer control tasks. Here, we studied several central design choices for such a system including the kinematic representation for cursor movement, the decoding method that translates neuronal ensemble spiking activity into a control signal and the cursor control task used during training for optimizing the parameters of the decoding method. In two tetraplegic participants, we found that controlling a cursor's velocity resulted in more accurate closed-loop control than controlling its position directly and that cursor velocity control was achieved more rapidly than position control. Control quality was further improved over conventional linear filters by using a probabilistic method, the Kalman filter, to decode human motor cortical activity. Performance assessment based on standard metrics used for the evaluation of a wide range of pointing devices demonstrated significantly improved cursor control with velocity rather than position decoding.

Subfields: Biomedical Engineering, Cellular and Molecular Neuroscience, Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/c3d16362e617d504581646110678e09083c2fa2a

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Citations in the corpus

Brain-Computer Interfaces Using Sensorimotor Rhythms: Current State and Future Perspectives (2014, positive): “3-D movement control [15]–[19], with DOF and performance comparable to studies using intracortical single units recordings [9], [10].”
Reach and grasp by people with tetraplegia using a neurally controlled robotic arm (2012, positive): “These findings extend our previous demonstrations of point and click neural control by people with tetraplegia 7,16 and show that neural spiking activity recorded from a small MI intracortical array contains sufficient information to allow people with longstanding tetraplegia to perform even more…”
A comprehensive review of EEG-based brain–computer interface paradigms (2019, positive): “Imagined body kinematics (IBK) is a motor imagery paradigm that originated from invasive BCI technology [9, 10].”
Long-term stability of neural prosthetic control signals from silicon cortical arrays in rhesus macaque motor cortex (2011, background): “The Utah array, which is a monolithic array of 100 silicon microelectrodes, is particularly important to understand because it has been FDA approved for use in human patients [8, 9, 30].”
Virtual typing by people with tetraplegia using a self-calibrating intracortical brain-computer interface (2015, methodology): “In closedloop neural control, intended movements were decoded from the incoming neural features and translated in real time into the movement of the cursor using a steady-state Kalman filter (5, 46, 47).”