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Positive-Unlabeled Learning Method for Positive Emotion Recognition Using EEG technology 利用脑电图技术识别积极情绪的积极无标记学习法
Pub Date : 2024-08-09 DOI: 10.24297/ijct.v24i.9650
Zizhu Li, Chengyuan Shen, Jianting Cao
Emotion is a reaction of the human brain to external events, and the study of emotion recognition has substantial practical applications. Therefore, accurately recognizing and understanding positive emotions across different populations is crucial. Traditional image recognition technology cannot effectively identify emotions in individuals with impaired facial muscle control, such as elderly people in nursing homes with Alzheimer’s disease and patients with facial nerve paralysis (Bell’s palsy). Consequently, many machine learning methods have been widely applied to emotion recognition based on electroencephalogram (EEG) signals in recent years. In cases where the number of samples is sufficient, powerful deep learning methods can achieve high performance in emotion recognition. However, obtaining a large amount of reliably labeled emotional EEG data is arduous. We introduce a Positive-Unlabeled (PU) learning method for classifying EEG signals into Positive and Non-Positive emotions using a binary classifier developed with minimal labeled data. This approach utilizes a small volume of labeled data containing only positive emotion signals, combined with unlabeled data that includes both classes, effectively reducing the dependency on extensive, reliably labeled EEG data. The best accuracy achieved by this method is 93.95%. Experimental results on the dataset demonstrate theeffectiveness of our approach.
情绪是人脑对外部事件的一种反应,而情绪识别研究具有重要的实际应用价值。因此,准确识别和理解不同人群的积极情绪至关重要。传统的图像识别技术无法有效识别面部肌肉控制能力受损的人的情绪,如养老院中患有阿尔茨海默病的老人和面部神经麻痹(贝尔氏麻痹)患者。因此,近年来许多机器学习方法被广泛应用于基于脑电图(EEG)信号的情绪识别。在样本数量充足的情况下,功能强大的深度学习方法可以在情感识别中取得很高的性能。然而,要获得大量可靠标记的情感脑电图数据却非常困难。我们引入了一种正向无标记(PU)学习方法,利用使用最少标记数据开发的二元分类器将脑电信号分为正向情绪和非正向情绪。这种方法利用了少量仅包含积极情绪信号的标注数据,并结合了包含两个类别的非标注数据,有效降低了对大量可靠标注脑电图数据的依赖。该方法达到的最佳准确率为 93.95%。数据集上的实验结果证明了我们方法的有效性。
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引用次数: 0
Unveiling Neurophysiological Markers of Consciousness Levels through EEG Exploration 通过脑电图探索揭示意识水平的神经生理学标记
Pub Date : 2024-06-05 DOI: 10.24297/ijct.v24i.9627
Jingming Gong, Linfeng Sui, Ran Zhang, Boning Li, Chengyuan Shen, ,Taiyo Maeda, Jianting Cao
The concept of consciousness levels typically refers to various aspects and tiers related to an individual’s cognition, perception, thinking, and awareness. Although neurophysiological markers have not yet been definitively identified to distinguish between these nuanced levels, this paper introduces a robust marker, the Approximate Entropy (ApEn), which quantifies the complexity of EEG signals to differentiate states of altered consciousness. Utilizing ApEn, we analyze EEG data from the frontal lobe—a region closely associated with consciousness—in states indicative of severely altered conditions, specifically anesthesia, coma, and brain death. To enhance the precision of consciousness levelassessment, we employ a Support Vector Machine (SVM) model, which classifies the states based on EEG complexity measures. This approach not only provides valuable insights into the neural correlations associated with changes in these critical states but also underscores the potential of combining quantitative EEG analysis with machine learning techniques to advance our understanding of consciousness. The findings demonstrate that EEG complexity, when analyzed using ApEn coupled with SVM classification, offers a novel and effective method for assessing and distinguishing between degrees of consciousness. This approach promises significant implications for clinical diagnostics and patient monitoring.
