Cortical neuroprosthetics from a clinical perspective

Tsu, Adelyn; Burish, Mark J.; Godlove, Jason; Ganguly, Karunesh

doi:10.1016/j.nbd.2015.07.015

Cited by 15 publications

(12 citation statements)

References 107 publications

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“…Arm movements accompanying BMI use are not expected in nearer-term clinical BMI systems 1 providing cursor control to people with paralysis or amputation. Nuisance variable mitigations such as Cursor Position Subtraction are therefore unnecessary in these applications, and we recommend against their use because such decoder operations introduce additional free parameters that are subject to neural non-stationarities or estimation errors, as noted in 24,25 .…”

Section: Discussionmentioning

confidence: 99%

“…These systems read out the movement intentions of people with paralysis to restore function 1 , for example by controlling computer cursors for communication 2 , commanding arm and hand movements of robotic limbs 3 , or electrically stimulating the person’s own paralyzed muscles 4 . A key component of BMI systems is the decoder, which attempts to infer the user’s movement intentions from recorded neural activity 5 .…”

Section: Introductionmentioning

confidence: 99%

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Brain-machine interface cursor position only weakly affects monkey and human motor cortical activity in the absence of arm movements

Stavisky

Kao

Nuyujukian

et al. 2018

Sci Rep

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Brain-machine interfaces (BMIs) that decode movement intentions should ignore neural modulation sources distinct from the intended command. However, neurophysiology and control theory suggest that motor cortex reflects the motor effector’s position, which could be a nuisance variable. We investigated motor cortical correlates of BMI cursor position with or without concurrent arm movement. We show in two monkeys that subtracting away estimated neural correlates of position improves online BMI performance only if the animals were allowed to move their arm. To understand why, we compared the neural variance attributable to cursor position when the same task was performed using arm reaching, versus arms-restrained BMI use. Firing rates correlated with both BMI cursor and hand positions, but hand positional effects were greater. To examine whether BMI position influences decoding in people with paralysis, we analyzed data from two intracortical BMI clinical trial participants and performed an online decoder comparison in one participant. We found only small motor cortical correlates, which did not affect performance. These results suggest that arm movement and proprioception are the major contributors to position-related motor cortical correlates. Cursor position visual feedback is therefore unlikely to affect the performance of BMI-driven prosthetic systems being developed for people with paralysis.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Brain-machine interface cursor position only weakly affects monkey and human motor cortical activity in the absence of arm movements

Stavisky

Kao

Nuyujukian

et al. 2018

Sci Rep

View full text Add to dashboard Cite

show abstract

“…BMIs seek to restore lost function to people with neurological motor injury or disease by decoding neural activity from the brain to drive a prosthesis device, such as a computer cursor on a screen or a robotic arm (Bensmaia and Miller 2014; Ethier, Gallego, and Miller 2015; Homer et al 2013; Kao et al 2014; Tsu et al 2015). To date, substantial progress has been made in intracortical BMIs in which movement intention is inferred from electrical recordings of neurons in PMd and M1, enabling recent phase I clinical trials for translating this technology to humans (Collinger et al 2013; Gilja et al 2015; Hochberg et al 2006; Hochberg et al 2012; Wodlinger et al 2015).…”

Section: Insights From Optical Methods For Brain-machine Interface Dementioning

confidence: 99%

The need for calcium imaging in nonhuman primates: New motor neuroscience and brain-machine interfaces

O’Shea

Trautmann

Chandrasekaran

et al. 2017

Experimental Neurology

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A central goal of neuroscience is to understand how populations of neurons coordinate and cooperate in order to give rise to perception, cognition, and action. Nonhuman primates (NHPs) are an attractive model with which to understand these mechanisms in humans, primarily due to the strong homology of their brains and the cognitively sophisticated behaviors they can be trained to perform. Using electrode recordings, the activity of one to a few hundred individual neurons may be measured electrically, which has enabled many scientific findings and the development of brain-machine interfaces. Despite these successes, electrophysiology samples sparsely from neural populations and provide little information about the genetic identity and spatial micro-organization of recorded neurons. These limitations have spurred the development of all-optical methods for neural circuit interrogation. Fluorescent calcium signals serve as a reporter of neuronal responses, and when combined with post-mortem optical clearing techniques such as CLARITY, provide dense recordings of neuronal populations, spatially organized and annotated with genetic and anatomical information. Here, we exhort that this methodology, which has been of tremendous utility in smaller animal models, can and should be designed and developed for use with NHPs. We review here several of the key opportunities and challenges for calcium-based optical imaging in NHPs. We focus on motor neuroscience and brain-machine interface design as representative domains of opportunity within the larger field of NHP neuroscience.

