Department of Electrical Engineering Faculty Research and Papers

Permanent URI for this collectionhttps://digital.lib.washington.edu/handle/1773/15629

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How Did the Landscape of Student Belonging Shift During COVID-19?
(2023-06-21) Misra, Shruti; Kardam, Neha; VanAntwerp, Jennifer; Wilson, Denise
The goal of this study is to understand if and how emergency remote teaching (ERT) used during the COVID-19 pandemic changed the ways in which instructional support and interactions were linked to belonging among engineering students. Belonging is a fundamental human motivation associated with a wide range of positive psychological, educational, social, and job outcomes. Frequent and predominantly conflict-free interactions within a stable, relational framework of caring are required to facilitate belonging. To better understand potential shifts in belonging that occurred from pre-pandemic to mid-pandemic, this study used survey data from a cross-sectional dataset at a single, large institution comprised of sophomore to senior level students (N = 1,485) enrolled in engineering courses between 2016 and 2021. Hierarchical linear modeling (HLM) was used to study relationships among instructional support, instructor interactions, and belonging. The HLM models of ERT and traditional learning differed dramatically. In traditional classroom learning, race, interactions with faculty and teaching assistants (TAs), and instructional support were important factors in belonging. In ERT, certain motivations to study engineering (altruism; desire to build things) had nuanced associations with belonging, while race and interactions with faculty as well as with TAs became largely irrelevant. Most concerning, however, faculty interactions in traditional learning were negatively associated with belonging, indicating a need for a deeper understanding of the impact of those interactions. Further, the differences in the HLM models suggest that rather than returning to pre-pandemic traditional learning, a hybrid model that offers a more level playing field for marginalized students to find belonging in the classroom is recommended. In developing such models, faculty must take special care to avoid having a potentially negative impact on student belonging.
UW Sinus Surgery Cadaver/Live Dataset (UW-Sinus-Surgery-C/L)
(2020) Lin, Shan; Qin, Fangbo; Bly, Randall A.; Moe, Kris S.; Hannaford, Blake
This dataset was developed at the University of Washington's BioRobotics Lab (http://brl.ee.washington.edu). It has endoscopic sinus surgery images with manual annotations for surgical instrument segmentation task. The challenging conditions of this dataset include specular reflections, blur from motion, blood, smoke and tools in shadow.
Short Time Fourier Analysis of the Electromyogram
Hannaford, Blake || Lehman, Steven
We applied short-time Fourier analysis to surface electromyograms (EMG) recorded during rapid movements, and during isometric contractions at constant forces. We selected a portion of the data to be transformed by multiplying the signal by a Hamming window, then computed the discrete Fourier transform. Shifting the window along the data record, we computed a new spectrum each 10 ms. We displayed the transformed data in spectrograms or "voiceprints." This short-time technique allowed us to see time-dependencies in the EMG that are normally averaged in the Fourier analysis of these signals. Spectra of EMG's during isometric contractions at constant force vary in the short (10-20 ms) term. Moments of the spectral distribution show this variability. Short-time spectra from EMG's recorded during rapid movements were much less variable. The windowing technique picked out the typical "three-burst pattern" in EMG's from both wrist and head movements. Spectra during the bursts were more consistent than those during isometric contractions. Furthermore, there was a consistent shift in spectral statistics in the course of the three bursts. Both the center frequency and the variance of the spectral energy distribution grew from from the first burst to the second burst in the same muscle. We discuss this pattern with respect to the origin of the EMG bursts in rapid movement. We also extend the analogy between electromyograms and speech signals to argue for future applicability of short-time spectral analysis of EMG.
Personal Responsibility in the Age of User-Controlled Neuroprosthetics
Brown, Timothy || Moore, Patrick || Herron, Jeffrey || Thompson, Margaret || Bonaci, Tamara || Goering, Sara || Chizeck, Howard Jay
Deep-brain stimulation systems are an accepted and clinically effective form of neuroprosthetic treatment for a variety of common and debilitating neurological movement disorders
Dual Stable Point Model of Muscle Activation and Deactivation
Chou, C.P. || Hannaford, B.
Two dynamic models of muscle activation and deactivation based on the concepts of ion transport, reaction rates, and muscle mechanics are proposed. Storage release and uptake of calcium by the sarcoplasmic reticulum, and a two-step chemical reaction of calcium and troponin are included in the first model. This is a concise version of the complex chemical reactions of muscle activation and deactivation in sarcoplasm. The second model is similar to the first, but calcium-troponin reactions are simplified into two non-linear rates functions. Due to these nonlinear dynamics, the second model can explain the catch-like enhancement of isometric force response. Simulation results which match experimental data are shown. Also, two new phenomena which need further experiment to verify are predicted by the second model.
Influence of head models on neuromagnetic fields and inverse source localizations
(2006) Ramon, Ceon; Haueisen, Jens; Schimpf, Paul H.
Background: The magnetoencephalograms (MEGs) are mainly due to the source currents. However, there is a significant contribution to MEGs from the volume currents. The structure of the anatomical surfaces, e.g., gray and white matter, could severely influence the flow of volume currents in a head model. This, in turn, will also influence the MEGs and the inverse source localizations. This was examined in detail with three different human head models. Methods: Three finite element head models constructed from segmented MR images of an adult male subject were used for this study. These models were: (1) Model 1: full model with eleven tissues that included detailed structure of the scalp, hard and soft skull bone, CSF, gray and white matter and other prominent tissues, (2) the Model 2 was derived from the Model 1 in which the conductivity of gray matter was set equal to the white matter, i.e., a ten tissuetype model, (3) the Model 3 consisted of scalp, hard skull bone, CSF, gray and white matter, i.e., a five tissue-type model. The lead fields and MEGs due to dipolar sources in the motor cortex were