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.
{"title":"Positive-Unlabeled Learning Method for Positive Emotion Recognition Using EEG technology","authors":"Zizhu Li, Chengyuan Shen, Jianting Cao","doi":"10.24297/ijct.v24i.9650","DOIUrl":"https://doi.org/10.24297/ijct.v24i.9650","url":null,"abstract":"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.","PeriodicalId":210853,"journal":{"name":"INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141922780","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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.
{"title":"Unveiling Neurophysiological Markers of Consciousness Levels through EEG Exploration","authors":"Jingming Gong, Linfeng Sui, Ran Zhang, Boning Li, Chengyuan Shen, ,Taiyo Maeda, Jianting Cao","doi":"10.24297/ijct.v24i.9627","DOIUrl":"https://doi.org/10.24297/ijct.v24i.9627","url":null,"abstract":"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.","PeriodicalId":210853,"journal":{"name":"INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141386371","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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。
{"title":"A NEW ROBUST HOMOMORPHIC ENCRYPTION SCHEME BASED ON PAILLIER, RESIDUE NUMBER SYSTEM AND EL-GAMAL","authors":"P. A. Agbedemnab, Abdul Somed Safianu and, Abdul-Mumin Selanwiah Salifu","doi":"10.24297/ijct.v24i.9606","DOIUrl":"https://doi.org/10.24297/ijct.v24i.9606","url":null,"abstract":"\u0000\u0000\u0000The 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.\u0000\u0000\u0000","PeriodicalId":210853,"journal":{"name":"INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140693210","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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.
{"title":"On Defining Smart Cities using Transformer Neural Networks","authors":"Andrei Khurshudov","doi":"10.24297/ijct.v24i.9579","DOIUrl":"https://doi.org/10.24297/ijct.v24i.9579","url":null,"abstract":"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.","PeriodicalId":210853,"journal":{"name":"INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140490788","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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.
{"title":"Convolutional Neural Networks for Deep Sleep Detection Based on Data Augmentation","authors":"Ruixuan Chen, Linfeng Sui, Mo Xia, Jinsha Liu, Tao Zhang, Jianting Cao","doi":"10.24297/ijct.v24i.9567","DOIUrl":"https://doi.org/10.24297/ijct.v24i.9567","url":null,"abstract":"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.","PeriodicalId":210853,"journal":{"name":"INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139592244","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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.
{"title":"Experimental Implementation approach of Crawling Framework CARE JOBS for Disability Job Search Using Intelligent Agents","authors":"Diana Avram","doi":"10.24297/ijct.v23i.9559","DOIUrl":"https://doi.org/10.24297/ijct.v23i.9559","url":null,"abstract":"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.","PeriodicalId":210853,"journal":{"name":"INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138953344","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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.
{"title":"Rotitome-G: Principles and design concept of experimental compliant continuum robotic microsurgical endoscopic sarcotome for pixel/voxel-level target access neurosurgery","authors":"H. S. Gandhi","doi":"10.24297/ijct.v23i.9549","DOIUrl":"https://doi.org/10.24297/ijct.v23i.9549","url":null,"abstract":"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.","PeriodicalId":210853,"journal":{"name":"INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138606517","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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.
{"title":"Construct and Evaluate a Phone Dialing System Leveraging SSVEP Brain-Computer Interface","authors":"Jinsha Liu, Boning Li, Jianting Cao","doi":"10.24297/ijct.v23i.9539","DOIUrl":"https://doi.org/10.24297/ijct.v23i.9539","url":null,"abstract":"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.","PeriodicalId":210853,"journal":{"name":"INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139242934","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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。
{"title":"Real-time Interpretation of EEG Signals for Consciousness State Assessment","authors":"Jingming Gong, Linfeng Sui, Ran Zhang, Boning Li, Chengyuan Shen, Jianting Cao","doi":"10.24297/ijct.v23i.9541","DOIUrl":"https://doi.org/10.24297/ijct.v23i.9541","url":null,"abstract":"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.","PeriodicalId":210853,"journal":{"name":"INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139262486","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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.
{"title":"Design and Implementation of P300 Brain-Controlled Wheelchair with a Developed Wireless DA Converter","authors":"Zizhu Li, Boning Li, Wenping Luo, Jianting Cao","doi":"10.24297/ijct.v23i.9485","DOIUrl":"https://doi.org/10.24297/ijct.v23i.9485","url":null,"abstract":"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.","PeriodicalId":210853,"journal":{"name":"INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117113666","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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