About

Carlos E. Vargas-Irwin is an Assistant Professor of Neuroscience (Research) at Brown University. His long-term research goal is to understand how networks of neurons represent and transform information, with a focus on the relationship between cortical neuron activity and upper limb motion control. His work involves analyzing movements that involve multiple degrees of freedom, providing a measurable representation of high-dimensional nervous system output. Vargas-Irwin has developed mathematical neural decoder models capable of reconstructing shoulder, elbow, wrist, and digit movements solely from neural activity, and has examined how visual information influences motor cortex activity during grasping tasks. Additionally, he has contributed to the development of novel algorithms for automated electrophysiological data processing, including spike sorting techniques. His research aims to advance understanding of the nervous system and support the development of brain-controlled neuroprosthetic devices.

Research topics

• Computer Science
• Medicine
• Psychology
• Neuroscience
• Physical therapy
• Physical medicine and rehabilitation

Selected publications

• Observation-Related Activity in Human Motor Cortex Increases with Effector Anthropomorphicity

  bioRxiv (Cold Spring Harbor Laboratory) · 2026-04-28

  articleOpen access

  ABSTRACT Neurons in motor cortex can be engaged not only in motor execution but also during observation of movements performed by other anthropomorphic agents (i.e. humans or monkeys). However, it is unknown how motor cortical neurons respond during observation of the range of assistive or prosthetic devices controlled by people using intracortical brain-computer interfaces (iBCIs). We recorded single-unit activity in the precentral gyrus while iBCI users viewed grasp-like movements performed by a spectrum of virtual effectors that included human, robotic, and hand-like dot stimuli. We found a relationship between neural modulation and effector anthropomorphicity (i.e. human-likeness) that existed on an ensemble-wide and individual neuron level, suggesting that human motor cortex activity incrementally increases in response to the visually observed agent’s human-likeness. Both solicited and spontaneous feedback from the participant indicated a relationship between neural activity and subjective assessments of anthropomorphicity, revealing a powerful contribution of context on observation-induced activity in motor cortex. The activity of motor cortex remained similar during attempted hand movements while different effectors were being observed, suggesting that intuitive external device control via iBCIs may not be overtly affected by the anthropomorphicity of the effector. SIGNIFICANCE STATEMENT The tendency for neurons in motor cortex to respond during movement observation has been proposed to underlie cognitive processes from motor learning and language development to empathy and theory of mind. Understanding how the motor cortex is engaged during observation of abstract and anthropomorphic agents informs our understanding of these processes and may guide development of neural prostheses which harness the activity of motor cortical neurons to restore lost neurologic function. Here we provide unique neuron-level evidence that human motor cortex activity is gradually modulated by how human-like an observed agent appears and moves. This finding advances our interpretation of “mirror” activity in the brain and could help guide the design of brain-controlled prostheses used by people with tetraplegia.

  PublisherOA PDFDOI

• Mapping the computational similarity of individual neurons within large-scale ensemble recordings using the SIMNETS analysis framework

  Frontiers in Neuroscience · 2025-08-14 · 1 citations

  articleOpen access1st authorCorresponding

  The expansion of large-scale neural recording capabilities has provided new opportunities to examine multi-scale cortical network activity at single neuron resolution. At the same time, the growing scale and complexity of these datasets introduce new conceptual and technical challenges beyond what can be addressed using traditional analysis techniques. Here, we present the Similarity Networks (SIMNETS) analysis framework: an efficient and scalable pipeline designed to embed simultaneously recorded neurons into low dimensional maps according to the intrinsic relationship between their spike trains, making it possible to identify and visualize groups of neurons performing similar computations. The critical innovation is the use of pairwise spike train similarity (SSIM) matrices to capture the intrinsic relationship between the spike trains emitted by a neuron at different points in time (i.e., different experimental conditions), reflecting how the neuron responds to time-varying internal and external drives and making it possible to easily compare the information processing properties across neuronal populations. We use three publicly available neural population test datasets from the visual, motor, and hippocampal CA1 brain regions to validate the SIMNETS framework and demonstrate how it can be used to identify putative subnetworks (i.e., clusters of neurons with similar computational properties). Our analysis pipeline includes a novel statistical test designed to evaluate the likelihood of detecting spurious neuron clusters to validate network structure results. The SIMNETS framework provides a way to rapidly examine the computational structure of neuronal networks at multiple scales based on the intrinsic structure of single unit spike trains.

  PublisherOA PDFDOI

• Pseudo-linear summation explains neural geometry of multi-finger movements in human premotor cortex

  Nature Communications · 2025-05-29 · 4 citations

  articleOpen access

  How does the motor cortex combine simple movements (such as single finger flexion/extension) into complex movements (such as hand gestures, or playing the piano)? To address this question, motor cortical activity was recorded using intracortical multi-electrode arrays in two male people with tetraplegia as they attempted single, pairwise and higher-order finger movements. Neural activity for simultaneous movements was largely aligned with linear summation of corresponding single finger movement activities, with two violations. First, the neural activity exhibited normalization, preventing a large magnitude with an increasing number of moving fingers. Second, the neural tuning direction of weakly represented fingers changed significantly as a result of the movement of more strongly represented fingers. These deviations from linearity resulted in non-linear methods outperforming linear methods for neural decoding. Simultaneous finger movements are thus represented by the combination of individual finger movements by pseudo-linear summation. Here, the authors show that Intracortical recordings in human motor cortex during attempted multi-finger movements reveal a largely linear code with two violations: magnitude normalization and greater shifts in tuning direction for weakly represented fingers.

