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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Tao Sun Fuze Tian Hua Jiang Qinglin Zhao Bin Hu Gansu Provincial Key Laboratory of Wearable Computing School of Information Science and Engineering Lanzhou University Lanzhou China School of Medical Technology Beijing Institute of Technology Beijing China CAS Center for Excellence in Brain Science and Institutes for Biological Sciences Shanghai Institutes for Biological Sciences Chines Academy of Sciences Shanghai China Joint Research Center for Cognitive Neurosensor Technology of Lanzhou University & Institute of Semiconductors Chinese Academy of Sciences Lanzhou China

Mental fatigue is a prevalent issue in contemporary society and can negatively affect physical performance and concentration, increasing the likelihood of adverse consequences due to inattention during productive activities. Therefore, it becomes increasingly important to address and eliminate fatigue within a specific period of time. Aromatherapy, as a form of Complementary Alternative Medicine (CAM), is a non-invasive, cost-effective, and efficient method to combat fatigue. Previous studies have assessed the effects of specific aromatherapy oils using scales, but there is a lack of objective and reliable physiological indicators to prove the effectiveness of aromatherapy. Hence, this paper seeks to establish a model illustrating the effects of aromatic essential oil gases on the human body. A multimodal physiological fatigue signal acquisition system that integrates aromatherapy feedback was designed. In addition, an experimental paradigm was developed to explore the potential of aromatherapy in mitigating mental fatigue. Electroencephalogram (EEG) and Electrocardiogram (ECG) signals were collected, allowing for the analysis of time-frequency domain features in EEG and ECG signals, as well as Heart Rate Variability (HRV) features in ECG signals. Our findings indicate that specific aromatic gases demonstrate effectiveness in reducing mental fatigue. Furthermore, we employed the Support Vector Machine (SVM) algorithm to classify the state of human mental fatigue. Based on the classification results, the release of aromatic gas was controlled to provide targeted aromatic feedback. This innovative approach offers a promising avenue for objectively assessing and addressing mental fatigue through aromatherapy interventions.

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Weight-based Channel-model Matrix Framework provides a reasonable solution for EEG-based cross-dataset emotion recognition

arXiv

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arXiv 2022年

作者： Chen, Huayu He, Huanhuan Zhu, Jing Sun, Shuting Li, Jianxiu Shao, Xuexiao Li, Junxiang Li, Xiaowei Hu, Bin Gansu Provincial Key Laboratory of Wearable Computing School of Information Science and Engineering Lanzhou University Lanzhou China Brain Health Engineering Laboratory School of Medical Technology Beijing Institute of Technology Beijing China School of Information Science and Engineering Inner Mongolia University Inner Mongolia China Shandong Academy of Intelligent Computing Technology Jinan China Chinese Academy of Sciences Center for Excellence in Brain Science and Intelligence Technology Shanghai Institutes for Biological Sciences Chinese Academy of Sciences Shanghai China Joint Research Center for Cognitive Neurosensor Technology of Lanzhou University Institute of Semiconductors Chinese Academy of Sciences Lanzhou China Open Source Software and Real-Time System Lanzhou University Ministry of Education Lanzhou China

Cross-dataset emotion recognition as an extremely challenging task in the field of EEG-based affective computing is influenced by many factors, which makes the universal models yield unsatisfactory results. Facing the situation that lacks EEG information decoding research, we first analyzed the impact of different EEG information(individual, session, emotion and trial) for emotion recognition by sample space visualization, sample aggregation phenomena quantification, and energy pattern analysis on five public datasets. Based on these phenomena and patterns, we provided the processing methods and interpretable work of various EEG differences. Through the analysis of emotional feature distribution patterns, the Individual Emotional Feature Distribution Difference(IEFDD) was found, which was also considered as the main factor of the stability for emotion recognition. After analyzing the limitations of traditional modeling approach suffering from IEFDD, the Weight-based Channel-model Matrix Framework(WCMF) was proposed. To reasonably characterize emotional feature distribution patterns, four weight extraction methods were designed, and the optimal was the correction T-test(CT) weight extraction method. Finally, the performance of WCMF was validated on cross-dataset tasks in two kinds of experiments that simulated different practical scenarios, and the results showed that WCMF had more stable and better emotion recognition ability. Copyright © 2022, The Authors. All rights reserved.

