Human balance is a complex process in healthy adults, requiring precisely timed coordination among sensory information, cognitive processing, and motor control. It has been difficult to quantify brain dynamics during human control due limitations brain-imaging modalities. The goal of this study was determine whether by using high-density electroencephalography (EEG) independent component analysis, we can identify common cortical responses visual physical perturbations walking standing. We...

Virtual reality has been increasingly used in research on balance rehabilitation because it provides robust and novel sensory experiences controlled environments. We studied 19 healthy young subjects performing a beam walking task two virtual conditions with unaltered view (15 minutes each) to determine if high heights exposure induced stress. recorded number of steps off the beam, heart rate, electrodermal activity, response time an auditory cue, high-density electroencephalography (EEG)....

Maintaining balance is a complex process requiring multisensory processing and coordinated muscle activation. Previous studies have indicated that the cortex directly involved in control, but less information known about cortical flow of signals for balance. We studied source-localized electrocortical effective connectivity dynamics healthy young subjects (29 subjects: 14 male 15 female) walking standing with both visual physical perturbations to their The goal this study was quantify...

Immersive virtual reality can expose humans to novel training and sensory environments, but motor with has not been able improve performance as much in real-world conditions. An advantage of immersive that fully leveraged is it introduce transient visual perturbations on top the environment being displayed. The goal this study was determine whether introduced modify electrocortical activity behavioral outcomes human subjects practicing a balancing task during walking. We studied three groups...

Abstract Objective . Advances in neural decoding have enabled brain-computer interfaces to perform increasingly complex and clinically-relevant tasks. However, such decoders are often tailored specific participants, days, recording sites, limiting their practical long-term usage. Therefore, a fundamental challenge is develop that can robustly train on pooled, multi-participant data generalize new participants. Approach We introduce decoder, HTNet, which uses convolutional network with two...

Abstract Most sensory stimuli evoke spiking responses that are distributed across neurons and temporally structured. Whether the temporal structure of ensemble activity is modulated to facilitate different neural computations not known. Here, we investigated this issue in insect olfactory system. We found an odourant can generate synchronous or asynchronous a antennal lobe circuit depending on its relative novelty with respect preceding stimulus. Regardless variations patterns, activated...

Mobile electroencephalography (EEG) is a very useful tool to investigate the physiological basis of cognition under real-world conditions. However, as we move experimentation into less-constrained environments, influence state changes increases. The stress on cortical activity and an important example. Monitoring modulation by EEG measurements promising for assessing acute stress. In this study, test hypothesis combine with independent component analysis source localization identify...

Understanding the neural basis of human movement in naturalistic scenarios is critical for expanding neuroscience research beyond constrained laboratory paradigms. Here, we describe our Annotated Joints Long-term Electrocorticography 12 participants (AJILE12) dataset, largest neurobehavioral dataset that publicly available; was recorded opportunistically during passive clinical epilepsy monitoring. AJILE12 includes synchronized intracranial recordings and upper body pose trajectories across...

Due to its high temporal resolution, electroencephalography (EEG) has become a promising tool for quantifying cortical dynamics and effective connectivity in mobile setting. While many estimators are available, the efficacy of these measures not been rigorously validated real-world scenarios. The goal this study was quantify accuracy independent component analysis multiple on ground-truth connections while exposed volume conduction head motion.We collected high-density EEG from phantom with...

Motor behaviors are central to many functions and dysfunctions of the brain, understanding their neural basis has consequently been a major focus in neuroscience. However, most studies motor have restricted artificial, repetitive paradigms, far removed from natural movements performed "in wild." Here, we leveraged recent advances machine learning computer vision analyze intracranial recordings 12 human subjects during thousands spontaneous, unstructured arm reach movements, observed over...

Beam walking is a highly studied assessment of balance. Recent research has demonstrated that brief intermittent visual rotations and occlusions can increase the efficacy beam practice on subsequent without perturbations. We sought to examine influence full vision removal during treadmill-mounted balance beam. Although disruptions improved performance this task, we hypothesized removing feedback completely would lead less improvements than with normal due specificity practice.

A bstract Motor behaviors are central to many functions and dysfunctions of the brain, understanding their neural basis has consequently been a major focus in neuroscience. However, most studies motor have restricted artificial, repetitive paradigms, far removed from natural movements performed “in wild.” Here, we leveraged recent advances machine learning computer vision analyze intracranial recordings 12 human subjects during thousands spontaneous, unstructured arm reach movements,...

Objective.Recent advances in neural decoding have accelerated the development of brain-computer interfaces aimed at assisting users with everyday tasks such as speaking, walking, and manipulating objects. However, current approaches for training decoders commonly require large quantities labeled data, which can be laborious or infeasible to obtain real-world settings. Alternatively, self-supervised models that share self-generated pseudo-labels between two data streams shown exceptional...

Studying the neural correlates of sleep can lead to revelations in our understanding and its interplay with different neurological disorders. Sleep research relies on manual annotation stages based rules developed for healthy adults. Automating stage expedite enable us better understand atypical patterns. Our goal was create a fully unsupervised approach label wake states human electro-corticography (ECoG) data from epilepsy patients. Here, we demonstrate that continuous single ECoG...

Abstract Objective Advances in neural decoding have enabled brain-computer interfaces to perform increasingly complex and clinically-relevant tasks. However, such decoders are often tailored specific participants, days, recording sites, limiting their practical long-term usage. Therefore, a fundamental challenge is develop that can robustly train on pooled, multi-participant data generalize new participants. Approach We introduce decoder, HTNet, which uses convolutional network with two...

ABSTRACT Understanding the neural basis of human movement in naturalistic scenarios is critical for expanding neuroscience research beyond constrained laboratory paradigms. Here, we describe our Annotated Joints Long-term Electrocorticography 12 participants (AJILE12) dataset, largest neurobehavioral dataset that publicly available; was recorded opportunistically during passive clinical epilepsy monitoring. AJILE12 includes synchronized intracranial recordings and upper body pose...

A fully recurrent neural network and a rule-based expert system are combined in hybrid architecture to provide power plant operators with an intelligent on-line advisory system. Its purpose is alert the operator impending or occurring abnormal conditions related plant's boiler. The trained model of boiler under normal operation, while rules address general set diagnostic events. Deviation from trigger suggest corrective action. This intended increase availability efficiency by automatically...

Abstract Recent advances in neural decoding have accelerated the development of brain-computer interfaces aimed at assisting users with everyday tasks such as speaking, walking, and manipulating objects. However, current approaches for training decoders commonly require large quantities labeled data, which can be laborious or infeasible to obtain real-world settings. One intriguing alternative uses self-supervised models that share self-generated pseudo-labels between two data streams; shown...

Active balance control is critical for performing many of our everyday activities. Our nervous systems rely on multiple sensory inputs to inform cortical processing, leading coordinated muscle actions that maintain balance. However, such processing can be challenging record during mobile tasks due limitations in noninvasive neuroimaging and motion artifact contamination. Here, we present a synchronized, multi-modal dataset from 30 healthy, young human participants standing walking while...