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Epileptic Seizure Prediction Based on EEG by Auto-machine Learning

Epileptic Seizure Prediction Based on EEG by Auto-Machine Le...

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2022 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2022

作者： Chen, Cai Peng, Fulai Sun, Yue Lv, Danyang Zhang, Ningling Wang, Xingwei Wang, Lin Shandong Institute of Advanced Technology Chinese Academy of Sciences Jinan China Shandong JinMm Recycled Water Resources Co. Ltd. China Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen China

ISBN: (数字)9781665469838

ISBN: (纸本)9781665469838

The sudden epileptic seizures may not only cause accidental injuries to the patient, but also lead to psychological trauma. It is crucial to predict the onset of a seizure before it occurs. Although the current researches could achieve relatively high prediction performance, there still remain some challenges in the practical scenes, such as class-imbalance problem between pre-ictal and inter-ictal samples, manual hyperparameter tuning problem, etc. This paper proposes a feature-enhancing strategy combining automatic machine learning method to solve these problems. Firstly, the EEG signals are divided into preictal and interictal stages, and then separated into five sub-bands by the pre-processing stage. Then, the features are extracted from the preprocessed signals, followed by feature smoothing and feature augmentation process, which we employ conditional tabular generative adversarial network (CTGAN) to generate the preictal samples. Finally, the processed features are fed into the automatic machine learning (Auto-ML) for seizure prediction. The CHB-MIT EEG dataset is used in this study to evaluate the performance of our proposed method. The combination CTGAN and K-nearest neighbors (KNN), logistic regression (LR), Naive Bayes (NB) classifier and multilayer perceptron (MLP) achieved an average precision of 0.97, 0.94, 0.87 and 0.95, respectively. Auto-ML combined with CTGAN outperforms traditional machine learning models in seizure prediction, with an average accuracy of 99%. Results show that feature augmentation strategy and automatic machine learning can improve the epileptic seizures prediction performance. © 2022 IEEE.

关键词： Generative adversarial networks

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A Multi-Scale Layer-Channel Attention Network for Image Super-Resolution

A Multi-Scale Layer-Channel Attention Network for Image Supe...

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2021 China Automation Congress, CAC 2021

作者： Wang, Jian Hirota, Kaoru Pan, Bei Dai, Yaping Jia, Zhiyang Jiang, Naifu School of Automation Beijing Institute of Technology Beijing China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China

ISBN: (纸本)9781665426473

Recently, image super-resolution (SR) methods widely employ deep convolutional neural networks (CNNs) to improve the performance of reconstructed images through extracting informative features. However, the most of existing SR networks ignore the interdependencies among layers from different modules, which might hinder the further improvement of representational ability. In this paper, a newly designed multi-scale layer-channel attention network (MSLCAN) is proposed for single image super resolution (SISR) task. Our contributions are twofold. First, the layer-channel attention module (LCAM) can adaptively emphasize the correlations among different channels and interrelationships among multi-scale layers. Second, the multi-scale feature extraction module (MSFEM) effectively extracts adequate image information and enhances network ability of feature representation. In the experiments on five standard benchmarks, we have observed that our MSLCAN outperforms other SISR methods on quantitative and perceptual quality. © 2021 IEEE

关键词： Deep neural networks

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An Attention-Based Hybrid Deep Learning Approach for Patient-Specific, Cross-Patient, and Patient-Independent Seizure Detection 46

An Attention-Based Hybrid Deep Learning Approach for Patient...

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

作者： Ahmad, Ijaz Wang, Xin Li, Lin Liu, Zhenzhen Huang, Jun Aboyeji, Sunday Timothy Li, Guanglin Mukhopadhyay, Subhas Chandra Liu, Zhiyuan Zhao, Guoru Guo, Yi Chen, Shixiong Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Cas Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen China Shenzhen Children's Hospital Surgery Division Epilepsy Center Shenzhen China Shenzhen Fengsheng Biotechnology Co. Ltd Shenzhen China Macquarie University Department of Engineering SydneyNSW Australia Shenzhen People's Hospital Department of Neurology Shenzhen China The Chinese University of Hong Kong Shenzhen School of Medicine Shenzhen China

