A5052644646- Neural dynamics and brain function
- Advanced Memory and Neural Computing
- Opinion Dynamics and Social Influence
- Simulation Techniques and Applications
- COVID-19 epidemiological studies
- EEG and Brain-Computer Interfaces
- Human Mobility and Location-Based Analysis
- Video Surveillance and Tracking Methods
- Urban Transport and Accessibility
Brown University
2023-2024
John Brown University
2024
IEEE Computer Society
2023
Institute of Electrical and Electronics Engineers
2023
Regional Municipality of Niagara
2023
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...
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...
In order to contain the COVID-19 pandemic, countries around world have introduced social distancing guidelines as public health interventions reduce spread of disease. However, monitoring efficacy these at a large scale (nationwide or worldwide) is difficult. To make matters worse, traditional observational methods such in-person reporting dangerous because observers may risk infection. A better solution observe activities through network cameras; this approach scalable and can stay in safe...
This article analyzes visual data captured from five countries and three U.S. states to evaluate the effectiveness of lockdown policies for reducing spread COVID-19.The main challenge is scale: nearly six million images are analyzed observe how people respond policy changes.
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...