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Development of EEG-Based Optimal Biomarkers for Decoding Fatigue during Motor Imagery Tasks

Development of EEG-Based Optimal Biomarkers for Decoding Fat...

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International Winter Workshop on Brain-Computer Interface (BCI)

作者： Jun-Seok Lee Seung-Won Lee Sang-Rok Kim Han-Jeong Hwang Dept. Electronics and Information Engineering Korea University Sejong Republic of Korea Interdisciplinary Graduate Program for Artificial Intelligence Smart Convergence Technology Korea University Sejong Republic of Korea

ISBN: (数字)9798331521929

ISBN: (纸本)9798331521936

This study aimed to identify electroencephalography (EEG)-based neurophysiological biomarkers for monitoring mental fatigue during motor imagery (MI) tasks. We used EEG data recorded from 29 healthy adults during MI tasks. For the preprocessing of the EEG data, we sequentially performed down-sampling into 200 Hz, independent component analysis (ICA), band-pass filtering between 0.5 and 50 Hz, and common average reference (CAR). The preprocessed EEG data were analyzed using a fast Fourier transform (FFT) to calculate spectral power in the δ (0.5-3 Hz), θ (4–7 Hz), $a$ (8–13 Hz), and β (13–30 Hz) frequency bands. Subsequently, seventeen fatigue-related metrics were calculated using individual spectral powers and their combinations. These fatigue-related metrics were averaged for the frontal lobe (F3 and F4), central lobe (FCC3h and FCC4h), parietal lobe (P3 and P4), and occipital lobe (POO1 and POO2). The statistical significance of the average fatigue metrics was evaluated through Pearson correlation analysis with the sequence of trials. As a result, we observed strong positive correlations (r < 0.9, p < 0.001) between beta power in the frontal and central regions and the sequence of trials. These findings suggest that central and frontal beta activity can serve as reliable biomarkers for estimating fatigue during MI tasks, supporting their potential for real-time fatigue monitoring.

关键词： Measurement Correlation Fast Fourier transforms Biomarkers Fatigue Motors Electroencephalography Decoding Reliability Monitoring

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EXOT: Exit-aware Object Tracker for Safe Robotic Manipulation of Moving Object

EXOT: Exit-aware Object Tracker for Safe Robotic Manipulatio...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Hyunseo Kim Hye Jung Yoon Minji Kim Dong-Sig Han Byoung-Tak Zhang Interdisciplinary Program in Neuroscience Seoul National University Seoul Korea Interdisciplinary Program in Artificial Intelligence Seoul National University Dept. of Computer Science and Engineering Seoul National University AIIS Seoul National University

Current robotic hand manipulation narrowly operates with objects in predictable positions in limited environments. Thus, when the location of the target object deviates severely from the expected location, a robot sometimes responds in an unexpected way, especially when it operates with a human. For safe robot operation, we propose the EXit-aware Object Tracker (EXOT) on a robot hand camera that recognizes an object's absence during manipulation. The robot decides whether to proceed by examining the tracker's bounding box output containing the target object. We adopt an out-of-distribution classifier for more accurate object recognition since trackers can mistrack a background as a target object. To the best of our knowledge, our method is the first approach of applying an out-of-distribution classification technique to a tracker output. We evaluate our method on the first-person video benchmark dataset, TREK-150, and on the custom dataset, RMOT-223, that we collect from the UR5e robot. Then we test our tracker on the UR5e robot in real-time with a conveyor-belt sushi task, to examine the tracker's ability to track target dishes and to determine the exit status. Our tracker shows 38% higher exit-aware performance than a baseline method. The dataset and the code will be released at https://***/hskAlena/EXOT.

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The Combined Localization Based on TDOA and FDOA for High-Speed Moving Object

The Combined Localization Based on TDOA and FDOA for High-Sp...