  PublisherOA PDFDOI

• Neural signal analysis in chronic stroke: advancing intracortical brain-computer interface design

  Frontiers in Human Neuroscience · 2025-02-21

  articleOpen access

  Introduction: Intracortical Brain-computer interfaces (iBCIs) are a promising technology to restore function after stroke. It remains unclear whether iBCIs will be able to use the signals available in the neocortex overlying stroke affecting the underlying white matter and basal ganglia. Methods: Here, we decoded both local field potentials (LFPs) and spikes recorded from intracortical electrode arrays in a person with chronic cerebral subcortical stroke performing various tasks with his paretic hand, with and without a powered orthosis. Analysis of these neural signals provides an opportunity to explore the electrophysiological activities of a stroke affected brain and inform the design of medical devices that could restore function. Results: The frequency domain analysis showed that as the distance between an array and the stroke site increased, the low frequency power decreased, and high frequency power increased. Coordinated cross-channel firing of action potentials while attempting a motor task and cross-channel simultaneous low frequency bursts while relaxing were also observed. Using several offline analysis techniques, we propose three features for decoding motor movements in stroke-affected brains. Discussion: Despite the presence of unique activities that were not reported in previous iBCI studies with intact brain functions, it is possible to decode motor intents from the neural signals collected from a subcortical stroke-affected brain.

  PublisherOA PDFDOI

• Gesture encoding in human left precentral gyrus neuronal ensembles

  Communications Biology · 2025-08-30 · 2 citations

  articleOpen access1st authorCorresponding

  Understanding the cortical activity patterns driving dexterous upper limb motion has the potential to benefit a broad clinical population living with limited mobility through the development of novel brain-computer interface (BCI) technology. The present study examines the activity of ensembles of motor cortical neurons recorded using microelectrode arrays in the dominant hemisphere of two BrainGate clinical trial participants with cervical spinal cord injury as they attempted to perform a set of 48 different hand gestures. Although each participant displayed a unique organization of their respective neural latent spaces, it was possible to achieve classification accuracies of ~70% for all 48 gestures (and ~90% for sets of 10). Our results show that single-unit ensemble activity recorded in a single hemisphere of human precentral gyrus has the potential to generate a wide range of gesture-related signals across both hands, providing an intuitive and diverse set of potential command signals for intracortical BCI use.

  PublisherOA PDFDOI

• Multi-gesture drag-and-drop decoding in a 2D iBCI control task

  Journal of Neural Engineering · 2025-02-03 · 1 citations

  articleOpen accessSenior author

  Abstract Objective . Intracortical brain–computer interfaces (iBCIs) have demonstrated the ability to enable point and click as well as reach and grasp control for people with tetraplegia. However, few studies have investigated iBCIs during long-duration discrete movements that would enable common computer interactions such as ‘click-and-hold’ or ‘drag-and-drop’. Approach . Here, we examined the performance of multi-class and binary (attempt/no-attempt) classification of neural activity in the left precentral gyrus of two BrainGate2 clinical trial participants performing hand gestures for 1, 2, and 4 s in duration. We then designed a novel ‘latch decoder’ that utilizes parallel multi-class and binary decoding processes and evaluated its performance on data from isolated sustained gesture attempts and a multi-gesture drag-and-drop task. Main results . Neural activity during sustained gestures revealed a marked decrease in the discriminability of hand gestures sustained beyond 1 s. Compared to standard direct decoding methods, the Latch decoder demonstrated substantial improvement in decoding accuracy for gestures performed independently or in conjunction with simultaneous 2D cursor control. Significance . This work highlights the unique neurophysiologic response patterns of sustained gesture attempts in human motor cortex and demonstrates a promising decoding approach that could enable individuals with tetraplegia to intuitively control a wider range of consumer electronics using an iBCI.