关键词： Emotion Recognition

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EEG Based Depression Recognition by Combining Functional Brain Network and Traditional Biomarkers

EEG Based Depression Recognition by Combining Functional Bra...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Shuting Sun Huayu Chen Xuexiao Shao Liangliang Liu Xiaowei Li Bin Hu Gansu Provincial Key Laboratory of Wearable Computing School of Information Science and Engineering Lanzhou University Lanzhou China CAS Center for Excellence in BrainScience and Intelligence Technology Shanghai Institutes for Biological Sciences Chinese Academy of Sciences Beijing China Chinese Academy of Sciences Joint Research Center for Cognitive Neurosensor Technology of Lanzhou University & Institute of Semiconductors Beijing China Ministry of Education Engineering Research Center of Open Source Software and Real-Time System (Lanzhou University) Lanzhou China

ISBN: (数字)9781728162157

ISBN: (纸本)9781728162164

This Electroencephalography (EEG)-based research is to explore the effective biomarkers for depression recognition. Resting-state EEG data were collected from 24 major depressive patients (MDD) and 29 normal controls using 128-electrode geodesic sensor net. To better identify depression, we extracted multi-type of EEG features including linear features (L), nonlinear features (NL), functional connectivity features phase lagging index (PLI) and network measures (NM) to comprehensively characterize the EEG signals in patients with MDD. And machine learning algorithms and statistical analysis were used to evaluate the EEG features. Combined multi-types features (All: L+ NL + PLI + NM) outperformed single-type features for classifying depression. Analyzing the optimal features set we found that compared to other type features, PLI occupied the largest proportion of which functional connections in intra-hemisphere were much more than that of in inter-hemisphere. In addition, when using PLI features and All features, high frequency bands (alpha, beta) could achieve obviously higher classification accuracy than low frequency bands (delta, theta). Parietal-occipital lobe in the high frequency bands had great effect in depression identification. In conclusion, combined multi-types EEG features along with a robust classifier can better distinguish depressive patients from normal controls. And intra-hemispheric functional connections might be an effective biomarker to detect depression. Hence, this paper may provide objective and potential electrophysiological characteristics in depression recognition.

关键词： Electroencephalography Depression Feature extraction Electrodes Biomarkers Support vector machines Entropy

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Design and Application of a Portable Sleep Inertia Detection System Based on EEG Signals

Design and Application of a Portable Sleep Inertia Detection...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Yunzhi Cui Fuze Tian Qinglin Zhao Bin Hu Gansu Provincial Key Laboratory of Wearable Computing School of Information Science and Engineering Lanzhou University Lanzhou China CAS Center for Excellence in Brain Science and Intelligence Technology Shanghai Institutes for Biological Sciences Chinese Academy of Sciences Chinese Academy of Sciences Joint Research Center for Cognitive Neurosensor Technology of Lanzhou University & Institute of Semiconductors Ministry of Education Engineering Research Center of Open Source Software and Real-Time System (Lanzhou University) Lanzhou China

ISBN: (纸本)9781665429825

Sleep inertia is a transitional state from sleep to wakefulness, accompanied by groggy feelings and cognitive impairment. Previous research on sleep inertia mainly used expensive and cumbersome equipment, and the analysis of physiological signals relied on computers. This work introduces a sleep inertia detection system that consists of a wearable low-power electroencephalogram (EEG) acquisition module based on STM32WB55 and ADS1299, and a data processing module based on the Xilinx ® Zynq ® -7000 XC7Z020. This work recorded the EEG signals of ten subjects in the alert and sleep inertia states to extract the delta power, alpha power, beta power, EEG vigilance, and sample entropy. A linear support vector machine (SVM) was then used to classify the two states based on all subjects’ EEG signals, with an accuracy of 72.5%, and the average accuracy based on a single participant was 8S.9%. Finally, the feature extraction algorithm and SVM parameters were entered into the Zynq ® system-on-chip (SoC) development board to realize onboard processing of the algorithm. The system is capable of evaluating the severity of human sleep inertia, which has reference significance for the practical application of sleep inertia detection.

