Long-Term Asynchronous Decoding of Arm Motion Using Electrocorticographic Signals in Monkeys

By Zenas C. Chao, Y. Nagasaka, N. Fujii. Frontiers in Neuroengineering, 2009

364 citations recorded by the source corpus.

Brain–machine interfaces (BMIs) employ the electrical activity generated by cortical neurons directly for controlling external devices and have been conceived as a means for restoring human cognitive or sensory-motor functions. The dominant approach in BMI research has been to decode motor variables based on single-unit activity (SUA). Unfortunately, this approach suffers from poor long-term stability and daily recalibration is normally required to maintain reliable performance. A possible alternative is BMIs based on electrocorticograms (ECoGs), which measure population activity and may provide more durable and stable recording. However, the level of long-term stability that ECoG-based decoding can offer remains unclear. Here we propose a novel ECoG-based decoding paradigm and show that we have successfully decoded hand positions and arm joint angles during an asynchronous food-reaching task in monkeys when explicit cues prompting the onset of movement were not required. Performance using our ECoG-based decoder was comparable to existing SUA-based systems while evincing far superior stability and durability. In addition, the same decoder could be used for months without any drift in accuracy or recalibration. These results were achieved by incorporating the spatio-spectro-temporal integration of activity across multiple cortical areas to compensate for the lower fidelity of ECoG signals. These results show the feasibility of high-performance, chronic and versatile ECoG-based neuroprosthetic devices for real-life applications. This new method provides a stable platform for investigating cortical correlates for understanding motor control, sensory perception, and high-level cognitive processes.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/95531830133d01f4f832a4268b36f2ce044cd716

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Citations in the corpus

Long-term stability of neural prosthetic control signals from silicon cortical arrays in rhesus macaque motor cortex (2011, background): “parameters [51, 52] for up to 8 months [53], but this may”
Brain-computer interfaces in medicine. (2012, positive): “A study in monkeys found that ECoG recordings and the performance of the model used to decode movement remained stable over several months.(101,102) These results suggest that ECoG is likely to prove practical for long-term BCI use.”
Brain Computer Interfaces, a Review (2012, background): “More recent experiments with monkeys have shown that ECoG can perform at a high level for months without any drift in accuracy or recalibration [63].”
Flexible, Foldable, Actively Multiplexed, High-Density Electrode Array for Mapping Brain Activity in vivo (2011, background): “Highly flexible arrays of subdural electrodes have unique advantages over penetrating microelectrode arrays in that they are able to maintain signal quality over extended periods of time with minimized irritation and injury to brain tissue”
Brain-Machine Interfaces: From Basic Science to Neuroprostheses and Neurorehabilitation. (2017, background): “Chronic ECoG recordings can last many years in animals (105, 123, 131, 508, 682) and humans (845, 870).”