意识水平的概念通常是指与个体的认知、感知、思考和意识相关的各个方面和层次。虽然目前还没有明确的神经生理学标记来区分这些细微的层次,但本文介绍了一种稳健的标记--近似熵(ApEn),它可以量化脑电信号的复杂性,从而区分意识改变的状态。利用 ApEn,我们分析了额叶--与意识密切相关的区域--的脑电图数据,这些数据显示了严重的意识改变状态,特别是麻醉、昏迷和脑死亡。为了提高意识水平评估的精确度,我们采用了支持向量机(SVM)模型,该模型根据脑电图的复杂度对状态进行分类。这种方法不仅为了解与这些临界状态变化相关的神经相关性提供了宝贵的见解,还凸显了将脑电图定量分析与机器学习技术相结合以促进我们对意识的理解的潜力。研究结果表明,使用 ApEn 分析脑电图的复杂性并结合 SVM 分类,为评估和区分意识程度提供了一种新颖而有效的方法。这种方法有望对临床诊断和病人监护产生重大影响。
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引用次数: 0
A NEW ROBUST HOMOMORPHIC ENCRYPTION SCHEME BASED ON PAILLIER, RESIDUE NUMBER SYSTEM AND EL-GAMAL 基于派利尔、残差数系统和埃尔-伽马尔的新型稳健同态加密方案
Pub Date : 2024-04-17 DOI: 10.24297/ijct.v24i.9606
P. A. Agbedemnab, Abdul Somed Safianu and, Abdul-Mumin Selanwiah Salifu
The new focus of cryptographic research is on encryption schemes that can withstand cyber-attacks, with the arrival of cloud computing. The widely used public key encryption system designed by Taher El Gamal based on the discrete logarithm problem has been used in many sectors such as internet security, E-voting systems, and other applications for a long time. However, considering the potential data security threats in cloud computing, cryptologists are developing new and more robust cryptographic algorithms. To this end, a new robust homomorphic encryption scheme based on Paillier, Residue Number system (RNS), and El Gamal (PRE), is proposed in this paper., which is expected to be highly effective and resistant to cyber-attacks. The proposed scheme is composed a three-layer encryption and a three-layer decryption processes thereby, making it robust. It employs an existing RNS moduli set {2n + 1, 2n, 2n − 1, 2n−1} − 1}, having passed it through the Paillier encryption process for forward conversion and then the El Gamal cryptosystem to encrpyt any data. The decryption process is a reversal of these processes starting from the El Gamal through a reverse conversion with the same moduli set using the Chinese Remainder Theorem (CRT). The simulation results shows that the proposed scheme outperforms similar existing schemes in terms of robustness and therefore, making it more secured which however, trades off with the time of execution in similar comparison.
随着云计算的到来,能够抵御网络攻击的加密方案成为密码学研究的新焦点。塔希尔-埃尔-加马尔(Taher El Gamal)基于离散对数问题设计的公钥加密系统已被广泛应用于互联网安全、电子投票系统等多个领域。然而,考虑到云计算中潜在的数据安全威胁,密码学家们正在开发新的、更稳健的密码算法。为此,本文提出了一种基于 Paillier、残差数系统(RNS)和 El Gamal(PRE)的新型稳健同态加密算法,该算法有望高效抵御网络攻击。所提出的方案由三层加密和三层解密过程组成,因此具有很强的鲁棒性。它采用了现有的 RNS 模量集 {2n + 1, 2n, 2n - 1, 2n-1} 。- 1},通过 Paillier 加密过程进行正向转换,然后通过 El Gamal 密码系统加密任何数据。解密过程是这些过程的逆转,从 El Gamal 开始,利用中文余数定理(CRT)进行相同模集的反向转换。仿真结果表明,所提出的方案在鲁棒性方面优于现有的类似方案,因此更加安全,但在类似的比较中,执行时间有所折损。
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引用次数: 0
On Defining Smart Cities using Transformer Neural Networks 利用变压器神经网络定义智能城市
Pub Date : 2024-01-28 DOI: 10.24297/ijct.v24i.9579
Andrei Khurshudov
Cities worldwide are rapidly adopting “smart” technologies, transforming urban life. Despite this trend, a universally accepted definition of “smart city” remains elusive. Past efforts to define it haven’t yielded a consensus, as evidenced by the numerous definitions in use. In this paper, we endeavored to create a new “compromise” definition that should resonate with most experts previously involved in defining this concept and aimed to validate one of the existing definitions. We reviewed 60 definitions of smart cities from industry, academia, and various relevant organizations, employing transformer architecture-based generative AI and semantic text analysis to reach this compromise. We proposed a semantic similarity measure as an evaluation technique, which could generally be used to compare different smart city definitions, assessing their uniqueness or resemblance. Our methodology employed generative AI to analyze various existing definitions of smart cities, generating a list of potential new composite definitions. Each of these new definitions was then tested against the pre-existing individual definitions we’ve gathered, using cosine similarity as our metric. This process identified smart city definitions with the highest average cosine similarity, semantically positioning them as the closest on average to all the 60 individual definitions selected.