关键词： Support vector machines Sleep Conferences Feature extraction Data processing Electroencephalography Physiology

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MODMA dataset: a Multi-modal Open Dataset for Mental-disorder Analysis

arXiv

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arXiv 2020年

作者： Cai, Hanshu Gao, Yiwen Sun, Shuting Li, Na Tian, Fuze Xiao, Han Li, Jianxiu Yang, Zhengwu Li, Xiaowei Zhao, Qinglin Liu, Zhenyu Yao, Zhijun Yang, Minqiang Peng, Hong Zhu, Jing Zhang, Xiaowei Gao, Guoping Zheng, Fang Li, Rui Guo, Zhihua Ma, Rong Yang, Jing Zhang, Lan Hu, Xiping Li, Yumin Hu, Bin Gansu Provincial Key Laboratory of Wearable Computing School of Information Science and Engineering Lanzhou University China CAS Center for Excellence in Brain Science and Intelligence Technology Shanghai Institutes for Biological Sciences Chinese Academy of Sciences China Joint Research Center for Cognitive Neurosensor Technology of Lanzhou University Institute of Semiconductors Chinese Academy of Sciences China Open Source Software and Real-Time System Lanzhou University Ministry of Education China Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China Lanzhou University Second Hospital China

According to the World Health Organization, the number of mental disorder patients, especially depression patients, has grown rapidly and become a leading contributor to the global burden of disease. However, the present common practice of depression diagnosis is based on interviews and clinical scales carried out by doctors, which is not only labor-consuming but also time-consuming. One important reason is due to the lack of physiological indicators for mental disorders. With the rising of tools such as data mining and artificial intelligence, using physiological data to explore new possible physiological indicators of mental disorder and creating new applications for mental disorder diagnosis has become a new research hot topic. However, good quality physiological data for mental disorder patients are hard to acquire. We present a multi-modal open dataset for mental-disorder analysis. The dataset includes EEG and audio data from clinically depressed patients and matching normal controls. All our patients were carefully diagnosed and selected by professional psychiatrists in hospitals. The EEG dataset includes not only data collected using traditional 128-electrodes mounted elastic cap, but also a novel wearable 3-electrode EEG collector for pervasive applications. The 128-electrodes EEG signals of 53 subjects were recorded as both in resting state and under stimulation;the 3-electrode EEG signals of 55 subjects were recorded in resting state;the audio data of 52 subjects were recorded during interviewing, reading, and picture description. We encourage other researchers in the field to use it for testing their methods of mental-disorder analysis. Copyright © 2020, The Authors. All rights reserved.

关键词： Diagnosis

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Designing and Application of Wearable Fatigue Detection System Based on Multimodal Physiological Signals

Designing and Application of Wearable Fatigue Detection Syst...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Xiaoxuan Qiu Fuze Tian Qiuxia Shi Qinglin Zhao Bin Hu Gansu Provincial Key Laboratory of Wearable Computing School of Information Science and Engineering Lanzhou University Lanzhou China CAS Center for Excellence in Brain Science and Intelligence Technology Shanghai Institutes for Biological Sciences Chinese Academy of Sciences Lanzhou China Joint Research Center for Cognitive Neurosensor Technology of Lanzhou University Institute of Semiconductors Chinese Academy of Sciences Lanzhou China Engineering Research Center of Open Source Software and Real-Time System (Lanzhou University) Ministry of Education Lanzhou China