ISBN: (纸本)9798350371499

Automatic detection of epilepsy plays a crucial role in diagnosing and treatment of patients, while most current methods rely on patient-specific models and have shown promising results, which is not suitable for clinical application, especially when new patient data are used for diagnosis in EEG epileptic seizure detection (ESD). Therefore, the proposed study introduces a novel hybrid deep learning approach consisting of a one-dimensional convolutional neural network (1D CNN), a Multi-Long Short-Term Memory Network (MLSTM) with a multi-attention layer (MAT) for patient-specific, cross-patient, and patient-independent seizure detection. The 1D CNN model extracts spatial features, while the MLSTM extracts temporal features from segmented EEG data. Moreover, the MAT layer conducts feature fusion and identifies relevant patterns. Experiments conducted using the CHB-MIT EEG dataset confirm our method's superiority over other sibling and state-of-the-art methods by an average of 2% in classification accuracy, recall, specificity, and *** of using patient-specific, cross-patient, and patient-independent seizure detection, demonstrating a robust and effective framework in EEG ESD. © 2024 IEEE.

关键词： Convolutional neural networks

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Evoking and Evaluation of Non-invasive Sensory Feedback for Applications in Neural Rehabilitation: A Pilot Study

Evoking and Evaluation of Non-invasive Sensory Feedback for ...

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2023 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2023

作者： Dong, Yuanzhe Tang, Xi Jiang, Naifu Xie, Jun Liang, Wenyuan Shang, Peng Li, Guanglin Fang, Peng Chinese Academy of Sciences Cas Key Laboratory of Human-Machine Intelligence-Synergy Systems The Shenzhen Engineering Laboratory of Neural Rehabilitation Technology Shenzhen Institute of Advanced Technology Shenzhen518055 China University of Chinese Academy of Sciences Shenzhen College of Advanced Technology Shenzhen518055 China Xi'an Jiaotong University School of Mechanical Engineering Xi'an710049 China National Research Center for Rehabilitation Technical Aids Beijing100176 China

ISBN: (纸本)9798350327182

Protheses are very useful for limb amputees to restore their lost motor functions. However, most commercially available prostheses lack an intuitive and natural sensory feedback function, which may strongly limit the performance of prosthetic control and lowers the acceptance of amputees. As a non-invasive stimulation method, Transcutaneous Electrical Nerve Stimulation (TENS) has been proven to effectively evoke intuitive and natural sensations, which is practical for upper-limb amputees in clinical applications. In this paper, we explored the relationship between TENS modalities and induced sensory information based on able-bodied subjects by using electroencephalogram (EEG). We computed the Event-related Potential (ERP) and Brain Electrical Activity Mapping (BEAM) to evaluate the performance of sensation evoking. The results demonstrated that the TENS applied on wrist areas with proper parameters could stably induce multiple types of intuitive sensations of different fingers, including touching, vibrating, and flapping, and the mapping relationship between stimulation modality and sensory feedback were objectively verified and evaluated with EEG. This pilot study might provide a support for future researches on restoration of sensory paths and feedback for upper-limb amputees. © 2023 IEEE.

关键词： Electroencephalography

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Characterizing Multiple Patterns of Motor Intent Using Spatial-Temporal Information for Intuitively Active Motor Training in Stroke Survivors 42

Characterizing Multiple Patterns of Motor Intent Using Spati...

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42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society, EMBC 2020

作者： Samuel, Oluwarotimi Williams Grace Asogbon, Mojisola Geng, Yanjuan Rusydi, Muhammad I. Mzurikwao, Zacharia B. Chen, Shixiong Fang, Peng Li, Guanglin Cas Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Guangdong518055 China

ISBN: (纸本)9781728119908

Upper extremity motor function loss severely affects stroke survivors during daily life activities. Different rehabilitation robotic systems have been developed to allow stroke survivors regain their motor function. Meanwhile, most of the robots only operate in a passive mode and restrict the users to navigate predefined trajectories which may not align with their motion intent, thus limiting motor recovery. One way to resolve this issue would be to utilize a decoded movement intent to trigger intuitively active motor training for patients. In this direction, this study proposed and investigated the use of spatial-temporal neuromuscular descriptor (STD) for optimal decoding of multiple patterns of movement intents in patient to provide inputs for active motor training in the rehabilitation robotic systems. The STD performance was validated using High-Density surface electromyogram recordings from five stroke survivors who performed 21 limb movements. Experimental results show that the STD achieved a significant reduction in limb movement classification error (13.36%) even in the presence of the inevitable White Gaussian Noise compared to other methods (p © 2020 IEEE.