世界各地的城市正在迅速采用 "智能 "技术,改变城市生活。尽管出现了这种趋势,但 "智慧城市 "这一公认的定义仍然难以确定。过去为定义 "智慧城市 "所做的努力并未达成共识,目前使用的众多定义就是明证。在本文中,我们努力创造一个新的 "折中 "定义,该定义应与之前参与定义这一概念的大多数专家产生共鸣,并旨在验证现有的定义之一。我们回顾了来自工业界、学术界和各种相关组织的 60 个智慧城市定义,并采用了基于变换器架构的生成式人工智能和语义文本分析来达成这一折衷方案。我们提出了一种语义相似性度量作为评估技术,一般可用于比较不同的智慧城市定义,评估其独特性或相似性。我们的方法采用生成式人工智能来分析现有的各种智慧城市定义,从而生成一个潜在的新复合定义列表。然后,我们使用余弦相似度作为衡量标准,将每个新定义与我们收集到的已有单个定义进行对比测试。这一过程识别出了平均余弦相似度最高的智慧城市定义,从语义上将其定位为平均最接近所有被选中的 60 个单个定义。
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引用次数: 0
Convolutional Neural Networks for Deep Sleep Detection Based on Data Augmentation 基于数据增强的卷积神经网络用于深度睡眠检测
Pub Date : 2024-01-28 DOI: 10.24297/ijct.v24i.9567
Ruixuan Chen, Linfeng Sui, Mo Xia, Jinsha Liu, Tao Zhang, Jianting Cao
Sleep is a necessary process that individuals undergo daily for physical recovery, and the proportion of deep sleep in the sleep stages is a critical aspect of the recovery process. Convolutional Neural Networks (CNNs) have shown remarkable success in automatically identifying deep sleep stages through the analysis of electroencephalogram (EEG) signals. This article introduces three data augmentation techniques, including time shifting, amplitude scaling and noise addition, to enhance the diversity and features of the data. These techniques aim to enable machine learning models to extract features from various aspects of sleep EEG data, thus improving the model’s accuracy. Three deep learning models are introduced, namely DeepConvNet, ShallowConvNet and EEGNet, for the identification of deep sleep. To evaluate the proposed methods, the Sleep-EDF public dataset was utilized. Experimental results demonstrate that the enhanced dataset formed by applying the three data augmentation techniques achieved higher accuracy in all deep learning models compared to the original dataset. This highlights the feasibility and effectiveness of these methods in deep sleep detection.
睡眠是人每天身体恢复的必要过程,而深度睡眠在睡眠阶段中所占的比例是恢复过程中的一个关键环节。卷积神经网络(CNN)在通过分析脑电图(EEG)信号自动识别深度睡眠阶段方面取得了显著成效。本文介绍了三种数据增强技术,包括时间移动、振幅缩放和噪声添加,以增强数据的多样性和特征。这些技术旨在使机器学习模型能够从睡眠脑电图数据的各个方面提取特征,从而提高模型的准确性。本文引入了三种深度学习模型,即 DeepConvNet、ShallowConvNet 和 EEGNet,用于识别深度睡眠。为了评估所提出的方法,我们使用了 Sleep-EDF 公共数据集。实验结果表明,与原始数据集相比,应用三种数据增强技术形成的增强数据集在所有深度学习模型中都达到了更高的准确度。这凸显了这些方法在深度睡眠检测中的可行性和有效性。
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引用次数: 0
Experimental Implementation approach of Crawling Framework CARE JOBS for Disability Job Search Using Intelligent Agents 利用智能代理进行残疾工作搜索的 CARE JOBS 抓取框架的实验实施方法
Pub Date : 2023-12-21 DOI: 10.24297/ijct.v23i.9559
Diana Avram
The Crawling Framework CAREJOBS (CF) we present here is a suite of tools dedicated to facilitating job search for people with disabilities. In this experimental implementation we propose a crawler with modern architecture based on intelligent agents, a multi-agent system trained on the basis of a rule engine. We also present a case of using CF in a CV creation expert. It can be seen that our approach is perfectible by adding learning to the agent model.
