757 research outputs found
Charles Henderson Presents Plaque to Winnie Jensen
No full textCharles Henderson, left, presents Winnie Jensen a plaque in memory of Dale Jensen and his work in the Rod and Gun Club as sportsman and conservationist. Jim Jensen is to the right
Interactions among biotic and abiotic factors affect the reliability of tungsten microneedles puncturing in vitro and in vivo peripheral nerves:A hybrid computational approach
Full text linkTungsten is an elective material to produce slender and stiff microneedles able to enter soft tissues and minimize puncture wounds. In particular, tungsten microneedles are used to puncture peripheral nerves and insert neural interfaces, bridging the gap between the nervous system and robotic devices (e.g., hand prostheses). Unfortunately, microneedles fail during the puncture process and this failure is not dependent on stiffness or fracture toughness of the constituent material. In addition, the microneedles' performances decrease during in vivo trials with respect to the in vitro ones. This further effect is independent on internal biotic effects, while it seems to be related to external biotic causes. Since the exact synergy of phenomena decreasing the in vivo reliability is still not known, this work explored the connection between in vitro and in vivo behavior of tungsten microneedles through the study of interactions between biotic and abiotic factors. A hybrid computational approach, simultaneously using theoretical relationships and in silico models of nerves, was implemented to model the change of reliability varying the microneedle diameter, and to predict in vivo performances by using in vitro reliability and local differences between in vivo and in vitro mechanical response of nerves.</p
Spike detection and clustering with unsupervised wavelet optimization in extracellular neural recordings
Full text linkAutomatic and accurate detection of action potentials of unknown waveforms in noisy extracellular neural recordings is an important requirement for developing brain-computer interfaces. This study introduces a new, wavelet-based manifestation variable that combines the wavelet shrinkage denoising with multiscale edge detection for robustly detecting and finding the occurrence time of action potentials in noisy signals. To further improve the detection performance by eliminating the dependence of the method to the choice of the mother wavelet, we propose an unsupervised optimization for best basis selection. Moreover, another unsupervised criterion based on a correlation similarity measure was defined to update the wavelet selection during the clustering to improve the spike sorting performance. The proposed method was compared to several previously proposed methods by using a wide range of realistic simulated data as well as selected experimental recordings of intracortical signals from freely moving rats. The detection performance of the proposed method substantially surpassed previous methods for all signals tested. Moreover, updating the wavelet selection for the clustering task was shown to improve the classification performance with respect to maintaining the same wavelet as for the detection stage.Iran University of Science and Technology, Tehran, Ira
Winnie Wyman
No full textWinifred (Winnie) Wyman is the daughter of Henry and Mary Wyman of Jensen. She married Leslie Odell Johnson in September 1927. She died March 15, 1998
Leslie and Winnie Johnson
No full textLeslie Odell Johnson, son of Bejamin and Emily Johnson of Duchesne County, married Winnie Wyman, daughter of Henry and Mary Wyman of Jensen, Utah
Novel approach for investigation of neuronal alterations following ischemic stroke in a rat model
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Simulation of a real-time brain computer interface for detecting a self-paced hitting task
No full textOBJECTIVES: An invasive brain-computer interface (BCI) is a promising neurorehabilitation device for severely disabled patients. Although some systems have been shown to work well in restricted laboratory settings, their utility must be tested in less controlled, real-time environments. Our objective was to investigate whether a specific motor task could be reliably detected from multiunit intracortical signals from freely moving animals in a simulated, real-time setting.MATERIALS AND METHODS: Intracortical signals were first obtained from electrodes placed in the primary motor cortex of four rats that were trained to hit a retractable paddle (defined as a "Hit"). In the simulated real-time setting, the signal-to-noise-ratio was first increased by wavelet denoising. Action potentials were detected, and features were extracted (spike count, mean absolute values, entropy, and combination of these features) within pre-defined time windows (200 ms, 300 ms, and 400 ms) to classify the occurrence of a "Hit."RESULTS: We found higher detection accuracy of a "Hit" (73.1%, 73.4%, and 67.9% for the three window sizes, respectively) when the decision was made based on a combination of features rather than on a single feature. However, the duration of the window length was not statistically significant (p = 0.5).CONCLUSION: Our results showed the feasibility of detecting a motor task in real time in a less restricted environment compared to environments commonly applied within invasive BCI research, and they showed the feasibility of using information extracted from multiunit recordings, thereby avoiding the time-consuming and complex task of extracting and sorting single units.</p
Simultaneous and proportional force estimation in multiple degrees of freedom from intramuscular EMG
No full textThis letter investigates simultaneous and proportional estimation of force in 2 degree-of-freedoms (DoFs) from intramuscular electromyography (EMG). Intramuscular EMG signals from three able-bodied subjects were recorded along with isometric forces in multiple DoF from the right arm. The association between five EMG features and force profiles was modeled using an artificial neural network. Correlation coefficients between the measured and the estimated forces were 0.85 +/- 0.056 and 0.88 +/- 0.05 without and with post processing, respectively. The results showed that force can be estimated in 2 DoFs with high accuracy and that the degree of performance depended on the force function (task) to be estimated
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