A5008311870- EEG and Brain-Computer Interfaces
- Neuroscience and Neural Engineering
- Advanced Memory and Neural Computing
- Muscle activation and electromyography studies
- Neural dynamics and brain function
- Multiple and Secondary Primary Cancers
- Opinion Dynamics and Social Influence
- Simulation Techniques and Applications
- Photoreceptor and optogenetics research
- Analog and Mixed-Signal Circuit Design
- Molecular Communication and Nanonetworks
- Bladder and Urothelial Cancer Treatments
- Testicular diseases and treatments
- CCD and CMOS Imaging Sensors
- Neural Networks and Reservoir Computing
- Sensor Technology and Measurement Systems
- Neural Networks and Applications
- Functional Brain Connectivity Studies
- Action Observation and Synchronization
- Genetic factors in colorectal cancer
Emory University
2024
The Wallace H. Coulter Department of Biomedical Engineering
2023-2024
Georgia Institute of Technology
2023-2024
University of Michigan
2007-2023
Michigan United
2020
Stanford University
2007
University of Chicago
2007
Johns Hopkins University
2007
Bristol-Myers Squibb (United States)
2007
Abstract Despite the rapid progress and interest in brain-machine interfaces that restore motor function, performance of prosthetic fingers limbs has yet to mimic native function. The algorithm converts brain signals a control signal for device is one limitations achieving realistic finger movements. To achieve more movements, we developed shallow feed-forward neural network decode real-time two-degree-of-freedom movements two adult male rhesus macaques. Using two-step training method,...
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...
Intracortical brain-machine interfaces (BMIs) are a promising source of prosthesis control signals for individuals with severe motor disabilities. Previous BMI studies have primarily focused on predicting and controlling whole-arm movements; precise hand kinematics, however, has not been fully demonstrated. Here, we investigate the continuous decoding finger movements in rhesus macaques.
Objective. To date, many brain-machine interface (BMI) studies have developed decoding algorithms for neuroprostheses that provide users with precise control of upper arm reaches some limited grasping capabilities. However, comparatively few focused on quantifying the performance finger control. Here we expand upon this work by investigating online individual groups. Approach. We a novel training manipulandum non-human primate (NHP) to isolate movements two specific groups: index and...
Brain machine interfaces using neural recording systems [1]–[4] can enable motor prediction [5]–[6] for accurate arm and hand control in paralyzed or severely injured individuals. However, implantable have historically used wires data communication power, increasing risks of tissue damage, infection, cerebrospinal fluid leakage, rendering these devices unsuitable long-term implantation (Fig. 26.9.1, top). Recently, several wireless miniaturized implants with various power transmission...
Miniaturized and wireless near-infrared (NIR) based neural recorders with optical powering data telemetry have been introduced as a promising approach for safe long-term monitoring the smallest physical dimension among state-of-the-art standalone recorders. However, main challenge NIR recording ICs is to maintain robust operation in presence of light-induced parasitic short circuit current from junction diodes. This especially true when signal currents are kept small reduce power...
Arrays of floating neural sensors with high channel count that cover an area square centimeters and larger would be transformative for engineering brain-machine interfaces. Meeting the power wireless data communications requirements within size constraints each sensor has been elusive due to need incorporate sensing, computing, communications, functionality in a package approximately 100 micrometers on side. In this work, we demonstrate near infrared optical communication link recording...
Intracortical brain-machine interfaces have shown promise for restoring function to people with paralysis, but their translation portable and implantable devices is hindered by high power consumption. Recent drastically reduced consumption compared standard experimental interfaces, still require wired or wireless connections computing hardware feature extraction inference. Here, we introduce a Neural Recording And Decoding (NeuRAD) application specific integrated circuit (ASIC) in 180 nm...
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...
Brain-machine interfaces (BMIs) are promising for motor rehabilitation and mobility augmentation. High-accuracy low-power algorithms required to achieve implantable BMI systems. In this paper, we propose a novel spiking neural network (SNN) decoder regression tasks. The SNN is trained with enhanced spatio-temporal backpropagation fully leverage its ability in handling temporal problems. proposed achieves the same level of correlation coefficient as state-of-the-art ANN offline finger...
