Ogdensburg, NY, United States
Ogdensburg, NY, United States
SEARCH FILTERS
Time filter
Source Type

Okorokova E.,National Research University Higher School of Economics | Lebedev M.,Duke University | Linderman M.,Norconnect Inc | Ossadtchi A.,National Research University Higher School of Economics
Frontiers in Neuroscience | Year: 2015

In recent years, several assistive devices have been proposed to reconstruct arm and hand movements from electromyographic (EMG) activity. Although simple to implement and potentially useful to augment many functions, such myoelectric devices still need improvement before they become practical. Here we considered the problem of reconstruction of handwriting from multichannel EMG activity. Previously, linear regression methods (e.g., the Wiener filter) have been utilized for this purpose with some success. To improve reconstruction accuracy, we implemented the Kalman filter, which allows to fuse two information sources: the physical characteristics of handwriting and the activity of the leading hand muscles, registered by the EMG. Applying the Kalman filter, we were able to convert eight channels of EMG activity recorded from the forearm and the hand muscles into smooth reconstructions of handwritten traces. The filter operates in a causal manner and acts as a true predictor utilizing the EMGs from the past only, which makes the approach suitable for real-time operations. Our algorithm is appropriate for clinical neuroprosthetic applications and computer peripherals. Moreover, it is applicable to a broader class of tasks where predictive myoelectric control is needed. © 2015 Okorokova, Lebedev, Linderman and Ossadtchi.


Rupasov V.I.,Norconnect Inc | Lebedev M.A.,Duke University | Erlichman J.S.,St. Lawrence University | Linderman M.,Norconnect Inc
PLoS ONE | Year: 2012

To elucidate the cortical control of handwriting, we examined time-dependent statistical and correlational properties of simultaneously recorded 64-channel electroencephalograms (EEGs) and electromyograms (EMGs) of intrinsic hand muscles. We introduced a statistical method, which offered advantages compared to conventional coherence methods. In contrast to coherence methods, which operate in the frequency domain, our method enabled us to study the functional association between different neural regions in the time domain. In our experiments, subjects performed about 400 stereotypical trials during which they wrote a single character. These trials provided time-dependent EMG and EEG data capturing different handwriting epochs. The set of trials was treated as a statistical ensemble, and time-dependent correlation functions between neural signals were computed by averaging over that ensemble. We found that trial-to-trial variability of both the EMGs and EEGs was well described by a log-normal distribution with time-dependent parameters, which was clearly distinguished from the normal (Gaussian) distribution. We found strong and long-lasting EMG/EMG correlations, whereas EEG/EEG correlations, which were also quite strong, were short-lived with a characteristic correlation durations on the order of 100 ms or less. Our computations of correlation functions were restricted to the β spectral range (13-30 Hz) of EEG signals where we found the strongest effects related to handwriting. Although, all subjects involved in our experiments were right-hand writers, we observed a clear symmetry between left and right motor areas: inter-channel correlations were strong if both channels were located over the left or right hemispheres, and 2-3 times weaker if the EEG channels were located over different hemispheres. Although we observed synchronized changes in the mean energies of EEG and EMG signals, we found that EEG/EMG correlations were much weaker than EEG/EEG and EMG/EMG correlations. The absence of strong correlations between EMG and EEG signals indicates that (i) a large fraction of the EEG signal includes electrical activity unrelated to low-level motor variability; (ii) neural processing of cortically-derived signals by spinal circuitry may reduce the correlation between EEG and EMG signals. © 2012 Rupasov et al.


Rupasov V.I.,Norconnect Inc | Lebedev M.A.,Duke University | Erlichman J.S.,St. Lawrence University | Linderman M.,Norconnect Inc
PLoS ONE | Year: 2012

We examined time-dependent statistical properties of electromyographic (EMG) signals recorded from intrinsic hand muscles during handwriting. Our analysis showed that trial-to-trial neuronal variability of EMG signals is well described by the lognormal distribution clearly distinguished from the Gaussian (normal) distribution. This finding indicates that EMG formation cannot be described by a conventional model where the signal is normally distributed because it is composed by summation of many random sources. We found that the variability of temporal parameters of handwriting - handwriting duration and response time - is also well described by a lognormal distribution. Although, the exact mechanism of lognormal statistics remains an open question, the results obtained should significantly impact experimental research, theoretical modeling and bioengineering applications of motor networks. In particular, our results suggest that accounting for lognormal distribution of EMGs can improve biomimetic systems that strive to reproduce EMG signals in artificial actuators. © 2012 Rupasov et al.


Biometric assessment is performed by use of electromyography (EMG) signals detected from muscles at several locations on the hand/or other part of the body subject to fine motor control. In addition, electroencephalography (EEG), signals detect other biomarkers. The EMG and EEG signals are sensed, synchronized and registered. The signals are converted into digital data and are stored and processed for use in performing the biometric assessment.


Patent
Norconnect Inc | Date: 2013-03-01

A system for identifying the connectivity between different brain regions to determine the functional role of brain regions in various human and animal actions.