ISBN: (数字)9781728162157

ISBN: (纸本)9781728162164

Judging mental fatigue can be guided by acquiring and analyzing various physiological data. However, the existing equipment focuses on single physiological signals, such as electroencephalogram (EEG) and electrocardiogram (ECG), which ignores the preponderance of multimodal physiological signals in fatigue states. Alternatively, some equipment is difficult to operate and thus unsuitable for portable applications. To this end, this paper details the design of a miniaturized multi-physiological signal acquisition system. The system not only acquires EEG and ECG based on combining wavelet transform with Kalman filter, but also uses precise temperature sensors to synchronously acquire proximal skin temperature signals which are not easily interfered with by environmental noise or other physiological signals of human body. Through a fatigue assessment experiment on ten healthy subjects, it was verified that our equipment reliably detects mental fatigue states by monitoring and analyzing EEG, ECG, and proximal skin temperature data. In actual applications, this system appears to the traits of easy operation and good stability. It provides better hardware support for the pervasive applications and concrete implementation of mental health in multi-scene, and can popularize use.

关键词： Fatigue Electrocardiography Electroencephalography Skin Temperature measurement Temperature distribution Temperature sensors

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A Portable System of Mental Fatigue Detection and Mitigation based on Physiological Signals

A Portable System of Mental Fatigue Detection and Mitigation...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Chao Wu Fuze Tian Qiuxia Shi Qinglin Zhao Bin Hu Gansu Provincial Key Laboratory of Wearable Computing School of Information Science and Engineering Lanzhou University Lanzhou China CAS Center for Excellence in Brain Science and Intelligence Technology Shanghai Institutes for Biological Sciences Chinese Academy of Sciences Lanzhou China Joint Research Center for Cognitive Neurosensor Technology of Lanzhou University & Institute of Semiconductors Chinese Academy of Sciences Lanzhou China Engineering Research Center of Open Source Software and Real-Time System(Lanzhou University) Ministry of Education Lanzhou China

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

Mental fatigue of the brain will cause the weakening of the psychological and physiological functions of the human body, which increases the risk of mistakes and accidents. This paper designs a mental fatigue detection and mitigation system based on human physiological signals and classical music. Through the fatigue assessment and mitigation experiment on 20 healthy subjects, it is verified that the system is able to judge the mental fatigue state of the subjects and play classical music to mitigate fatigue via acquiring and analyzing (alpha+theta)/beta of electroencephalogram (EEG) and heart rate variability (HRV) of ballistocardiogram (BCG). The designed system realizes signal acquisition and fatigue analysis only through using portable device, this makes it have certain application value in office building, hospital, aircraft driving and other scenes.

关键词： Hospitals Buildings Psychology Fatigue Physiology Electroencephalography Multiple signal classification

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Gaussian Decay Function-based Improved Moment Matching for Ocular Artifacts Removal

Gaussian Decay Function-based Improved Moment Matching for O...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Hua Jiang Qiuxia Shi Siying Liu Jiuying Zhang Qinglin Zhao Bin Hu Gansu Provincial Key Laboratory of Wearable Computing School of Information Science and Engineering Key Laboratory of Special Functional Materials and Structural Design Ministry of Education Lanzhou University Lanzhou China CAS Center for Excellence in Brain Science and Intelligence Technology Shanghai Institutes for Biological Sciences Chinese Academy of Sciences Chinese Academy of Sciences Joint Research Center for Cognitive Neurosensor Technology of Lanzhou University & Institute of Semiconductors Engineering Research Center of Open Source Software and Real-Time System (Lanzhou University) Ministry of Education Key Laboratory of Special Functional Materials and Structural Design (Lanzhou University) Ministry of Education Lanzhou China

