A5089854808- EEG and Brain-Computer Interfaces
- Neuroscience and Neural Engineering
- Neural dynamics and brain function
- Advanced Memory and Neural Computing
- Functional Brain Connectivity Studies
- Opinion Dynamics and Social Influence
- Simulation Techniques and Applications
- Advanced MRI Techniques and Applications
- Phonetics and Phonology Research
- Speech Recognition and Synthesis
- Muscle activation and electromyography studies
- Energy Harvesting in Wireless Networks
- Action Observation and Synchronization
- Neurological disorders and treatments
- Gaze Tracking and Assistive Technology
- Robotics and Automated Systems
- Modular Robots and Swarm Intelligence
University of California, Davis
2023-2024
Neurological Surgery
2023-2024
University of Pittsburgh
2014-2022
Center for the Neural Basis of Cognition
2020-2022
Center for Neurosciences
2020
BackgroundBrain–computer interfaces can enable communication for people with paralysis by transforming cortical activity associated attempted speech into text on a computer screen. Communication brain–computer has been restricted extensive training requirements and limited accuracy.MethodsA 45-year-old man amyotrophic lateral sclerosis (ALS) tetraparesis severe dysarthria underwent surgical implantation of four microelectrode arrays his left ventral precentral gyrus 5 years after the onset...
Abstract Objective. Artificial neural networks (ANNs) are state-of-the-art tools for modeling and decoding activity, but deploying them in closed-loop experiments with tight timing constraints is challenging due to their limited support existing real-time frameworks. Researchers need a platform that fully supports high-level languages running ANNs (e.g. Python Julia) while maintaining critical low-latency data acquisition processing C C++). Approach. To address these needs, we introduce the...
INTRODUCTION: Communication is a top priority for people living with dysarthria. One approach to restoring communication use brain-computer interface (BCI), system that links the neural activity external devices. By translating intracortical text appearing on screen, we have previously shown how man ALS and severe dysarthria could BCI communicate at 98% accuracy from dictionary of 125,000 words. METHODS: As part ongoing BrainGate2 clinical trial (NCT00912041), surgically implanted four...
Recognizing keyboard typing as a familiar, high information rate communication paradigm, we developed an intracortical brain computer interface (iBCI) neuroprosthesis providing bimanual QWERTY functionality for people with paralysis. Typing this iBCI involves only attempted finger movements, which are decoded accurately few 30 calibration sentences. Sentence decoding is improved using 5-gram language model. This performed well two clinical trial participants tetraplegia - one ALS and spinal...
The forelimb representation in motor cortex (M1) is an important model system contemporary neuroscience. Efforts to understand the organization of M1 monkeys have focused on inputs and outputs. In contrast, intrinsic connections remain mostly unexplored, which surprising given that intra-areal universally outnumber extrinsic connections. To address this knowledge gap, we first mapped with intracortical microstimulation (ICMS) male squirrel monkeys. Next, determined connectivity individual...
Brain-computer interfaces can enable rapid, intuitive communication for people with paralysis by transforming the cortical activity associated attempted speech into text on a computer screen. Despite recent advances, brain-computer has been restricted extensive training data requirements and inaccurate word output. A man in his 40's ALS tetraparesis severe dysarthria (ALSFRS-R = 23) was enrolled BrainGate2 clinical trial. He underwent surgical implantation of four microelectrode arrays left...
Summary Understanding how the body is represented in motor cortex key to understanding brain controls movement. The precentral gyrus (PCG) has long been thought contain largely distinct regions for arm, leg and face (represented by “motor homunculus”). However, mounting evidence begun reveal a more intermixed, interrelated broadly tuned map. Here, we revisit homunculus using microelectrode array recordings from 20 arrays that sample PCG across 8 individuals, creating comprehensive map of...
Abstract Brain computer interfaces (BCIs) have the potential to restore communication people who lost ability speak due neurological disease or injury. BCIs been used translate neural correlates of attempted speech into text 1–3 . However, fails capture nuances human such as prosody, intonation and immediately hearing one’s own voice. Here, we demonstrate a “brain-to-voice” neuroprosthesis that instantaneously synthesizes voice with closed-loop audio feedback by decoding activity from 256...
Brain computer interface (BCI) control predominately uses visual feedback. Real arm movements, however, are controlled under a diversity of feedback mechanisms. The lack additional BCI modalities forces users to maintain contact while performing tasks. Such stringent requirements result in poor during tasks that inherently feedback, such as grasping, or when attention is diverted. Using modified version the Critical Tracking Task [1] which we call Stability (CST), tested ability 9 human...
Abstract Speech brain-computer interfaces show great promise in restoring communication for people who can no longer speak 1–3 , but have also raised privacy concerns regarding their potential to decode private verbal thought 4–6 . Using multi-unit recordings three participants with dysarthria, we studied the representation of inner speech motor cortex. We found a robust neural encoding speech, such that individual words and continuously imagined sentences could be decoded real-time This was...
Decoding neural activity from ventral (speech) motor cortex is known to enable high-performance speech brain-computer interface (BCI) control. It was previously unknown whether this brain area could also computer control via cursor and click, as typically associated with dorsal (arm hand) cortex. We recruited a clinical trial participant ALS implanted intracortical microelectrode arrays in precentral gyrus (vPCG), which the used operate BCI prior study. developed driven by participant's vPCG...
Artificial neural networks (ANNs) are state-of-the-art tools for modeling and decoding activity, but deploying them in closed-loop experiments with tight timing constraints is challenging due to their limited support existing real-time frameworks. Researchers need a platform that fully supports high-level languages running ANNs (e.g., Python Julia) while maintaining critical low-latency data acquisition processing C C++). To address these needs, we introduce the Backend Realtime Asynchronous...