我们在此介绍的 CAREJOBS(CF)爬虫框架是一套专门为残疾人求职提供便利的工具。在本实验实施中,我们提出了一种基于智能代理的现代架构爬虫,这是一种在规则引擎基础上训练出来的多代理系统。我们还介绍了在简历创建专家中使用 CF 的案例。可以看出,通过在代理模型中添加学习功能,我们的方法是可行的。
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引用次数: 0
Rotitome-G: Principles and design concept of experimental compliant continuum robotic microsurgical endoscopic sarcotome for pixel/voxel-level target access neurosurgery Rotitome-G:用于像素/体素级靶点入路神经外科手术的实验性顺应式连续体机器人显微手术内窥镜纤支镜的原理和设计理念
Pub Date : 2023-12-02 DOI: 10.24297/ijct.v23i.9549
H. S. Gandhi
The practice of minimal access surgery is widely accepted, and it has become prevalent with improved endoscope design. The traditional microscope in neurosurgery is gradually being challenged by the neuro-endoscope for its direct co-axial vision and direct illumination of the deep set subcortical pathology. The conceptualized design of Rotitome-G is based on compliant continuum robotic system. The system is a biomimicry of muscular hydrostat anatomy of the elephant trunk, a plant tendril, and many similar structures in the animal world with an infinite degree of freedom. The article describes the functional anatomy of these structures and the extensor expansion of the human finger as applied to the construction and implementation of the Rotitome-G. This flexible microsurgical endoscope integral to its design has unique cutting tool versions and multiple assistive tools passed through single ‘target access’ burr hole aperture. It is navigable within the surgical space co-relative to the image space to increase precision and improve the volume of tumour resection. The current study is theoretical and further work is in progress to assess its surgical capabilities to bring it to the clinical arena.
微创手术已被广泛接受,并随着内窥镜设计的改进而变得普遍。传统的神经外科显微镜正逐渐被神经内窥镜以其直接的同轴视觉和对深部皮层下病理的直接显示所挑战。Rotitome-G的概念化设计是基于柔性连续体机器人系统。该系统仿造了象鼻、植物卷须的肌肉静水解剖学,以及动物世界中许多类似的结构,具有无限自由度。本文描述了这些结构的功能解剖和人类手指的伸肌扩张,应用于Rotitome-G的构建和实施。这种灵活的显微外科内窥镜集成到其设计中,具有独特的切割工具版本和多个辅助工具通过单个“目标访问”毛刺孔孔径。它可以在相对于图像空间的手术空间内导航,以提高肿瘤切除的精度和体积。目前的研究是理论性的,进一步的工作正在进行中,以评估其手术能力,将其带入临床领域。
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引用次数: 0
Construct and Evaluate a Phone Dialing System Leveraging SSVEP Brain-Computer Interface 利用 SSVEP 脑机接口构建和评估电话拨号系统
Pub Date : 2023-11-23 DOI: 10.24297/ijct.v23i.9539
Jinsha Liu, Boning Li, Jianting Cao
This study presents a SSVEP based BCI system, designed for dialing a phone number through EEG signals. Our SSVEP system leverages a tablet-based stimulator and OpenBCI Cyton board, employing Canonical Correlation Analysis for EEG signal classification. Tested on 7 participants, the system demonstrated a high accuracy rate of 98.1% in identifying the observed keys. The use of a tablet-based SSVEP stimulator was found to reduce visual fatigue compared to traditional LED stimulators. Despite its initial success, further validation with a larger cohort and in varied real-world conditions is required. This work signifies a promising advancement in utilizing BCIs in practical applications.