  PublisherDOI

• Multi-gesture drag-and-drop decoding in a 2D iBCI control task

  medRxiv · 2024-09-18

  preprintOpen accessSenior author

  Abstract Objective Intracortical brain-computer interfaces (iBCIs) have demonstrated the ability to enable point and click as well as reach and grasp control for people with tetraplegia. However, few studies have investigated iBCIs during long-duration discrete movements that would enable common computer interactions such as “click-and-hold” or “drag-and-drop”. Approach Here, we examined the performance of multi-class and binary (attempt/no-attempt) classification of neural activity in the left precentral gyrus of two BrainGate2 clinical trial participants performing hand gestures for 1, 2, and 4 seconds in duration. We then designed a novel “latch decoder” that utilizes parallel multi-class and binary decoding processes and evaluated its performance on data from isolated sustained gesture attempts and a multi-gesture drag-and-drop task. Main Results Neural activity during sustained gestures revealed a marked decrease in the discriminability of hand gestures sustained beyond 1 second. Compared to standard direct decoding methods, the Latch decoder demonstrated substantial improvement in decoding accuracy for gestures performed independently or in conjunction with simultaneous 2D cursor control Significance This work highlights the unique neurophysiologic response patterns of sustained gesture attempts in human motor cortex and demonstrates a promising decoding approach that could enable individuals with tetraplegia to intuitively control a wider range of consumer electronics using an iBCI.

  PublisherOA PDFDOI

• Measuring instability in chronic human intracortical neural recordings towards stable, long-term brain-computer interfaces

  bioRxiv (Cold Spring Harbor Laboratory) · 2024-03-04 · 1 citations

  preprintOpen access

  Abstract Intracortical brain-computer interfaces (iBCIs) enable people with tetraplegia to gain intuitive cursor control from movement intentions. To translate to practical use, iBCIs should provide reliable performance for extended periods of time. However, performance begins to degrade as the relationship between kinematic intention and recorded neural activity shifts compared to when the decoder was initially trained. In addition to developing decoders to better handle long-term instability, identifying when to recalibrate will also optimize performance. We propose a method to measure instability in neural data without needing to label user intentions. Longitudinal data were analyzed from two BrainGate2 participants with tetraplegia as they used fixed decoders to control a computer cursor spanning 142 days and 28 days, respectively. We demonstrate a measure of instability that correlates with changes in closed-loop cursor performance solely based on the recorded neural activity (Pearson r = 0.93 and 0.72, respectively). This result suggests a strategy to infer online iBCI performance from neural data alone and to determine when recalibration should take place for practical long-term use.

  PublisherOA PDFDOI

• Measuring instability in chronic human intracortical neural recordings towards stable, long-term brain-computer interfaces

  Communications Biology · 2024-10-21 · 11 citations

  articleOpen access

  Intracortical brain-computer interfaces (iBCIs) enable people with tetraplegia to gain intuitive cursor control from movement intentions. To translate to practical use, iBCIs should provide reliable performance for extended periods of time. However, performance begins to degrade as the relationship between kinematic intention and recorded neural activity shifts compared to when the decoder was initially trained. In addition to developing decoders to better handle long-term instability, identifying when to recalibrate will also optimize performance. We propose a method, "MINDFUL", to measure instabilities in neural data for useful long-term iBCI, without needing labels of user intentions. Longitudinal data were analyzed from two BrainGate2 participants with tetraplegia as they used fixed decoders to control a computer cursor spanning 142 days and 28 days, respectively. We demonstrate a measure of instability that correlates with changes in closed-loop cursor performance solely based on the recorded neural activity (Pearson r = 0.93 and 0.72, respectively). This result suggests a strategy to infer online iBCI performance from neural data alone and to determine when recalibration should take place for practical long-term use.

  PublisherOA PDFDOI

• Gesture encoding in human left precentral gyrus neuronal ensembles

  bioRxiv (Cold Spring Harbor Laboratory) · 2024-08-24 · 3 citations

  preprintOpen access1st authorCorresponding

  Understanding the cortical activity patterns driving dexterous upper limb motion has the potential to benefit a broad clinical population living with limited mobility through the development of novel brain-computer interface (BCI) technology. The present study examines the activity of ensembles of motor cortical neurons recorded using microelectrode arrays in the dominant hemisphere of two BrainGate clinical trial participants with cervical spinal cord injury as they attempted to perform a set of 48 different hand gestures. Although each participant displayed a unique organization of their respective neural latent spaces, it was possible to achieve classification accuracies of ~70% for all 48 gestures (and ~90% for sets of 10). Our results show that single unit ensemble activity recorded in a single hemisphere of human precentral gyrus has the potential to generate a wide range of gesture-related signals across both hands, providing an intuitive and diverse set of potential command signals for intracortical BCI use.

  PublisherOA PDFDOI

Frequent coauthors

• Leigh R. Hochberg

  Harvard University

  163 shared

• John P. Donoghue

  Rehabilitation Research and Development Service

  110 shared

• John D. Simeral

  70 shared

• Wilson Truccolo

  Providence College

  55 shared

• Jaimie M. Henderson

  40 shared

• Tommy Hosman

  39 shared

• Krishna V. Shenoy

  Howard Hughes Medical Institute

  35 shared

• David M. Brandman

  University of California, Davis

  32 shared

Labs

• Donoghue LabPI

  Our laboratory investigates how the brain turns thought into voluntary behavior. We seek to expand our understanding of the basic principles of neural computation, and use this knowledge to improve the quality of life of persons with paralysis. We study how populations of neurons represent and transform information as motor plans evolve towards movement execution.

Awards & honors

• NIH Director's New Innovator Award: DP2NS111817 (NINDS)