The loss of motor functions resulting from spinal cord injury can have devastating implications on the quality one's life. Functional electrical stimulation has been used to help restore mobility, however, current functional (FES) systems require residual movements control patterns, which may be unintuitive and not useful for individuals with higher level cervical injuries. Brain machine interfaces (BMI) offer a promising approach controlling such systems; they currently still transcutaneous...
A key factor in the clinical translation of brain-machine interfaces (BMIs) for restoring hand motor function will be their robustness to changes a task. With functional electrical stimulation (FES) example, patient's own used produce wide range forces otherwise similar movements. To investigate impact task on BMI performance, we trained two rhesus macaques control virtual with physical while added springs each finger group (index or middle-ring-small) altered wrist posture. Using...
Abstract Objective . Brain–machine interfaces (BMIs) have the potential to restore motor function but are currently limited by electrode count and long-term recording stability. These challenges may be solved through use of free-floating ‘motes’ which wirelessly transmit recorded neural signals, if power consumption can kept within safe levels when scaling thousands motes. Here, we evaluated a pulse-interval modulation (PIM) communication scheme for infrared (IR)-based motes that aims reduce...
A key challenge for near-infrared (NIR) powered neural recording ICs is to maintain robust operation in the presence of parasitic short circuit current from junction diodes when exposed light. This especially so intentional currents are kept small reduce power consumption. We present a IC that tolerant up 300 μW/mm2 light exposure (above tissue limit) and consumes 0.57 μW at 38°C, making it lowest among standalone motes while incorporating on-chip feature extraction individual gain control.
This letter proposes an instrumentation amplifier for neural recording applications whose measured noise efficiency factor (NEF) is 2.2. A discrete-time parametric adopted as a preamplification stage to lower the input-referred noise, thus improving NEF. The additional induced sampling minimized by oversampling, and power overhead switching adopting 8-phase soft-charging technique.
Objective.Brain-machine interfaces (BMIs) have shown promise in extracting upper extremity movement intention from the thoughts of nonhuman primates and people with tetraplegia. Attempts to restore a user's own hand arm function employed functional electrical stimulation (FES), but most work has restored discrete grasps. Little is known about how well FES can control continuous finger movements. Here, we use low-power brain-controlled (BCFES) system volitional positions monkey temporarily...
This paper proposes a 2.2 noise efficiency factor (NEF) instrumentation amplifier for neural recording applications. A parametric based on the MOS C-V characteristic is designed as pre-amplifier stage, lowering input referred of following stages by 3.4×. Sampling minimized oversampling signal and switching power reduced adopting an 8-phase soft-charging technique.
Brain-machine interfaces (BMIs) may generate more errors than those encountered during normal motor control. Thus, they provide an opportunity to investigate neural correlates of error processing. Characterizing processing may, in turn, a tool for on-line correction the that are made by interface. We investigated BMI experiments which monkeys controlled animated hand on screen touch ball moving their own fingers. Short movement segments were consistently toward or away from target labeled...
15543 Background: Vinflunine (VFL) is a new microtubule inhibitor of the vinca alkaloid class with clinical activity in TCCU (S. Culine, BJC 2006). This trial was conducted to define VFL platinum-refractory patients (pts). Methods: Multicenter, single-arm study. Primary endpoint: Objective response rate (Independent Review; WHO criteria). Planned sample size: 150 pts. Main pt eligibility: at least one measurable lesion; documented progression within 12 months last dose platinum-containing...
Objective.While brain-machine interfaces (BMIs) are promising technologies that could provide direct pathways for controlling the external world and thus regaining motor capabilities, their effectiveness is hampered by decoding errors. Previous research has demonstrated detection correction of BMI outcome errors, which occur at end trials. Here we focus on continuous execution during real-time movements.Approach.Two adult male rhesus macaques were implanted with Utah arrays in cortex. The...
Abstract Despite the rapid progress and interest in brain-machine interfaces that restore motor function, performance of prosthetic fingers limbs has yet to mimic native function. The algorithm converts brain signals a control signal for device is one limitations achieving realistic finger movements. To achieve more movements, we developed shallow feed-forward neural network, loosely inspired by biological pathway, decode real-time two-degree-of-freedom Using two-step training method,...