关键词： Decoding

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Toward Efficient Automated Feature Engineering 39

Toward Efficient Automated Feature Engineering

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39th IEEE International Conference on Data Engineering, ICDE 2023

作者： Wang, Kafeng Wang, Pengyang Xu, Chengzhong Shenzhen Institute of Advanced Technology Chinese Academy of Sciences China University of Chinese Academy of Sciences University of Macau China Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen China State Key Laboratory of Internet of Things for Smart City Department of Computer and Information Science University of Macau China

ISBN: (纸本)9798350322279

Automated Feature Engineering (AFE) refers to automatically generate and select optimal feature sets for downstream tasks, which has achieved great success in real-world applications. Current AFE methods mainly focus on improving the effectiveness of the produced features, but ignoring the low-efficiency issue for large-scale deployment. Therefore, in this work, we propose a generic framework to improve the efficiency of AFE. Specifically, we construct the AFE pipeline based on reinforcement learning setting, where each feature is assigned an agent to perform feature transformation and selection, and the evaluation score of the produced features in downstream tasks serve as the reward to update the policy. We improve the efficiency of AFE in two perspectives. On the one hand, we develop a Feature Pre-Evaluation (FPE) Model to reduce the sample size and feature size that are two main factors on undermining the efficiency of feature evaluation. On the other hand, we devise a two-stage policy training strategy by running FPE on the pre-evaluation task as the initialization of the policy to avoid training policy from scratch. We conduct comprehensive experiments on 36 datasets in terms of both classification and regression tasks. The results show 2.9% higher performance in average and 2x higher computational efficiency comparing to state-of-the-art AFE methods. © 2023 IEEE.

关键词： Reinforcement learning

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Design of magnetorheological brake with adjustable interdigitated comb for lower limb prostheses and exoskeletons

Design of magnetorheological brake with adjustable interdigi...

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2023 IEEE International Conference on Robotics and Biomimetics, ROBIO 2023

作者： Shi, Qiuyu Zhao, Xuan Liao, Haisu Lin, Yongji Huang, Ruisen Wu, Xinyu Gao, Fei Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Provincial Key Laboratory of Robotics and Intelligent System Shenzhen518055 China Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen518055 China The Chinese University of Hong Kong Department of Mechanical and Automation Engineering Shatin 999077 Hong Kong

ISBN: (纸本)9798350325706

Magnetorheological (MR) fluids-based devices such as MR dampers and brakes can output a continuously controllable damping force via modulating the magnitude of the applied magnetic field. Owing to low energy consumption and fast response, recently vast MR dampers and brakes are widely studied and explored for lower limb prostheses and exoskeletons. This paper proposed a novel MR brake with adjustable interdigitated comb for lower limb prostheses. Through actively adjusting the area the of the comb engagement, the maximum viscous and field-induced damping torque can be increased or decreased. Because of that, the dynamic adjustment interval of the output damping torque can be increased to meet the function requirements of the prosthetic knee in different walking modes. Magnetic analysis via ANSYS is performed to check the magnetic density of the MR fluid under different excitations. Mathematical models of the damping torque are built. Finally, a prototype of the novel MR brake is fabricated. © 2023 IEEE.

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Development and evaluation of a hand exoskeleton for finger rehabilitation

Development and evaluation of a hand exoskeleton for finger ...

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2022 IEEE International Conference on Robotics and Biomimetics, ROBIO 2022

作者： Luo, Haiyi Sun, Zhenyu Jing, Xiaobei Shu, Bairui Chen, Shixiong Yong, Xu Yokoi, Hiroshi Department of Mechanical Engineering and Intelligent Systems The University of Electro-Communications 1-5-1 Chofugaoka Tokyo Chofu182-8585 Japan CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Guangdong Shenzhen518055 China Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Shenzhen518055 China Center for Neuroscience and Biomedical Engineering The University of Electro-communications 1-5-1 Chofugaoka Tokyo Chofu182-8585 Japan