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International Symposium on Soft Computing and Intelligent Systems (SCIS)

作者： Seul-Bi Jeon Tae-Ho Jo Jong-Hwan Park Suk-Seung Hwang Dept. of Electronic Engineering Interdisciplinary Program in IT-Bio Convergence System Chosun University Gwangju Korea Satellite System Lab Soletop Co. Ltd Daejeon Korea Interdisciplinary Program in IT-Bio Convergence System School of Electronic Eigineering Chosun University Gwangju Korea

ISBN: (数字)9798350373332

ISBN: (纸本)9798350373349

Detecting the location of high-speed moving objects is a critical issue in various fields including the medical, emergence service, aerospace, defense areas, etc. In general, Time Difference of Arrival (TDOA) and Frequency Difference of Arrival (FDOA) techniques are primarily used for estimating the location of a moving object. Although these techniques provide excellent estimation performance for the stationary or low speed moving object, they may have significant location estimation errors for the high-speed object moving. In order to improve this problem, this paper introduces the combined localization method based on TDOA and FDOA for estimating the location of high-speed moving objects efficiently.

关键词： Location awareness Time-frequency analysis Estimation error Time difference of arrival Computer simulation Frequency estimation

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MAMMOS: MApping Multiple human MOtion with Scene understanding and natural interactions

MAMMOS: MApping Multiple human MOtion with Scene understandi...

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International Conference on Computer Vision Workshops (ICCV Workshops)

作者： Donggeun Lim Cheongi Jeong Young Min Kim Dept. of Electrical and Computer Engineering Seoul National University Samsung Research Interdisciplinary Program in Artificial Intelligence and INMC Seoul National University

We present MAMMOS, an automated framework that generates the motions of multiple humans that naturally interact with each other in a given 3D scene. Many practical VR scenarios require creating dynamic human characters in harmony with the surrounding environment and other people. However, it is hard for an artist to manually generate multiple character motions tailored to the given 3D scene structure, or gather sufficient data to train an automated system that jointly considers the entangled requirements. MAMMOS is a hierarchical framework that successfully handles spatio-temporal constraints and generates high-quality motions. Given a simple tuple of action labels of the desired motion sequence, MAMMOS first places anchors in time and location for characters that avoid collisions yet enable necessary interactions. Then we generate the timelines of individual collision-free paths within the scene and connect them to perform diverse and natural motions. To the best of our knowledge, we are the first to generate long-horizon motion sequences of multiple humans with realistic interactions such that we can automatically populate the 3D scenes.

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Dental Imaging Using a Higher Signal-to-Noise Ratio Optical Coherence Tomography System

Dental Imaging Using a Higher Signal-to-Noise Ratio Optical ...

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Electrical, Computer and Energy Technologies (ICECET), International Conference on

作者： Akeem. A. Azeez Biniyam Mezgebo Rodrigo França Sherif S. Sherif Biomedical Engineering Graduate Program University of Manitoba Winnipeg Canada Dept. of Electrical andComputer Engineering University of Manitoba Winnipeg Canada Dept. of Restorative Dentistry University of Manitoba Winnipeg Canada Dept. of Electrical and Computer Engineering University of Manitoba Winnipeg Canada

This paper presents an optical coherence tomography (OCT) system in conjunction with a novel image reconstruction technique employed for in vitro imaging of human teeth. The primary goal is to enhance the signal-to-noise ratio (SNR) in the obtained images. The study entails a comparative analysis between the conventional Fast Fourier Transform (FFT) OCT image reconstruction method and a newly introduced scaled nonuniform discrete Fourier transform (NDFT) approach. The findings reveal that the NDFT method consistently delivers superior results in terms of peak signal-to-noise ratio (PSNR) and overall image quality, even when dealing with redundant and nonuniform frequency domain samples. In light of these results, this paper concludes that integrating NDFT into OCT procedures has the potential to significantly enhance the quality of image reconstructions, thereby fostering its broader application in the field of dental imaging.

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Accelerating Real-Time Imaging for Radiotherapy: Leveraging Multi-GPU Training with PyTorch 22

Accelerating Real-Time Imaging for Radiotherapy: Leveraging ...

