scholarly journals A Gel‐Free Ti 3 C 2 T x ‐Based Electrode Array for High‐Density, High‐Resolution Surface Electromyography

2020 ◽  
Vol 5 (8) ◽  
pp. 2000325 ◽  
Author(s):  
Brendan B. Murphy ◽  
Patrick J. Mulcahey ◽  
Nicolette Driscoll ◽  
Andrew G. Richardson ◽  
Gregory T. Robbins ◽  
...  
Keyword(s):  
High Resolution ◽  
Surface Electromyography ◽  
Electrode Array ◽  
High Density

scholarly journals A modular high-density 294-channel μECoG system on macaque vlPFC for auditory cognitive decoding

2019 ◽  
Author(s):  
Chia-Han Chiang ◽  
Jaejin Lee ◽  
Charles Wang ◽  
Ashley J. Williams ◽  
Timothy H. Lucas ◽  
...  
Keyword(s):  
High Resolution ◽  
Auditory Perception ◽  
Large Scale ◽  
Electrode Array ◽  
Auditory Pathway ◽  
Sound Level ◽  
High Density ◽  
Electrode Arrays ◽  
Spatiotemporal Resolution

AbstractOBJECTIVEA fundamental goal of the auditory system is to parse the auditory environment into distinct perceptual representations. Auditory perception is mediated by the ventral auditory pathway, which includes the ventrolateral prefrontal cortex (vlPFC) late. Because large-scale recordings of auditory signals are quite rare, the spatiotemporal resolution of the neuronal code that underlies vlPFC’s contribution to auditory perception has not been fully elucidated. Therefore, we developed a modular, chronic, high-resolution, multi-electrode array system with long-term viability.APPROACHWe molded three separate μECoG arrays into one and implanted this system in a non-human primate. A custom 3D-printed titanium chamber was mounted on left hemisphere. The molded 294-contact μECoG array was implanted subdurally over vlPFC. μECoG activity was recorded while the monkey participated in a “hearing-in-noise” task in which they reported hearing a “target” vocalization from a background “chorus” of vocalizations. We titrated task difficulty by varying the sound level of the target vocalization, relative to the chorus (target-to-chorus ratio, TCr).MAIN RESULTSWe decoded the TCr and the monkey’s behavioral choices from the μECoG signal. We analyzed decoding capacity as a function of neuronal frequency band, spatial resolution, and time from implantation. Over a one-year period, we were successfully able to record μECoG signals. Although we found significant decoding with as few as two electrodes, we found near-perfect decoding with ∼16 electrodes. Decoding further improved when we included more electrodes. Finally, because the decoding capacity of individual electrodes varied on a day-by-day basis, high-density electrode arrays ensure robust decoding in the long term.SIGNIFICANCEOur results demonstrate the utility and robustness of high-resolution chronic µECoG recording. We developed a new high-resolution surface electrode array that can be scaled to cover larger cortical areas without increasing the chamber footprint.

Bipolar versus high-density surface electromyography for evaluating risk in fatiguing frequency-dependent lifting activities

2021 ◽  
Vol 95 ◽  
pp. 103456
Author(s):  
Tiwana Varrecchia ◽  
Alberto Ranavolo ◽  
Silvia Conforto ◽  
Alessandro Marco De Nunzio ◽  
Michail Arvanitidis ◽  
...  
Keyword(s):  
Surface Electromyography ◽  
High Density ◽  
Frequency Dependent ◽  
Density Surface

3-D High-Density Strain Mapping Procedure Based on High-Resolution CT

2007 ◽  
Vol 539-543 ◽  
pp. 2377-2382 ◽  
Author(s):  
Masakazu Kobayashi ◽  
Hiroyuki Toda ◽  
Tomomi Ohgaki ◽  
Kentaro Uesugi ◽  
David S. Wilkinson ◽  
...  
Keyword(s):  
High Resolution ◽  
Local Strain ◽  
High Ratio ◽  
High Density ◽  
Strain Mapping ◽  
Mapping Procedure ◽  
Model Method ◽  
Model Sample ◽  
Alumina Particles ◽  
X Ray Computed

A tracking procedure for the high-resolution X-ray computed tomography (CT) has been developed in order to measure 3-D local strain within a deforming material in high-density. A dispersion-strengthened copper alloy model sample with alumina particles, which contains micropores, was visualized by the synchrotron radiation CT. The pores observed in reconstructed CT volumes were used as tracking markers. The developed tracking method using a set of matching parameters, which classifies matched, pended and rejected markers, exhibited high ratio of success tracking. Furthermore, the ratio was improved by applying the spring model method, which is one of the particle image velocity (PIV) methods utilized in the field of the fluid mechanics, to the pended markers. The method based on the image analysis of CT imaging volumes provides us 3-D high-density strain mapping.

scholarly journals Deep Heterogeneous Dilation of LSTM for Transient-phase Gesture Prediction through High-density Electromyography: Application in Neurorobotics

2021 ◽  
Author(s):  
Tianyun Sun ◽  
Qin Hu ◽  
Jacqueline Libby ◽  
S. Farokh Atashzar
Keyword(s):  
Surface Electromyography ◽  
Complex Model ◽  
High Density ◽  
Transient Phase ◽  
Spatiotemporal Resolution ◽  
Semg Signal ◽  
Training Time ◽  
Motor Intention ◽  
Density Surface ◽  
Human Robot Interfaces

Deep networks have been recently proposed to estimate motor intention using conventional bipolar surface electromyography (sEMG) signals for myoelectric control of neurorobots. In this regard, deepnets are generally challenged by long training times (affecting the practicality and calibration), complex model architectures (affecting the predictability of the outcomes), a large number of trainable parameters (increasing the need for big data), and possibly overfitting. Capitalizing on our recent work on homogeneous temporal dilation in a Recurrent Neural Network (RNN) model, this paper proposes, for the first time, heterogeneous temporal dilation in an LSTM model and applies that to high-density surface electromyography (HD-sEMG), allowing for decoding dynamic temporal dependencies with tunable temporal foci. In this paper, a 128-channel HD-sEMG signal space is considered due to the potential for enhancing the spatiotemporal resolution of human-robot interfaces. Accordingly, this paper addresses a challenging motor intention decoding problem of neurorobots, namely, transient intention identification. The aforementioned problem only takes into account the dynamic and transient phase of gesture movements when the signals are not stabilized or plateaued, addressing which can significantly enhance the temporal resolution of human-robot interfaces. This would eventually enhance seamless real-time implementations. Additionally, this paper introduces the concept of dilation foci to modulate the modeling of temporal variation in transient phases. In this work a high number (i.e. 65) of gestures is included, which adds to the complexity and significance of the understudied problem. Our results show state-of-the-art performance for gesture prediction in terms of accuracy, training time, and model convergence.

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