ISBN: (纸本)9781665481090

Exoskeleton robots are now prevalent in hand rehabilitation medical training, and they can effectively drive a variety of rehabilitative movements in a hand that has lost its motor ability. To adapt to the hand's physiological structure and motion characteristics, a hybrid-driven exoskeleton hand based on tendon rope and linkage and its validation experiments are proposed in this paper. The exoskeleton hand can assist one to five fingers independently or even assist a joint alone. Wearing the robot retains the physiological touch of the hand to the maximum extent, which is beneficial to rehabilitation. In addition, patients can also carry out rehabilitation training independently, and the control mode is simple and practical. To verify whether the exoskeleton can reach the grip standard of healthy hands, the Leap Motion Controller is also used to conduct experimental verification of finger movement wearing the exoskeleton. The results show that the maximum average differences between the angles of the finger flexion motion joints (MCP and PIP) with and without the exoskeleton are 10.33 degrees and 11.06 degrees. It was verified that the exoskeleton could meet the requirements of finger flexion and extension for assisted motion within a specific error range. © 2022 IEEE.

关键词： Physiology

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Enhancement of upper limb movement classification based on wiener filtering technique 22

Enhancement of upper limb movement classification based on w...

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22nd IEEE International Conference on E-Health Networking, Application and Services, HEALTHCOM 2020

作者： Jarrah, Yazan Ali Grace, Asogbon Mojisola Samuel, Oluwarotimi Williams Zhu, Mingxing Wang, Xin Delis, Alberto Lopez Huang, Weimin Chen, Shixiong Li, Guanglin CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen College of Advanced Technology UCAS Shenzhen China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China Medical Biophysics Center Oriente University Cuba Shenzhen Children's Hospital Department of Neonatology Shenzhen Guangdong518038 China

ISBN: (纸本)9781728162676

Electromyogram pattern recognition (EMG-PR) is considered a potential method for upper limb prosthesis control. In principle, the feature extraction technique has been ranked the most influential factor that affect the EMG-PR method's performance. Despite the progress made thus far, there are inevitable interferences that could not be handled by the usual signal filtering approaches that are applied to enhance the extracted features. To address this issue, this study proposed a technique based on Wiener filtering for the preprocessing of EMG signals towards increasing the classification performance of EMG-PR systems. The performance of the proposed approach was investigated with recordings of high-density surface EMG which was obtained from four transhumeral amputees who performed five classes of limb movements. Then, features of five time-domain were analyzed in terms of their decoding accuracy, sensitive, and F1-score, with and without the application of the proposed for linear discriminant analysis and support vector machine classifiers techniques. Experimental results showed that by applying the proposed technique to the different feature sets, significant improvements in classification accuracy, sensitivity, and F1-score were observed across all subjects and classifiers. The proposed method improved the average classification accuracy by an increase of approximately 6.24% compared with the conventional method, while an increment as high as 16.77% was recorded for individual classes of movements. The outcomes of this study indicate that Wiener filtering may potentially boost the performance of EMG-PR systems in practical applications. © 2021 IEEE.

关键词： Pattern recognition

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A Novel Hybrid Brain-Computer Interface Based on Integration of Steady-State Visual Stimulation and Rapid Serial Visual Presentation Paradigm

A Novel Hybrid Brain-Computer Interface Based on Integration...

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IEEE International Conference on Robotics and Biomimetics

作者： Jun Xie Huanqing Zhang Qing Tao Peng Fang Junjie Liu Zengle Ge Yixuan Shao Yuhang Ding School of Mechanical Engineering Xinjiang University Urumqi China School of Mechanical Engineering Xi'an Jiaotong University Xi'an China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems and the Shenzhen Engineering Laboratory of Neural Rehabilitation Technology Shenzhen Institute of Advanced Technology Shenzhen China

ISBN: (数字)9798331509644

ISBN: (纸本)9798331509651

Brain-computer interface (BCI) is a kind of human-computer interaction which can realize the communication and control between human brain and the external environment. The single-modality BCI has the problems of small command set and the difficulty to realize multi-dimensional control. To address these issues, a hybrid paradigm combining steady-state visual evoked potential (SSVEP) and rapid serial visual presentation (RSVP) was proposed and designed in this study. A 16-target SSVEP-RSVP hybrid BCI (hBCI) was constructed. This novel paradigm aims to achieve fast information transmission, guarantee system performance through efficient visual stimulation, and effectively overcome the crowding effect problem in the traditional BCIs. Through offline and online experiments, the hBCI system based on the SSVEP-RSVP integration paradigm shows high performance, which provides new insights and technical and theoretical supports for the applicable hBCIs.

关键词： human computer interaction Visualization Accuracy System performance Biomimetics Information processing Brain-computer interfaces User experience Steady-state Robots

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