Kevin Bodkin
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View article: Stabilizing brain-computer interfaces through alignment of latent dynamics
Stabilizing brain-computer interfaces through alignment of latent dynamics Open
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 de…
View article: Less is more: selection from a small set of options improves BCI velocity control
Less is more: selection from a small set of options improves BCI velocity control Open
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 ar…
View article: Unsupervised, piecewise linear decoding enables an accurate prediction of muscle activity in a multi-task brain computer interface
Unsupervised, piecewise linear decoding enables an accurate prediction of muscle activity in a multi-task brain computer interface Open
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 comp…
View article: Unsupervised, piecewise linear decoding enables an accurate prediction of muscle activity in a multi-task brain computer interface
Unsupervised, piecewise linear decoding enables an accurate prediction of muscle activity in a multi-task brain computer interface Open
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 compu…
View article: Less is more: selection from a small set of options improves BCI velocity control
Less is more: selection from a small set of options improves BCI velocity control Open
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 …
View article: BRAND: a platform for closed-loop experiments with deep network models
BRAND: a platform for closed-loop experiments with deep network models Open
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 e…
View article: BRAND: A platform for closed-loop experiments with deep network models
BRAND: A platform for closed-loop experiments with deep network models Open
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 ru…
View article: BRAND: A platform for closed-loop experiments with deep network models
BRAND: A platform for closed-loop experiments with deep network models Open
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 ru…
View article: From monkeys to humans: observation-based EMG brain–computer interface decoders for humans with paralysis
From monkeys to humans: observation-based EMG brain–computer interface decoders for humans with paralysis Open
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 …
View article: BRAND: A platform for closed-loop experiments with deep network models
BRAND: A platform for closed-loop experiments with deep network models Open
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 rea…
View article: Using adversarial networks to extend brain computer interface decoding accuracy over time
Using adversarial networks to extend brain computer interface decoding accuracy over time Open
Existing intracortical brain computer interfaces (iBCIs) transform neural activity into control signals capable of restoring movement to persons with paralysis. However, the accuracy of the ‘decoder’ at the heart of the iBCI typically degr…
View article: Author response: Using adversarial networks to extend brain computer interface decoding accuracy over time
Author response: Using adversarial networks to extend brain computer interface decoding accuracy over time Open
Full text Figures and data Side by side Abstract Editor's evaluation Introduction Results Discussion Materials and methods Appendix 1 Data availability References Decision letter Author response Article and author information Abstract Exis…
View article: Monkey-to-human transfer of brain-computer interface decoders
Monkey-to-human transfer of brain-computer interface decoders Open
Intracortical brain-computer interfaces (iBCIs) enable paralyzed persons to generate movement, but current methods require large amounts of both neural and movement-related data to be collected from the iBCI user for supervised decoder tra…
View article: Stabilizing brain-computer interfaces through alignment of latent dynamics
Stabilizing brain-computer interfaces through alignment of latent dynamics Open
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 de…