Stable long-term BCI-enabled communication in ALS and locked-in syndrome using LFP signals

Milekovic, Tomislav; Sarma, Anish A.; Bacher, Daniel; Simeral, John D.; Saab, Jad; Pandarinath, Chethan; Sorice, Brittany L; Blabe, Christine H; Oakley, Erin M.; Tringale, Kathryn R.; Eskandar, Emad N.; Cash, Sydney S.; Henderson, Jaimie M.; Shenoy, Krishna V.; Donoghue, John P.; Hochberg, Leigh R.

doi:10.1152/jn.00493.2017

Cited by 101 publications

(73 citation statements)

References 89 publications

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“…We implemented an approach based on a multiclass regularized linear discriminant analysis algorithm (mrLDA) to detect moments of movement onset and object grasp from the continuous neural recordings from either the motor or somatosensory cortices (Capogrosso et al, 2016;Milekovic et al, 2013a;Milekovic et al, 2018). We synchronized the multiunit spike activity with the movement onset and object grasp events identified using video recordings.…”

Section: Detection Of Movement Onset and Object Grasp From The Sensormentioning

confidence: 99%

“…where t i are all times at least 10ms away from all mo and og events. We then calibrated a set of mrLDA decoding models using C mo , C og and C OTHER and all possible combinations of parameter values (Capogrosso et al, 2016;Milekovic et al, 2013a;Milekovic et al, 2018). We used the mrLDA regularization parameter as an additional parameter with values of 0, 0.001, 0.1, 0.3, 0.5, 0.7, 0.9 and 0.99.…”

Section: Detection Of Movement Onset and Object Grasp From The Sensormentioning

confidence: 99%

“…To reproduce the sparsity of these events, we ignored the probability values of the detected event for 1s after the detection. We pooled the time series of actual and detected events across all four folds and calculated normalized mutual information using a tolerance window of 200ms (Capogrosso et al, 2016;Milekovic et al, 2013a;Milekovic et al, 2018). We then calibrated another decoding model on the complete testing part of the dataset using the parameter values that resulted with the maximum normalized mutual information.…”

Section: Detection Of Movement Onset and Object Grasp From The Sensormentioning

confidence: 99%

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A Versatile Robotic Platform for the Design of Natural, Three-Dimensional Reaching and Grasping Tasks in Monkeys

Barra

Badi

et al. 2019

Preprint

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words)Translational studies on motor control and neurological disorders require detailed monitoring of sensorimotor components of natural limb movements in relevant animal models. However, available experimental tools do not provide a sufficiently rich repertoire of behavioral signals. Here, we developed a robotic platform that enables the monitoring of kinematics, interaction forces, and neurophysiological signals during user-definable upper limb tasks for monkeys. We configured the platform to position instrumented objects in a three-dimensional workspace and provide an interactive dynamic force-field. We show the relevance of our platform for fundamental and translational studies with three example applications. First, we study the kinematics of natural grasp in response to variable interaction forces. We then show simultaneous and independent encoding of kinematic and forces in single unit intra-cortical recordings from sensorimotor cortical areas. Lastly, we demonstrate the relevance of our platform to develop clinically relevant brain computer interfaces in a kinematically unconstrained motor task control of the robot arm, 3) a synchronized interaction force recording system, 4) a strain-gauge grip pressure sensor, 5) an infrared video tracking system to measure three-dimensional joint kinematics (Vicon, Oxford, UK) and an 6) electrophysiology system (Blackrock Microsystems, Salt Lake City, USA). We assessed the versatility and efficacy of our framework by programming a robotic task for monkeys. We configured the robot to position objects in a three-dimensional workspace and trained monkeys to reach and pull on the objects while kinetic, kinematic and neural signals were simultaneously recorded. Closed-Loop control infrastructureThe IIWA robotic arm features a large workspace (Figure S1) allowing ranges of motion that are compatible with both human and monkey reaches. Additionally, the robotic arm is able to actively lift up to 7Kg of weight, which makes it robust to manipulation by monkeys.We developed a software package that implements a real-time closed-loop control (Figure 2A, 10.5281/zenodo.3234138) configured as a finite state machine. This allows fast configuration of tasks that proceed through several phases, where each phase requires a different behaviour of the robot.In our specific example application, at the beginning of the trial, the robot moves the end effector to a predetermined position in space using impedance joint control. Upon reaching position, the state machine switches to mass-spring damper behaviour (Figure 2A). Stiffness and damping parameters are definable by the user. Variations from this behaviour can be easily configured using our software.

