Advances in Neuroimaging and Deep Learning for Emotion Detection: A Systematic Review of Cognitive Neuroscience and Algorithmic Innovations

Diagnostics, Год журнала: 2025, Номер 15(4), С. 456 - 456

Опубликована: Фев. 13, 2025

Background/Objectives: The following systematic review integrates neuroimaging techniques with deep learning approaches concerning emotion detection. It, therefore, aims to merge cognitive neuroscience insights advanced algorithmic methods in pursuit of an enhanced understanding and applications recognition. Methods: study was conducted PRISMA guidelines, involving a rigorous selection process that resulted the inclusion 64 empirical studies explore modalities such as fMRI, EEG, MEG, discussing their capabilities limitations It further evaluates architectures, including neural networks, CNNs, GANs, terms roles classifying emotions from various domains: human-computer interaction, mental health, marketing, more. Ethical practical challenges implementing these systems are also analyzed. Results: identifies fMRI powerful but resource-intensive modality, while EEG MEG more accessible high temporal resolution limited by spatial accuracy. Deep models, especially CNNs have performed well emotions, though they do not always require large diverse datasets. Combining data behavioral features improves classification performance. However, ethical challenges, privacy bias, remain significant concerns. Conclusions: has emphasized efficiencies detection, technical were highlighted. Future research should integrate advances, establish innovative enhance system reliability applicability.

Язык: Английский

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The Impact of Adaptive Learning Technologies, Personalized Feedback, and Interactive AI Tools on Student Engagement: The Moderating Role of Digital Literacy

Sustainability, Год журнала: 2025, Номер 17(3), С. 1133 - 1133

Опубликована: Янв. 30, 2025

Using adaptive learning technologies, personalized feedback, and interactive AI tools, this study investigates how these tools affect student engagement what the mediating role of individuals’ digital literacy is at same time. The will target 500 students from different faculties such as science, engineering, humanities, social sciences. With changing trends in educational technology, it important to know if allow interact with materials. Through study, we explore which adapt content students’ progress, are influenced by motivation participation during process using that provide real-time feedback interaction. Also, presented a moderating factor may either accelerate or impede effectiveness tools. These findings demonstrate more have organized help improve engagement. Additionally, higher levels involved This research recognizes teachers should incorporate technologies into their courses manner synergizes student’s capabilities reap benefits technology on outcomes.

Язык: Английский

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Challenging Cognitive Load Theory: The Role of Educational Neuroscience and Artificial Intelligence in Redefining Learning Efficacy

Brain Sciences, Год журнала: 2025, Номер 15(2), С. 203 - 203

Опубликована: Фев. 15, 2025

Background/Objectives: This systematic review integrates Cognitive Load Theory (CLT), Educational Neuroscience (EdNeuro), Artificial Intelligence (AI), and Machine Learning (ML) to examine their combined impact on optimizing learning environments. It explores how AI-driven adaptive systems, informed by neurophysiological insights, enhance personalized education for K-12 students adult learners. study emphasizes the role of Electroencephalography (EEG), Functional Near-Infrared Spectroscopy (fNIRS), other tools in assessing cognitive states guiding AI-powered interventions refine instructional strategies dynamically. Methods: reviews n = 103 papers related integration principles CLT with AI ML educational settings. evaluates progress made neuroadaptive technologies, especially real-time management load, feedback multimodal applications AI. Besides that, this research examines key hurdles such as data privacy, ethical concerns, algorithmic bias, scalability issues while pinpointing best practices robust effective implementation. Results: The results show that significantly improve Efficacy due managing load automatically, providing instruction, adapting pathways dynamically based data. Deep models Convolutional Neural Networks (CNNs), Recurrent (RNNs), Support Vector Machines (SVMs) classification accuracy, making systems more efficient scalable. Multimodal approaches system robustness mitigating signal variability noise-related limitations combining EEG fMRI, Electrocardiography (ECG), Galvanic Skin Response (GSR). Despite these advances, practical implementation challenges remain, including considerations, security risks, accessibility disparities across learner demographics. Conclusions: are epitomes redefinition potentials solid frameworks, inclusive design, scalable methodologies must inform. Future studies will be necessary refining pre-processing techniques, expanding variety datasets, advancing developing high-accuracy, affordable, ethically responsible systems. future AI-enhanced should inclusive, equitable, various populations would surmount technological dilemmas.