本研究介绍了一种基于 SSVEP 的生物识别(BCI)系统,旨在通过脑电信号拨打电话号码。我们的 SSVEP 系统利用基于平板电脑的刺激器和 OpenBCI Cyton 板,并采用 Canonical Correlation Analysis 进行脑电信号分类。该系统对 7 名参与者进行了测试,在识别观察到的按键方面,准确率高达 98.1%。与传统的 LED 刺激器相比,使用基于平板电脑的 SSVEP 刺激器可减轻视觉疲劳。尽管该系统取得了初步成功,但还需要在更大的群体和各种实际条件下进行进一步验证。这项工作标志着在实际应用中使用生物识别技术取得了可喜的进展。
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引用次数: 0
Real-time Interpretation of EEG Signals for Consciousness State Assessment 实时解读脑电信号以评估意识状态
Pub Date : 2023-11-18 DOI: 10.24297/ijct.v23i.9541
Jingming Gong, Linfeng Sui, Ran Zhang, Boning Li, Chengyuan Shen, Jianting Cao
Assessing the level of consciousness is critical in clinical practice, especially for patients with traumatic brain injuriesor those in a coma or vegetative state. Traditional methods like the Glasgow Coma Scale have limitations, such asinter-observer variability and low sensitivity. In recent years, electroencephalography (EEG) has emerged as a promisingapproach for assessing consciousness, offering non-invasive, real-time monitoring of brain activity. In this study, we propose a real-time analysis system for assessing consciousness levels using a portable EEG device. Our system analyzes EEG signals and provides valuable insights into consciousness levels, enabling prompt clinical interventions. The real-time nature of our system allows for continuous monitoring and immediate assessment of consciousness levels. Compared to traditional methods, our system offers advantages in terms of real-time functionality, providing a comprehensive evaluation of consciousness. Through extensive experiments using real patient data, our systemdemonstrates its value as a valuable tool for assessing consciousness levels in clinical practice. It offers healthcare professionals an efficient and reliable method for evaluating consciousness.
评估意识水平在临床实践中至关重要,尤其是对于脑外伤患者、昏迷患者或植物人患者。格拉斯哥昏迷量表(Glasgow Coma Scale)等传统方法有其局限性,如观察者之间的差异性和灵敏度低。近年来,脑电图(EEG)已成为一种很有前途的意识评估方法,它能对大脑活动进行无创、实时的监测。在本研究中,我们提出了一种使用便携式脑电图设备评估意识水平的实时分析系统。我们的系统可分析脑电信号并提供有关意识水平的有价值的见解,从而实现及时的临床干预。我们系统的实时性允许对意识水平进行连续监测和即时评估。与传统方法相比,我们的系统在实时功能方面更具优势,可提供全面的意识评估。通过使用真实病人数据进行大量实验,我们的系统证明了其作为临床实践中评估意识水平的重要工具的价值。它为医护人员提供了一种高效可靠的意识评估方法。
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引用次数: 0
Design and Implementation of P300 Brain-Controlled Wheelchair with a Developed Wireless DA Converter P300脑控轮椅无线数模转换器的设计与实现
Pub Date : 2023-08-01 DOI: 10.24297/ijct.v23i.9485
Zizhu Li, Boning Li, Wenping Luo, Jianting Cao
This article presents a P300 brain-controlled wheelchair system utilizing a wireless Digital-to-Analog converter for signal transmission. The wireless Digital-to-Analog converter addresses issues with device connectivity and simplifies signal transmission, removing the need for complex serial port protocols. A support vector machine model is trained to extract the P300 component from the Electroencephalogram signal. A P300 stimulator is designed to elicit the P300 component response, with subjects controlling the wheelchair's movement by looking at randomly flickering white circles. Experimental validation is conducted on a modified wheelchair, demonstrating the effectiveness and reliability of the proposed method.
本文介绍了一种利用无线数模转换器进行信号传输的P300脑控轮椅系统。无线数模转换器解决了设备连接问题,简化了信号传输,无需复杂的串行端口协议。训练支持向量机模型提取脑电信号中的P300分量。P300刺激器被设计用来引起P300成分的反应,受试者通过观察随机闪烁的白色圆圈来控制轮椅的运动。在一辆改装轮椅上进行了实验验证,验证了该方法的有效性和可靠性。
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引用次数: 0
期刊
INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY
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