Long term, stable brain machine interface performance using local field potentials and multiunit spikes
TL;DR: It is demonstrated that LFPs can be used in a biomimetic BMI to control a computer cursor, and the results suggest that the monkeys were able to stabilize the relationship between neural activity and cursor movement during online BMI control, despite variability in the relationship Between Neural activity and hand movements.
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Abstract: Objective. Brain machine interfaces (BMIs) have the potential to restore movement to people with paralysis. However, a clinically-viable BMI must enable consistently accurate control over time spans ranging from years to decades, which has not yet been demonstrated. Most BMIs that use single-unit spikes as inputs will experience degraded performance over time without frequent decoder re-training. Two other signals, local field potentials (LFPs) and multi-unit spikes (MSPs), may offer greater reliability over long periods and better performance stability than single-unit spikes. Here, we demonstrate that LFPs can be used in a biomimetic BMI to control a computer cursor. Approach. We implanted two rhesus macaques with intracortical microelectrodes in primary motor cortex. We recorded LFP and MSP signals from the monkeys while they performed a continuous reaching task, moving a cursor to randomly-placed targets on a computer screen. We then used the LFP and MSP signals to construct biomimetic decoders for control of the cursor. Main results. Both monkeys achieved high-performance, continuous control that remained stable or improved over nearly 12 months using an LFP decoder that was not retrained or adapted. In parallel, the monkeys used MSPs to control a BMI without retraining or adaptation and had similar or better performance, and that predominantly remained stable over more than six months. In contrast to their stable online control, both LFP and MSP signals showed substantial variability when used offline to predict hand movements. Significance. Our results suggest that the monkeys were able to stabilize the relationship between neural activity and cursor movement during online BMI control, despite variability in the relationship between neural activity and hand movements.
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Brain–computer interfaces for communication and rehabilitation
TL;DR: The use of BCIs for communication in patients who are paralyzed, particularly those with locked- in syndrome or complete locked-in syndrome as a result of amyotrophic lateral sclerosis is considered, and the use ofBCIs for motor rehabilitation after severe stroke and spinal cord injury is discussed.
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Brain-Machine Interfaces: From Basic Science to Neuroprostheses and Neurorehabilitation
TL;DR: Brain-machine interfaces research has been at the forefront of many neurophysiological discoveries, including the demonstration that, through continuous use, artificial tools can be assimilated by the primate brain's body schema.
551
High performance communication by people with paralysis using an intracortical brain-computer interface
Chethan Pandarinath,Paul Nuyujukian,Christine H Blabe,Brittany L Sorice,Jad Saab,Jad Saab,Francis R. Willett,Francis R. Willett,Leigh R. Hochberg,Krishna V. Shenoy,Jaimie M. Henderson +10 more
TL;DR: A high-performance intracortical BCI for communication is reported, which was tested by three clinical trial participants with paralysis and demonstrates the potential utility of iBCIs as powerful assistive communication devices for people with limited motor function.
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TL;DR: Recent developments in the algorithms that decode motor intention from neuronal activity and in approaches to convey sensory feedback by electrically stimulating neurons have shown the promise of invasive interfaces with sensorimotor cortices, although substantial challenges remain.
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Virtual typing by people with tetraplegia using a self-calibrating intracortical brain-computer interface
Beata Jarosiewicz,Beata Jarosiewicz,Anish A. Sarma,Anish A. Sarma,Daniel Bacher,Nicolas Y. Masse,John D. Simeral,Brittany L Sorice,Erin M. Oakley,Christine H Blabe,Chethan Pandarinath,Vikash Gilja,Vikash Gilja,Sydney S. Cash,Emad N. Eskandar,Gerhard Friehs,Jaimie M. Henderson,Krishna V. Shenoy,John P. Donoghue,Leigh R. Hochberg +19 more
TL;DR: It is demonstrated that signal nonstationarity in an intracortical BCI can be mitigated automatically in software, enabling long periods (hours to days) of self-paced point-and-click typing by people with tetraplegia, without degradation in neural control.
340
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