Язык: Английский

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From Neural Networks to Emotional Networks: A Systematic Review of EEG-Based Emotion Recognition in Cognitive Neuroscience and Real-World Applications

Brain Sciences, Год журнала: 2025, Номер 15(3), С. 220 - 220

Опубликована: Фев. 20, 2025

Background/Objectives: This systematic review presents how neural and emotional networks are integrated into EEG-based emotion recognition, bridging the gap between cognitive neuroscience practical applications. Methods: Following PRISMA, 64 studies were reviewed that outlined latest feature extraction classification developments using deep learning models such as CNNs RNNs. Results: Indeed, findings showed multimodal approaches practical, especially combinations involving EEG with physiological signals, thus improving accuracy of classification, even surpassing 90% in some studies. Key signal processing techniques used during this process include spectral features, connectivity analysis, frontal asymmetry detection, which helped enhance performance recognition. Despite these advances, challenges remain more significant real-time processing, where a trade-off computational efficiency limits implementation. High cost is prohibitive to use real-world applications, therefore indicating need for development application optimization techniques. Aside from this, obstacles inconsistency labeling emotions, variation experimental protocols, non-standardized datasets regarding generalizability recognition systems. Discussion: These developing adaptive, algorithms, integrating other inputs like facial expressions sensors, standardized protocols elicitation classification. Further, related ethical issues respect privacy, data security, machine model biases be much proclaimed responsibly apply research on emotions areas healthcare, human–computer interaction, marketing. Conclusions: provides critical insight suggestions further field toward robust, scalable, applications by consolidating current methodologies identifying their key limitations.

Язык: Английский

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Next-Generation Cognitive-Behavioral Therapy for Depression: Integrating Digital Tools, Teletherapy, and Personalization for Enhanced Mental Health Outcomes

Medicina, Год журнала: 2025, Номер 61(3), С. 431 - 431

Опубликована: Фев. 28, 2025

Background and Objectives: This systematic review aims to present the latest developments in next-generation CBT interventions of digital support tools, teletherapies, personalized treatment modules enhancing accessibility, improving adherence, optimizing therapeutic outcomes for depression. Materials Methods: analyzed 81 PRISMA-guided studies on efficacy, feasibility, applicability NG-CBT approaches. Other important innovations include web-based interventions, AI-operated chatbots, teletherapy platforms, each which serves as a critical challenge delivering mental health care. Key messages have emerged regarding technological readiness, patient engagement, changing role therapists within context Results: Findings indicate that improve accessibility engagement while maintaining clinical effectiveness. Personalized tools enhance platforms provide scalable cost-effective alternatives traditional therapy. Conclusions: Such promise great avenues decreasing global burden depression quality life through novel, accessible, high-quality

Язык: Английский

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Emotional Neuroscience and Academic Achievement: Current Trends and Perspectives

Technium Social Sciences Journal, Год журнала: 2025, Номер 67, С. 219 - 233

Опубликована: Янв. 8, 2025

This paper approaches the intersection of emotional neuroscience with academic achievement, placing a strong emphasis on critical role emotions play in learning. It challenges traditional view education and points out need for understanding measuring biology creating brain-friendly learning environments. Combining theoretical frameworks, discussion draws link between success by highlighting neurobiological foundations, regulation, stress, anxiety, neuroplasticity. Most importantly, it explains intelligence how this ability creates positive impact cognitive functioning outcomes. pushes issue multidimensionality neuroscientific knowledge to nurture well-being bring about greater achievement. reflects innovative interventions leading-edge innovations that can shape schools into emotionally supportive The conclusion calls interdisciplinary collaboration change educational practices. supports its arguments an extensive bibliography areas neuroscience, intelligence, mental health research.