computed for all three models. The dipolar sources were oriented normal to the cortical surface and had a dipole moment of 100 [micro]A meter. The inverse source localizations were performed with an exhaustive search pattern in the motor cortex area. A set of 100 trial inverse runs was made covering the 3 cm cube motor cortex area in a random fashion. The Model 1 was used as a reference model. Results: The reference model (Model 1), as expected, performed best in localizing the sources in the motor cortex area. The Model 3 performed the worst. The mean source localization errors (MLEs) of the Model 3 were larger than the Model 1 or 2. The contour plots of the magnetic fields on top of the head were also different for all three models. The magnetic fields due to source currents were larger in magnitude as compared to the magnetic fields of volume currents. Discussion: These results indicate that the complexity of head models strongly influences the MEGs and the inverse source localizations. A more complex head model performs better in inverse source localizations as compared to a model with lesser tissue surfaces.
Influence of head models on EEG simulations and inverse source localizations
(2006) Ramon, Ceon; Schimpf, Paul H.; Haueisen, Jens
Background: The structure of the anatomical surfaces, e.g., CSF and gray and white matter, could severely influence the flow of volume currents in a head model. This, in turn, will also influence the scalp potentials and the inverse source localizations. This was examined in detail with four different human head models. Methods: Four finite element head models constructed from segmented MR images of an adult male subject were used for this study. These models were: (1) Model 1: full model with eleven tissues that included detailed structure of the scalp, hard and soft skull bone, CSF, gray and white matter and other prominent tissues, (2) the Model 2 was derived from the Model 1 in which the conductivity of gray matter was set equal to the white matter, i.e., a ten tissue-type model, (3) the Model 3 was derived from the Model 1 in which the conductivities of gray matter and CSF were set equal to the white matter, i.e., a nine tissue-type model, (4) the Model 4 consisted of scalp, hard skull bone, CSF, gray and white matter, i.e., a five tissue-type model. How model complexity influences the EEG source localizations was also studied with the above four finite element models of the head. The lead fields and scalp potentials due to dipolar sources in the motor cortex were computed for all four models. The inverse source localizations were performed with an exhaustive search pattern in the motor cortex area. The inverse analysis was performed by adding uncorrelated Gaussian noise to the scalp potentials to achieve a signal to noise ratio (SNR) of -10 to 30 dB. The Model 1 was used as a reference model. Results: The reference model, as expected, performed the best. The Model 3, which did not have the CSF layer, performed the worst. The mean source localization errors (MLEs) of the Model 3 were larger than the Model 1 or 2. The scalp potentials were also most affected by the lack of CSF geometry in the Model 3. The MLEs for the Model 4 were also larger than the Model 1 and 2. The Model 4 and the Model 3 had similar MLEs in the SNR range of -10 dB to 0 dB. However, in the SNR range of 5 dB to 30 dB, the Model 4 has lower MLEs as compared with the Model 3. Discussion: These results indicate that the complexity of head models strongly influences the scalp potentials and the inverse source localizations. A more complex head model performs better in inverse source localizations as compared to a model with lesser tissue surfaces. The CSF layer plays an important role in modifying the scalp potentials and also influences the inverse source localizations. In summary, for best results one needs to have highly heterogeneous models of the head for accurate simulations of scalp potentials and for inverse source localizations.
Synchronization analysis of the uterine magnetic activity during contractions
(2005) Ramon, Ceon; Preissl, Hubert; Murphy, Pam; Wilson, James D.; Lowery, Curtis; Eswaran, Hari
Background: Our objective was to quantify and compare the extent of synchronization of the spatial-temporal myometrial activity over the human uterus before and during a contraction using transabdominal magnetomyographic (MMG) recordings. Synchronization can be an important indicator for the quantification of uterine contractions. Methods: The spatialtermporal myometrial activity recordings were performed using a 151-channel noninvasive magnetic sensor system called SARA. This device covers the entire pregnant abdomen and records the magnetic field corresponding to the electrical activity generated in the uterine myometrium. The data was collected at 250 samples/sec and was resampled with 25 samples/sec and then filtered in the band of 0.1-0.2 Hz to study the primary magnetic activity of the uterus related to contractions. The synchronization between a channel pair was computed. It was inferred from a statistical tendency to maintain a nearly constant phase difference over a given period of time even though the analytic phase of each channel may change markedly during that time frame. The analytic phase was computed after taking Hilbert transform of the magnetic field data. The process was applied on the pairs of magnetic field traces (240 sec length) with a stepping window of 20 sec duration which is long enough to cover two cycle of the lowest frequency of interest (0.1 Hz). The analysis was repeated by stepping the window at 10 sec intervals. The spatial patterns of the synchronization indices covering the anterior transabdominal area were computed. For this, regional coil-pairs were used. For a given coil, the coil pairs were constructed with the surrounding six coils. The synchronization indices were computed for each coil pair, averaged over the 21 coil-pairs and then assigned as the synchronization index to that particular coil. This procedure was tested on six pregnant subjects at the gestational age between 29 and 40 weeks admitted to the hospital for contractions. The RMS magnetic field for each coil was also computed. Results: The results show that the spatial patterns of the synchronization indices change and follow the periodic pattern of the uterine contraction cycle. Spatial patterns of synchronization indices and the RMS magnetic fields show similarities in few window frames and also show large differences in few other windows. For six subjects, the average synchronization indices were: 0.346 [plus or minus] 0.068 for the quiescent baseline period and 0.545 [plus or minus] 0.022 at the peak of the contraction. Discussion: These results show that synchronization indices and their spatial distributions depict uterine contractions and relaxations.

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