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

Electroencephalogram (EEG) equipped with high time resolution that distracted by incoherent brain sources which including ocular artifacts (OAs) generating by blinks is of great significance. Therefore, these OAs got corrected before extracting information from EEG is indispensable. Improved Moment Matching (IMM) is a high-speed denoising algorithm suitable for removing OA in multi-channel EEG, which is an improvement of the moment matching method used to remove stripe noise in hyperspectral images. On foundation of this, this paper proposes an optimization algorithm for IMM based on Gaussian decay function (IMM_G). In the first place, the construction of the reference signal is optimized via utilizing a Gaussian decay function, thereby preventing the signal distortion caused by the filter. And then, a method for judging the to-be-processed interval is proposed to realizes the individualized processing of different channels. As a result, through quantitative comparison experiments with simulated data and real data from the UAIS laboratory, it was identified that IMM_G showed less time complexity, significantly enhanced arithmetic speed and denoising consequence while the detail retention ability of the original method for the non-blinking region of multi-channel EEG data is maintained. Hence, this method could be extensively used in High-density EEG (hdEEG) OAs removal scene.

关键词： Electrooculography Noise reduction Filtering algorithms Distortion Electroencephalography Recording Time-domain analysis

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Noncontact Doppler Radar-based Heart Rate Detection on the SVD and ANC

Noncontact Doppler Radar-based Heart Rate Detection on the S...

引用

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Qiuxia Shi Bin Hu Fuze Tian Qinglin Zhao Gansu Provincial Key Laboratory of Wearable Computing School of Information Science and Engineering Lanzhou University School of Computer and Communication Lanzhou University of Technology Ministry of Education Key Laboratory of Special Functional Materials and Structural Design (Lanzhou University) Lanzhou China CAS Center for Excellence in Brain Science and Intelligence Technology Shanghai Institutes for Biological Sciences Chinese Academy of Sciences Joint Research Center for Cognitive Neurosensor Technology of Lanzhou University & Institute of Semiconductors Chinese Academy of Sciences Engineering Research Center of Open Source Software and Real-Time System(Lanzhou University) Ministry of Education Key Laboratory of Special Functional Materials and Structural Design Ministry of Education Lanzhou University Lanzhou China

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

In the Doppler biological radar-based applications of noncontact measurement of vital signs, effectively extracting heartbeat information from weak thoracic mechanical motion is an important problem to be solved. This study is aimed at extracting heartbeat signal via the technology combined with Short Time Fourier Transform (STFT), Singular Value Decomposition (SVD) and Adaptive Noise Canceller (ANC) from radar recording. The simulated data and the data collected by Doppler radar biosensor realized in laboratory are employed to validate the proposed method. The results show that the proposed method has the ability of detection for the heart rate and heart rate variability indexes in rest state, it has certain advantages in time-consuming and detection accuracy. Therefore, the current method provides another way to process vital sign signals recorded by Doppler radar.

关键词： Biomedical measurement Noise cancellation Time measurement Doppler radar Recording Doppler effect Biosensors

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Weak Reverse attention with Context Aware for Person Re-identification

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Journal of Physics: Conference Series 2020年第1期1487卷

作者： Ke Gong Xin Ning Hanchao Yu Liping Zhang Linjun Sun Cognitive Computing Technology Joint Laboratory Wave Group Beijing 100083 Beijing Wave Security Technology company lmited Beijing 102208 Laboratory of Artificial Neural Networks and High-speed Circuits Institute of Semiconductors Chinese Academy of Sciences Beijing 100083 China Bureau of Frontier Sciences and Education Chinese Academy of Sciences Beijing 100864

Person re-identification is a difficult topic in computer vision. Some study think that current deep learning methods is biased to capture the most discriminative features and ignore low-level details, more serious is it pay too much attention on relevance between background appearances of person images. It might limit their accuracy or makes them needlessly expensive for a not best performance. In this paper, we carefully design the Weak Reverse attention with Context Aware Network (WRCANet). Specifically, by merging weak reverse attention network and content aware module, the model can not only remove the background noise to extract the main information of persons, but also suppress the loss of local detailed information as the network deepens. We experiment on the Market-1501, DukeMTMC-reID and CUHK03, and the results show that our method achieves the state-of-the-art performance.

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