Язык: Английский

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КӘСІПТІК БАҒДАР БЕРУДЕГІ ЖАСАНДЫ ИНТЕЛЛЕКТ – БҮГІНГІ МӘСЕЛЕЛЕР МЕН БОЛАШАҚҚА БОЛЖАМДАР

Eurasian science review., Год журнала: 2025, Номер 2(Special Issue), С. 1660 - 1670

Опубликована: Янв. 26, 2025

Жаңа технологиялардың пайда болуы мен автоматтандыру процесінің үдеуі көптеген мамандықтардың жойылып, орнына жаңа келуіне әкелуде. Бұл жағдай болашақ мамандардың өзгерістерге дайын болуын, сонымен қатар қазіргі заманғы қажеттіліктерге бейімделе алатын мамандықтарды таңдауды талап етеді. Сондықтан жастардың білім алу траекториясын дұрыс жоспарлап, еңбек нарығындағы қажетті дағдыларды меңгеруі үшін кәсіби бағдарлау қызметінің сапасы маңызды. Мақалада қызметінде жасанды интеллектіні қолдану мүмкіндіктеріне талдау жүргізілді. Машиналық оқыту, нейрондық желілер, деректер әлеуметтік желілерді талдау, табиғи тілді өңдеу және чат-боттар сияқты интеллект әдістері ұсыныстардың дәлдігі даралануын арттыра алатыны қарастырылған. Технологияларға қолжетімділік, деректердің құпиялылығы нарығымен жеткіліксіз интеграция мәселелеріне назар аударылды. Жасанды мамандықтарға деген сұранысты болжау, беру траекторияларын даралау бейімделген бағдар жүйелерін енгізу қолданудың болашағы жарқын екендігі анықталды. интеллекттің бағдарлауды трансформациялаудағы жоғары әлеуеті болғанымен, кездесетін кедергілерге шолу жасалды. Болашақта зерттеу нәтижелері осы салада инновациялық құралдар қызметтерді әзірлеу пайдаланылуы мүмкін.

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Integrating ARCS-V and MST motivation models into AI-supported distance education design: A synergistic approach

Açıköğretim Uygulamaları ve Araştırmaları Dergisi, Год журнала: 2025, Номер 11(1), С. 38 - 61

Опубликована: Янв. 28, 2025

This article proposes a new framework that integrates the ARCS-V (Attention, Relevance, Confidence, Satisfaction, and Volition) model Motivational Systems Theory (MST) into AI-supported distance learning environments. The proposed shows how integration of these models can support student motivation in more holistic way. By combining AI tools with assessment, adaptive interventions synergistic mechanisms, customized environments be developed according to needs. Combining strengths model, which focuses on providing engaging satisfying experiences, MST, emphasizes importance personal goals, emotions, environmental factors, this approach suggests effective way sustain motivation. paper examines MST combined intervention dimensions Artificial Intelligence education settings. integrating two motivational ODL AI, not only presentation content but also increased engagement achieved.

Язык: Английский

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Bert with Linear Regression Model for Captions Based Lecture Video Summarization

Опубликована: Янв. 1, 2025

Язык: Английский

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Policy Frameworks for Personalized STEAM Education

IGI Global eBooks, Год журнала: 2025, Номер unknown, С. 79 - 102

Опубликована: Март 4, 2025

This chapter highlights the growing importance of personalized STEAM education in preparing pupils for 21st-century issues. Individualized learning that meets students' needs and interests boosts creativity, critical thinking, problem-solving. Personalized matches preferences pace, topic, teaching approaches, improving engagement retention. Arts-STEM interaction promotes innovation holistic thinking. The notes efforts require strong policy foundations. Policy priorities include teacher training, equitable technological access, through AI adaptable platforms. By addressing these demands, governments institutions may establish a more inclusive educational system provides high-quality to all children, regardless socioeconomic background or academic ability. Finally, tailored prepares students quickly changing, multidimensional world.

Язык: Английский

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