Extracting kinetic information from human motor cortical signals

Flint, Robert D.; Wang, Po T.; Wright, Zachary A.; King, Christine; Krucoff, Max O.; Schuele, Stephan; Rosenow, Joshua M.; Hsu, Frank P.K.; Liu, Charles Yu; Lin, Jack J.; Sazgar, Mona; Millett, David; Shaw, Susan; Nenadic, Zoran; An, Hongyu; Slutzky, Marc W.

doi:10.1016/j.neuroimage.2014.07.049

Cited by 71 publications

(99 citation statements)

References 58 publications

(81 reference statements)

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“…The rich information in the gamma band was consistent with prior studies of grasp decoding using ECoG from human motor cortex (Flint et al, 2012, 2014; Pistohl et al, 2012). Changes in beta activity also contributed to the prediction of the force profile and inclusion of beta activity improved the encoding accuracy.…”

Section: Discussionsupporting

confidence: 87%

“…This extends the earlier observation that frequency-specific LFP activities in the STN correlate with force-related variables in manual grips (Anzak et al, 2012; Tan et al, 2013); and that beta and gamma activities can be considered complementary non-linear correlates of force in gripping, and when combined, afford a measure that linearly correlates with force across all effort levels. Compared to other more data-driven methods based on Wiener filter for decoding force (Flint et al, 2014), the first order linear dynamic model proposed here simulates how the musculoskeletal plant responds to the control signal from the brain. This offers more insight in to how the basal ganglia encodes gripping force, and provides a framework to further investigate and explain the pathophysiology of motor impairment in Parkinson’s disease.…”

Section: Discussionmentioning

confidence: 99%

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Decoding gripping force based on local field potentials recorded from subthalamic nucleus in humans

Tan

Pogosyan

Ashkan

et al. 2016

eLife

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The basal ganglia are known to be involved in the planning, execution and control of gripping force and movement vigour. Here we aim to define the nature of the basal ganglia control signal for force and to decode gripping force based on local field potential (LFP) activities recorded from the subthalamic nucleus (STN) in patients with deep brain stimulation (DBS) electrodes. We found that STN LFP activities in the gamma (55–90 Hz) and beta (13–30m Hz) bands were most informative about gripping force, and that a first order dynamic linear model with these STN LFP features as inputs can be used to decode the temporal profile of gripping force. Our results enhance the understanding of how the basal ganglia control gripping force, and also suggest that deep brain LFPs could potentially be used to decode movement parameters related to force and movement vigour for the development of advanced human-machine interfaces.DOI: http://dx.doi.org/10.7554/eLife.19089.001

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Section: Discussionsupporting

confidence: 87%

Section: Discussionmentioning

confidence: 99%

Decoding gripping force based on local field potentials recorded from subthalamic nucleus in humans

Tan

Pogosyan

Ashkan

et al. 2016

eLife

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“…Recently, it was shown that high gamma signals can be used to decode highly fractionated movements, for example in biomimetic BMIs (Flint et al, 2013). Such signals are most effectively obtained using invasive recordings with intracortical, subdural or epidural electrodes (Mehring et al, 2004;Stark and Abeles, 2007;Zhuang et al, 2010;Slutzky et al, 2010Slutzky et al, , 2011Flint et al, 2014). While intracranial electrodes require implantation, epidural or subdural electrodes could ultimately be implanted through a burr hole instead of a craniotomy reducing the perioperative risk and cost.…”

Section: Current Challenges and Future Developmentsmentioning

confidence: 99%

Brain–machine interfaces in neurorehabilitation of stroke

Soekadar

Birbaumer

Slutzky

et al. 2015

Neurobiology of Disease

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180

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Stroke is among the leading causes of long-term disabilities leaving an increasing number of people with cognitive, affective and motor impairments depending on assistance in their daily life. While function after stroke can significantly improve in the first weeks and months, further recovery is often slow or non-existent in the more severe cases encompassing 30-50% of all stroke victims. The neurobiological mechanisms underlying recovery in those patients are incompletely understood. However, recent studies demonstrated the brain's remarkable capacity for functional and structural plasticity and recovery even in severe chronic stroke. As all established rehabilitation strategies require some remaining motor function, there is currently no standardized and accepted treatment for patients with complete chronic muscle paralysis. The development of brain-machine interfaces (BMIs) that translate brain activity into control signals of computers or external devices provides two new strategies to overcome stroke-related motor paralysis. First, BMIs can establish continuous high-dimensional brain-control of robotic devices or functional electric stimulation (FES) to assist in daily life activities (assistive BMI). Second, BMIs could facilitate neuroplasticity, thus enhancing motor learning and motor recovery (rehabilitative BMI). Advances in sensor technology, development of non-invasive and implantable wireless BMI-systems and their combination with brain stimulation, along with evidence for BMI systems' clinical efficacy suggest that BMI-related strategies will play an increasing role in neurorehabilitation of stroke.

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“…By leveraging the ability to decode limb kinematics (Georgopoulos et al, 1982;Moran and Schwartz, 1999) or kinetics (Evarts, 1968;Fagg et al, 2007;Flint et al, 2014) from cortical activity, BMIs have the potential to restore lost function to individuals with paralysis (Hochberg et al, 2006;Collinger et al, 2013;Ethier et al, 2012). However, single-neuron action potentials are difficult to record over long time scales with silicon-based arrays and those that are recorded over long times can exhibit substantial nonstationarity in their amplitudes and waveforms (Chestek et al, 2011;Simeral et al, 2011).…”

Section: Implications For Decoding Motor Intent Over Long Time Periodsmentioning

confidence: 99%

Long-Term Stability of Motor Cortical Activity: Implications for Brain Machine Interfaces and Optimal Feedback Control

Flint¹,

Scheid²,

Wright³

et al. 2016

J. Neurosci.

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The human motor system is capable of remarkably precise control of movements-consider the skill of professional baseball pitchers or surgeons. This precise control relies upon stable representations of movements in the brain. Here, we investigated the stability of cortical activity at multiple spatial and temporal scales by recording local field potentials (LFPs) and action potentials (multiunit spikes, MSPs) while two monkeys controlled a cursor either with their hand or directly from the brain using a brain-machine interface. LFPs and some MSPs were remarkably stable over time periods ranging from 3 d to over 3 years; overall, LFPs were significantly more stable than spikes. We then assessed whether the stability of all neural activity, or just a subset of activity, was necessary to achieve stable behavior. We showed that projections of neural activity into the subspace relevant to the task (the "task-relevant space") were significantly more stable than were projections into the task-irrelevant (or "task-null") space. This provides cortical evidence in support of the minimum intervention principle, which proposes that optimal feedback control (OFC) allows the brain to tightly control only activity in the task-relevant space while allowing activity in the taskirrelevantspacetovarysubstantiallyfromtrialtotrial.Wefoundthatthebrainappearscapableofmaintainingstablemovementrepresentations for extremely long periods of time, particularly so for neural activity in the task-relevant space, which agrees with OFC predictions.

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Extracting kinetic information from human motor cortical signals

Cited by 71 publications

References 58 publications

Decoding gripping force based on local field potentials recorded from subthalamic nucleus in humans

Decoding gripping force based on local field potentials recorded from subthalamic nucleus in humans

Brain–machine interfaces in neurorehabilitation of stroke

Long-Term Stability of Motor Cortical Activity: Implications for Brain Machine Interfaces and Optimal Feedback Control

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