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22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023

作者： Obe, Ruth Kaufmann, Brandt Baird, Kaelen Kadel, Sam Soltani, Yasmin Cham, Mostafa Gobbert, Matthias K. Barajas, Carlos A. Jiang, Zhuoran Sharma, Vijay R. Ren, Lei Peterson, Stephen W. Poif, Jerimy C. U. of Houston-Clear Lake Dept. of Computer Science and of Software Engineering United States U. of San Francisco Dept. of Mathematics and Statistics United States Skidmore College Dept. of Computer Science and of Mathematics United States Mount Holyoke College Dept. of Computer Science and of Psychology United States U. of Houston Dept. of Biomedical Engineering United States U. of Maryland Dept. of Information Systems Baltimore County United States U. of Maryland Dept. of Mathematics and Statistics Baltimore County United States Medical Physics Graduate Program Duke University United States U. of Maryland School of Medicine Dept. of Radiation Oncology United States U. of Maryland School of Medicine Department of Radiation Oncology United States U. of Cape Town Dept. of Physics South Africa H3D Inc. United States

ISBN: (纸本)9798350345346

Proton beam therapy is an advanced form of cancer radiotherapy that uses high-energy proton beams to deliver precise and targeted radiation to tumors. This helps to mit-igate unnecessary radiation exposure in healthy tissues. Real-time imaging of prompt gamma rays with Compton cameras has been suggested to improve therapy efficacy. However, the camera's non-zero time resolution leads to incorrect interaction classifications and noisy images that are insufficient for accurately assessing proton delivery in patients. To address the challenges posed by the Compton camera's image quality, machine learning techniques are employed to classify and refine the generated data. These machine-learning techniques include recurrent and feedforward neural networks. A PyTorch model was designed to improve the data captured by the Compton camera. This decision was driven by PyTorch's flexibility, powerful capabilities in handling sequential data, and enhanced G PU usage. This accelerates the model's computations on large-scale radiotherapy data. Through hyperparameter tuning, the validation accuracy of our PyTorch model has been improved from an initial 7 % to over 60 %. Moreover, the PyTorch Distributed Data Parallelism strategy was used to train the RNN models on multiple G PU s, which significantly reduced the training time with a minor impact on model accuracy. © 2023 IEEE.

关键词： Clas-sification Compton camera Distributed Data Parallelism Proton beam therapy PyTorch Recurrent neural network

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Classification of Human Tactile Perception Using Vibration Data

Classification of Human Tactile Perception Using Vibration D...

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International Conference on Cyberworlds

作者： Hyung-Tak Lee Keungyonh Bak Sungwoo Chun Han-Jeong Hwang Dept. Electronics and Information Engineering Korea University Sejong Republic of Korea Interdisciplinary Graduate Program for Artificial Intelligence Smart Convergence Technology Korea University Sejong Republic of Korea

ISBN: (数字)9798331527174

ISBN: (纸本)9798331527181

To enhance the sense of reality in the VR environment, it is essential to incorporate not only visual and hearing but also tactile feedback. Previous studies related to tactile sensation have only focused on object classification to identify the object in contact with the tactile sensor. When people encounter a new object, they can use tactile perception information, such as roughness, to understand the object’s attributes and functions. Therefore, for tactile research to be applicable in real-life scenarios, tactile perception classification is essential not only for classifying an object but also for recognizing its attributes and functions. This study aims to investigate the possibility of predicting roughness, one of the important human tactile perceptions, using deep learning models. We created 63 tactile samples, measured the vibration data of each sample using an acceleration sensor, and investigated how humans perceive the roughness of the tactile samples with five different levels. Object classification was performed using five deep learning models, among which the Shallow ConvNet achieved the highest performance, confirming its suitability as the most appropriate model for analyzing vibration data. Using the Shallow ConvNet, we attained a high accuracy of $\mathbf{9 8. 6} \pm \mathbf{5. 6 \%}$ for the identification of five levels of roughness, demonstrating that different levels of tactile perception information can be discriminated with high performance.

关键词： Vibrations Deep learning Solid modeling Analytical models Visualization Tactile sensors Virtual reality Vibration measurement Predictive models Data models

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Spinal Noradrenergic Alpha-2 Receptors Mediate the Antinociceptive Effects of Therapeutic Intraspinal Microstimulation 46

Spinal Noradrenergic Alpha-2 Receptors Mediate the Antinocic...