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Section: Detection Of Movement Onset and Object Grasp From The Sensormentioning

confidence: 99%

Section: Detection Of Movement Onset and Object Grasp From The Sensormentioning

confidence: 99%

Section: Detection Of Movement Onset and Object Grasp From The Sensormentioning

confidence: 99%

See 1 more Smart Citation

A Versatile Robotic Platform for the Design of Natural, Three-Dimensional Reaching and Grasping Tasks in Monkeys

Barra

Badi

et al. 2019

Preprint

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“…Brain machine interfaces (BMIs) are playing an increasingly important role in neurological research (1)(2)(3)(4), clinical treatments (5,6) and neural-prosthetics (7)(8)(9). With the advent of powerful signal processing and data analytics software tools, the impetus has shifted from two-dimensional bulky probes to developing electrical probes with higher channel counts, lower tissue damage, and long-term recording stability at the single cell level.…”

Section: Introductionmentioning

confidence: 99%

NeuroRoots, a bio-inspired, seamless Brain Machine Interface device for long-term recording

Ferro

Proctor

Gonzalez

et al. 2018

Preprint

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Minimally invasive electrodes of cellular scale that approach a bio-integrative level of neural recording could enable the development of scalable brain machine interfaces that stably interface with the same neural populations over long period of time. In this paper, we designed and created NeuroRoots, a bio-mimetic multi-channel implant sharing similar dimension (10µm wide, 1.5µm thick), mechanical flexibility and spatial distribution as axon bundles in the brain. A simple approach of delivery is reported based on the assembly and controllable immobilization of the electrode onto a 35µm microwire shuttle by using capillarity and surface-tension in aqueous solution. Once implanted into targeted regions of the brain, the microwire was retracted leaving NeuroRoots in the biological tissue with minimal surgical footprint and perturbation of existing neural architectures within the tissue. NeuroRoots was implanted using a platform compatible with commercially available electrophysiology rigs and with measurements of interests in behavioral experiments in adult rats freely moving into maze. We demonstrated that NeuroRoots electrodes reliably detected action potentials for at least 7 weeks and the signal amplitude and shape remained relatively constant during long-term implantation. This research represents a step forward in the direction of developing the next generation of seamless brain-machine interface to study and modulate the activities of specific subpopulations of neurons, and to develop therapies for a plethora of neurological diseases. Science AdvancesManuscript Template

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“…By imagining natural hand, finger and arm movements, trial participants with paralysis have achieved reach-and grasp with robotic and prosthetic limbs [2], [3], [11] and their own reanimated limb [6], [12], and have demonstrated reliable cursor control for tablet use [8] and on-screen typing for communication [4], [7], [13]. Building on steady advances in point-and-click accuracy, speed [4], [7], [13]- [16] and consistency [17]- [21], iBCI trial participants at home have achieved average on-screen point-to-select typing rates over 37 correct characters per minute maintained over days and weeks [4], [7].…”

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confidence: 99%

Home Use of a Wireless Intracortical Brain-Computer Interface by Individuals With Tetraplegia

Hosman

Saab

et al. 2019

Preprint

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Individuals with neurological disease or injury such as amyotrophic lateral sclerosis, spinal cord injury or stroke may become tetraplegic, unable to speak or even locked-in. For people with these conditions, current assistive technologies are often ineffective. Brain-computer interfaces are being developed to enhance independence and restore communication in the absence of physical movement. Over the past decade, individuals with tetraplegia have achieved rapid on-screen typing and point-andclick control of tablet apps using intracortical brain-computer interfaces (iBCIs) that decode intended arm and hand movements from neural signals recorded by implanted microelectrode arrays. However, cables used to convey neural signals from the brain tether participants to amplifiers and decoding computers and require expert oversight during use, severely limiting when and where iBCIs could be available for use. Here, we demonstrate the first human use of a wireless broadband iBCI. Based on a prototype system previously used in pre-clinical research, we replaced the external cables of a 192-electrode iBCI with wireless transmitters and achieved high-resolution recording and decoding of broadband field potentials and spiking activity from people with paralysis. Two participants in an ongoing pilot clinical trial performed on-screen item selection tasks to assess iBCI-enabled cursor control. Communication bitrates were equivalent between cabled and wireless configurations. Participants also used the wireless iBCI to control a standard commercial tablet computer to browse the web and use several mobile applications. Within-day comparison of cabled and wireless interfaces evaluated bit error rate, packet loss, and the recovery of spike rates and spike waveforms from the recorded neural signals. In a representative use case, the wireless system recorded intracortical signals from two arrays in one participant continuously through a 24-hour period at home. Wireless multi-electrode recording of broadband neural signals over extended periods introduces a valuable tool for human neuroscience research and is an important step toward practical deployment of iBCI technology for independent use by individuals with paralysis. On-demand access to highperformance iBCI technology in the home promises to enhance independence and restore communication and mobility for individuals with severe motor impairment. 1

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Stable long-term BCI-enabled communication in ALS and locked-in syndrome using LFP signals

Cited by 101 publications

References 89 publications

A Versatile Robotic Platform for the Design of Natural, Three-Dimensional Reaching and Grasping Tasks in Monkeys

A Versatile Robotic Platform for the Design of Natural, Three-Dimensional Reaching and Grasping Tasks in Monkeys

NeuroRoots, a bio-inspired, seamless Brain Machine Interface device for long-term recording

Home Use of a Wireless Intracortical Brain-Computer Interface by Individuals With Tetraplegia

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