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“…1, 2 Intracortical brain-machine interfaces (BMIs) have emerged as a promising clinical tool for restoring lost motor functions by translating neural activity directly into control signals for guiding assistive devices. 3,4 Early pilot clinical studies have demonstrated that BMIs can be used to control a computer cursor, 5-8 robotic arm, [9][10][11] or functional electrical stimulation (FES) system. [12][13][14] While there has been tremendous progress in the field of BMIs from both nonhuman primate and human studies in the past two decades, several challenges remain to be overcome to achieve a clinically viable translation of BMIs.…”

Section: Introductionmentioning

confidence: 99%

Robust and accurate decoding of hand kinematics from entire spiking activity using deep learning

Ahmadi

Constandinou

Bouganis

2020

Preprint

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Robustness and decoding accuracy remain major challenges in the clinical translation of intracortical brain-machine interface (BMI) systems. In this work, we show that a signal/decoder co-design methodology (exploiting the synergism between the input signal and decoding algorithm within the design development process) can be used to yield robust and accurate BMI decoding performance. Specifically, through applying this process, we propose the combination of using entire spiking activity (ESA) as the input signal and quasi-recurrent neural network (QRNN) based deep learning as the decoding algorithm. We evaluated the performance of ESA-driven QRNN decoder for decoding hand kinematics from neural signals chronically recorded from the primary motor cortex area of a non-human primate. Our proposed method yielded consistently higher decoding performance than any other methods previously reported across long-term recording sessions. Its high decoding performance could sustain, even when spikes were removed from the raw signals.Overall results demonstrate exceptionally high decoding accuracy and chronic robustness, which is highly desirable given it is an unresolved challenge in BMIs. this issue is by utilising a different type of input signals, namely multiunit activity (MUA). MUA, defined as all spikes detected through threshold crossing without spike sorting, offers simpler processing while providing better signal stability over time than SUA. 25,29,30 Another alternative input signal is local field potential (LFP), which is thought to mainly reflect summed synaptic activity from a local neuronal population around the recording electrodes. It is believed and has been demonstrated by experimental studies that LFPs exhibit better long-term signal stability than their spike counterparts. [31][32][33][34] Moreover, LFPs can be obtained by simpler processing and lower sampling rate that can reduce the power consumption of BMIs. Despite the appealing advantages of MUA and LFP, a considerable number of published studies have reported that the decoding accuracy of MUA 24,29,30,35, 36 and LFP 33,[36][37][38][39] are lower than that of SUA. Therefore, it is highly desirable to have an input signal that is not only stable but also yields high decoding accuracy.The decoding accuracy is also affected by the decoder, that is, an algorithm used to convert the input signal from the brain into the behavioural parameter of interest (e.g. hand kinematics). Many BMI studies employ linear decoders, e.g. Wiener filter (WF) 5,40,41 and Kalman filter (KF), 6,9,21,32,33,42 which could yield suboptimal decoding accuracy as neural signals are known to exhibit nonlinear and nonstationary properties. 43 Although there exists a nonlinear extension of WF and KF, called Wiener cascade filter (WCF) 19,39,44 and unscented Kalman filter (UKF), 45,46 WCF and UKF assume that the noise is (additive) stationary and Gaussian, respectively. If these a priori assumptions are violated, the decoding performance of both decoders will not be optimal.The rise of ...

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Cortical neuroprosthetics from a clinical perspective

Cited by 15 publications

References 107 publications

Brain-machine interface cursor position only weakly affects monkey and human motor cortical activity in the absence of arm movements

Brain-machine interface cursor position only weakly affects monkey and human motor cortical activity in the absence of arm movements

The need for calcium imaging in nonhuman primates: New motor neuroscience and brain-machine interfaces

Robust and accurate decoding of hand kinematics from entire spiking activity using deep learning

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