Grant
Agency: Department of Defense | Branch: Air Force | Program: SBIR | Phase: Phase I | Award Amount: 99.91K | Year: 2011

ABSTRACT: Norconnect, Inc. proposes to develop a unique digital flight glove electronic system which will i) record electromyographic (EMG) signals generated by hand muscles of a pilot or a flight engineer when they move their fingers or hands in the air, ii) recognize recorded EMG signals with accuracy of 99%, and finally, iii) translate recognized signals into computer commands for real-time switch activation, typing of reports, annotation of geo-registered icons, etc. In Phase I of the project, we will utilize our laboratory setup available at Norconnect to develop recognition algorithms for the proposed digital glove electronic system. We will identify and optimize the number of sensors and other system parameters that will assure recognition accuracy of 99%. In Phase II of the project we will develop miniature electronics to be embedded into currently manufactured flight gloves without any essential modification of military specification MIL-G-181188B. We will also fabricate prototype systems and completely optimize their parameters. We expect that, in addition to applications by airline and general aviation pilots, police, border security personnel, road warriors and computer gamers, the proposed digital glove technology will find numerous applications in bio-medical systems that interface human bioelectric activity to external devices. EMG signal recognition, digital flight gloves, gesture control, real-world annotation, hand-wear computer input devices BENEFIT: In addition to applications by airline and general aviation pilots, police, border security personnel, road warriors and computer gamers, the proposed digital glove technology will find numerous applications in bio-medical systems that interface human bioelectric activity to external devices


PubMed | Norconnect Inc
Type: Journal Article | Journal: PloS one | Year: 2012

We examined time-dependent statistical properties of electromyographic (EMG) signals recorded from intrinsic hand muscles during handwriting. Our analysis showed that trial-to-trial neuronal variability of EMG signals is well described by the lognormal distribution clearly distinguished from the Gaussian (normal) distribution. This finding indicates that EMG formation cannot be described by a conventional model where the signal is normally distributed because it is composed by summation of many random sources. We found that the variability of temporal parameters of handwriting--handwriting duration and response time--is also well described by a lognormal distribution. Although, the exact mechanism of lognormal statistics remains an open question, the results obtained should significantly impact experimental research, theoretical modeling and bioengineering applications of motor networks. In particular, our results suggest that accounting for lognormal distribution of EMGs can improve biomimetic systems that strive to reproduce EMG signals in artificial actuators.


PubMed | National Research University Higher School of Economics, Duke University and Norconnect Inc
Type: | Journal: Frontiers in neuroscience | Year: 2015

In recent years, several assistive devices have been proposed to reconstruct arm and hand movements from electromyographic (EMG) activity. Although simple to implement and potentially useful to augment many functions, such myoelectric devices still need improvement before they become practical. Here we considered the problem of reconstruction of handwriting from multichannel EMG activity. Previously, linear regression methods (e.g., the Wiener filter) have been utilized for this purpose with some success. To improve reconstruction accuracy, we implemented the Kalman filter, which allows to fuse two information sources: the physical characteristics of handwriting and the activity of the leading hand muscles, registered by the EMG. Applying the Kalman filter, we were able to convert eight channels of EMG activity recorded from the forearm and the hand muscles into smooth reconstructions of handwritten traces. The filter operates in a causal manner and acts as a true predictor utilizing the EMGs from the past only, which makes the approach suitable for real-time operations. Our algorithm is appropriate for clinical neuroprosthetic applications and computer peripherals. Moreover, it is applicable to a broader class of tasks where predictive myoelectric control is needed.


PubMed | National Research University Higher School of Economics, Duke University and Norconnect Inc
Type: | Journal: Frontiers in neuroscience | Year: 2016

[This corrects the article on p. 389 in vol. 9, PMID: 26578856.].


PubMed | Norconnect Inc
Type: Journal Article | Journal: PloS one | Year: 2012

To elucidate the cortical control of handwriting, we examined time-dependent statistical and correlational properties of simultaneously recorded 64-channel electroencephalograms (EEGs) and electromyograms (EMGs) of intrinsic hand muscles. We introduced a statistical method, which offered advantages compared to conventional coherence methods. In contrast to coherence methods, which operate in the frequency domain, our method enabled us to study the functional association between different neural regions in the time domain. In our experiments, subjects performed about 400 stereotypical trials during which they wrote a single character. These trials provided time-dependent EMG and EEG data capturing different handwriting epochs. The set of trials was treated as a statistical ensemble, and time-dependent correlation functions between neural signals were computed by averaging over that ensemble. We found that trial-to-trial variability of both the EMGs and EEGs was well described by a log-normal distribution with time-dependent parameters, which was clearly distinguished from the normal (Gaussian) distribution. We found strong and long-lasting EMG/EMG correlations, whereas EEG/EEG correlations, which were also quite strong, were short-lived with a characteristic correlation durations on the order of 100 ms or less. Our computations of correlation functions were restricted to the [Formula: see text] spectral range (13-30 Hz) of EEG signals where we found the strongest effects related to handwriting. Although, all subjects involved in our experiments were right-hand writers, we observed a clear symmetry between left and right motor areas: inter-channel correlations were strong if both channels were located over the left or right hemispheres, and 2-3 times weaker if the EEG channels were located over different hemispheres. Although we observed synchronized changes in the mean energies of EEG and EMG signals, we found that EEG/EMG correlations were much weaker than EEG/EEG and EMG/EMG correlations. The absence of strong correlations between EMG and EEG signals indicates that (i) a large fraction of the EEG signal includes electrical activity unrelated to low-level motor variability; (ii) neural processing of cortically-derived signals by spinal circuitry may reduce the correlation between EEG and EMG signals.

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