Kevin L. Bodkin

Monoaminergic modulation of the relation between motor cortical activity and grip force

R. Henry Powell, Xuan Ma, Kevin L. Bodkin, Lee E Miller

Monoamines have been suggested to act as force gain controllers through their action on persistent inward currents in motoneurons. We tested this idea by comparing the modulation of grasp-related M1 activity in monkeys before and after oral administration of various monoaminergic drugs. …

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Patterned wireless transcranial optogenetics generates artificial perception

Mingzheng Wu, Yiyuan Yang, Jinglan Zhang, Andrew I. Efimov et autres

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Stabilizing brain-computer interfaces through alignment of latent dynamics

Brianna M. Karpowicz, Yahia H Ali, Lahiru N. Wimalasena, Andrew R. Sedler et autres

Intracortical brain-computer interfaces (iBCIs) restore motor function to people with paralysis by translating brain activity into control signals for external devices. In current iBCIs, instabilities at the neural interface result in a degradation of decoding performance, which necessitates frequent supervised recalibration using …

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Less is more: selection from a small set of options improves BCI velocity control

Pedro Ivan Alcolea, Xuan Ma, Kevin L. Bodkin, Lee E. Miller et autres

Abstract Objective. Decoding algorithms used in invasive brain–computer interfaces (iBCIs) typically convert neural activity into continuously varying velocity commands. We hypothesized that putting constraints on which decoded velocity commands are permissible could improve user performance. To test this hypothesis, we designed the …

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Unsupervised, piecewise linear decoding enables an accurate prediction of muscle activity in a multi-task brain computer interface

Xuan Ma, Fabio Rizzoglio, Kevin L. Bodkin, Lee E. Miller

Abstract Objective. Creating an intracortical brain computer interface (iBCI) capable of seamless transitions between tasks and contexts would greatly enhance user experience. However, the nonlinearity in neural activity presents challenges to computing a global iBCI decoder. We aimed to develop a method …

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Unsupervised, piecewise linear decoding enables an accurate prediction of muscle activity in a multi-task brain computer interface

Xuan Ma, Fabio Rizzoglio, Kevin L. Bodkin, Lee E. Miller

Objective: Creating an intracortical brain-computer interface (iBCI) capable of seamless transitions between tasks and contexts would greatly enhance user experience. However, the nonlinearity in neural activity presents challenges to computing a global iBCI decoder. We aimed to develop a method that differs …

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Less is more: selection from a small set of options improves BCI velocity control

Pedro Ivan Alcolea, Xuan Ma, Kevin L. Bodkin, Lee E. Miller et autres

We designed the discrete direction selection (DDS) decoder for intracortical brain computer interface (iBCI) cursor control and showed that it outperformed currently used decoders in a human-operated real-time iBCI simulator and in monkey iBCI use. Unlike virtually all existing decoders that map …

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BRAND: a platform for closed-loop experiments with deep network models

Yahia H Ali, Kevin L. Bodkin, Mattia Rigotti-Thompson, Kushant Patel et autres

Abstract Objective. Artificial neural networks (ANNs) are state-of-the-art tools for modeling and decoding neural activity, but deploying them in closed-loop experiments with tight timing constraints is challenging due to their limited support in existing real-time frameworks. Researchers need a platform that fully …

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BRAND: A platform for closed-loop experiments with deep network models

Yahia H Ali, Kevin L. Bodkin, Mattia Rigotti-Thompson, Kushant Patel et autres

This is the supporting data for "BRAND: A platform for closed-loop experiments with deep network models". It can be analyzed using the code at github.com/brandbci/paper-figures. This dataset contains: Latency measurements from computers running the BRAND software under several different conditions Example data …

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BRAND: A platform for closed-loop experiments with deep network models

Yahia H Ali, Kevin L. Bodkin, Mattia Rigotti-Thompson, Kushant Patel et autres

This is the supporting data for "BRAND: A platform for closed-loop experiments with deep network models". It can be analyzed using the code at github.com/brandbci/paper-figures. This dataset contains: Latency measurements from computers running the BRAND software under several different conditions Example data …

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From monkeys to humans: observation-based EMG brain–computer interface decoders for humans with paralysis

Fabio Rizzoglio, Ege Altan, Xuan Ma, Kevin L. Bodkin et autres

Abstract Objective . Intracortical brain–computer interfaces (iBCIs) aim to enable individuals with paralysis to control the movement of virtual limbs and robotic arms. Because patients’ paralysis prevents training a direct neural activity to limb movement decoder, most iBCIs rely on ‘observation-based’ decoding …

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BRAND: A platform for closed-loop experiments with deep network models

Yahia H Ali, Kevin L. Bodkin, Mattia Rigotti-Thompson, Kushant Patel et autres

Abstract Artificial neural networks (ANNs) are state-of-the-art tools for modeling and decoding neural activity, but deploying them in closed-loop experiments with tight timing constraints is challenging due to their limited support in existing real-time frameworks. Researchers need a platform that fully supports …

us (code pays fourni par la source)