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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Lucas-Romero, Javier Bandres, Maria F. McPherson, Jacob G. Washington University School of Medicine Program in Physical Therapy St. Louis United States Washington University School of Medicine Program in Physical Therapy Dept. of Biomedical Engineering St. Louis United States Prog. Physical Therapy St. Louis United States Dept. of Anesthesiology St. Louis United States Prog. Neurosciences St. Louis United States Dept. of Biomed. Eng. St. Louis United States Washingon Univ. Pain Ctr St. Louis United States Washington University School of Medicine St. Louis United States

ISBN: (纸本)9798350371499

This study investigates the role of noradrenergic signaling in the antinociceptive effects of intraspinal microstimulation intended to restore movement after spinal cord injury. Two rat cohorts, intact and with chronic spinal cord injury, underwent intraspinal microstimulation (ISMS), and the multiunit neuronal response to peripheral noxious stimulation was examined at the spinal cord level. The observed effects were compared with those after blocking alpha-2 adrenergic receptors using RX821002. SCI heightened the nociceptive response to pinch stimulation, but ISMS exhibited clear antinociceptive effects in both intact and injured groups. These effects were nullified by blocking alpha-2 receptors, highlighting their involvement in the depression of nociceptive processing induced by ISMS. © 2024 IEEE.

关键词： Patient rehabilitation

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Chronic Spinal Cord Injury Increases Spontaneous Intraspinal Neural Transmission and Spike Train Variability 46

Chronic Spinal Cord Injury Increases Spontaneous Intraspinal...

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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Bandres, Maria F. McPherson, Jacob G. Washington University School of Medicine Program in Physical Therapy Dept. of Biomedical Engineering St. Louis United States Prog. Physical Therapy St. Louis United States Dept. of Anesthesiology St. Louis United States Prog. Neurosciences St. Louis United States Dept. of Biomed. Eng. St. Louis United States Washingon Univ. Pain Ctr St. Louis United States Washington University School of Medicine St. Louis United States

ISBN: (纸本)9798350371499

Spinal cord injury (SCI) often leads to increased spontaneous activity (SpAP) and responsiveness to sensory feedback. This hyperexcitable state contributes to the development and maintenance of SCI-related neuropathic pain (SCI-NP). In general, characterizations of SpAP in the context of SCI-NP focus on the dorsal horns and sensory-processing neurons. As a result, changes in SpAP in the integrative intermediate gray and the motor-dominant ventral horn after SCI are considerably less understood. Thus, it is unclear whether altered firing dynamics in SpAP throughout the dorso-ventral extent of the spinal gray matter contribute to SCI-NP. Here we characterize the firing dynamics of dorsal and ventral interneurons in vivo in neurologically intact rats and rats with chronic SCI with and without SCI-NP. We find (1) robust SpAP throughout the dorsoventral extent of the spinal gray matter after chronic SCI and chronic SCI-NP;(2) that animals with SCI-NP showed higher spontaneous discharge rates compared to the other animal groups, particularly in the ventral horns;and (3) that animals with chronic SCI exhibited higher neuronal variability compared to neurologically intact animals. Together, the results suggest that increased SpAP in dorsal and ventral neurons may further exacerbate pathological sensory transmission after SCI and be a component of SCI-NP. © 2024 IEEE.

关键词： Rats

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Wireless Galvanic Impulse Communication for High-Throughput, Low-Power, Miniaturized Neuromodulation Implants

Wireless Galvanic Impulse Communication for High-Throughput,...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Morgan Riley FNU Tala Mehdi Bandali Benjamin C. Johnson Biomedical Engineering Doctoral Program Boise State University Boise ID USA Dept. of Electrical and Computer Engineering Boise State University Boise ID USA

Deeply implanted bioelectronic devices that selectively record and stimulate peripheral nerves have the potential to revolutionize healthcare by delivering on-demand, personalized therapy. A key barrier to this goal is the lack of a miniaturized, robust, and energy-efficient wireless link capable of transmitting data from multiple sensing channels. To address this issue, we present a wireless galvanic impulse link that uses two 500μm diameter planar electrodes on the outside of a nerve cuff to transmit data to a wearable receiver on the skin’s surface at rates greater than 1Mbps. To achieve an energy-efficient, high data rate link, our protocol encodes information in the timing of narrow biphasic pulses that is reconstructed by the wearable receiver. We use a combination of modeling and in vivo and in vitro experimentation to demonstrate the viability of the link. We demonstrate losses lower than 60dB even with significant, 50mm lateral misalignment, ensuring a sufficient signal-to-noise ratio for robust operation. Using a custom, flexible nerve cuff, we demonstrate data transmission in a 14mm-thick rodent animal model and in a 42mm-thick heterogeneous human tissue phantom.

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