Artificial Intelligence and Neuroscience: Transformative Synergies in Brain Research and Clinical Applications

Journal of Clinical Medicine, Journal Year: 2025, Volume and Issue: 14(2), P. 550 - 550

Published: Jan. 16, 2025

The convergence of Artificial Intelligence (AI) and neuroscience is redefining our understanding the brain, unlocking new possibilities in research, diagnosis, therapy. This review explores how AI’s cutting-edge algorithms—ranging from deep learning to neuromorphic computing—are revolutionizing by enabling analysis complex neural datasets, neuroimaging electrophysiology genomic profiling. These advancements are transforming early detection neurological disorders, enhancing brain–computer interfaces, driving personalized medicine, paving way for more precise adaptive treatments. Beyond applications, itself has inspired AI innovations, with architectures brain-like processes shaping advances algorithms explainable models. bidirectional exchange fueled breakthroughs such as dynamic connectivity mapping, real-time decoding, closed-loop systems that adaptively respond states. However, challenges persist, including issues data integration, ethical considerations, “black-box” nature many systems, underscoring need transparent, equitable, interdisciplinary approaches. By synthesizing latest identifying future opportunities, this charts a path forward integration neuroscience. From harnessing multimodal cognitive augmentation, fusion these fields not just brain science, it reimagining human potential. partnership promises where mysteries unlocked, offering unprecedented healthcare, technology, beyond.

Language: Английский

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Decoding Pain: A Comprehensive Review of Computational Intelligence Methods in Electroencephalography-Based Brain–Computer Interfaces

Diagnostics, Journal Year: 2025, Volume and Issue: 15(3), P. 300 - 300

Published: Jan. 27, 2025

Objective pain evaluation is crucial for determining appropriate treatment strategies in clinical settings. Studies have demonstrated the potential of using brain–computer interface (BCI) technology classification and detection. Collating knowledge insights from prior studies, this review explores extensive work on detection based electroencephalography (EEG) signals. It presents findings, methodologies, advancements reported 20 peer-reviewed articles that utilize machine learning deep (DL) approaches EEG-based We analyze various ML DL techniques, support vector machines, random forests, k-nearest neighbors, convolution neural network recurrent networks transformers, their effectiveness decoding The motivation combining AI with BCI lies significant real-time responsiveness adaptability these systems. reveal techniques effectively EEG signals recognize pain-related patterns. Moreover, we discuss challenges associated detection, focusing applications settings functional requirements effective By evaluating current research landscape, identify gaps opportunities future to provide valuable researchers practitioners.

Language: Английский

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Objective Pain Assessment Using Deep Learning Through EEG-Based Brain–Computer Interfaces

Biology, Journal Year: 2025, Volume and Issue: 14(2), P. 210 - 210

Published: Feb. 17, 2025

Objective pain measurements are essential in clinical settings for determining effective treatment strategies. This study aims to utilize brain–computer interface technology reliable classification and detection. We developed an electroencephalography-based detection system comprising two main components: (1) pain/no-pain (2) severity across three levels: low, moderate, high. Deep learning models, including convolutional neural networks recurrent networks, were employed classify the wavelet features extracted through time–frequency domain analysis. Furthermore, we compared performance of our against conventional machine such as support vector machines random forest classifiers. Our deep approach outperformed baseline achieving accuracies 91.84% 87.94% classification, respectively.

Language: Английский

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Artificial intelligence for life sciences: A comprehensive guide and future trends

The Innovation Life, Journal Year: 2024, Volume and Issue: unknown, P. 100105 - 100105

Published: Jan. 1, 2024

<p>Artificial intelligence has had a profound impact on life sciences. This review discusses the application, challenges, and future development directions of artificial in various branches sciences, including zoology, plant science, microbiology, biochemistry, molecular biology, cell developmental genetics, neuroscience, psychology, pharmacology, clinical medicine, biomaterials, ecology, environmental science. It elaborates important roles aspects such as behavior monitoring, population dynamic prediction, microorganism identification, disease detection. At same time, it points out challenges faced by application data quality, black-box problems, ethical concerns. The are prospected from technological innovation interdisciplinary cooperation. integration Bio-Technologies (BT) Information-Technologies (IT) will transform biomedical research into AI for Science paradigm.</p>

Language: Английский

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Mapping the evolution of neurofeedback research: a bibliometric analysis of trends and future directions

Frontiers in Human Neuroscience, Journal Year: 2024, Volume and Issue: 18

Published: May 10, 2024

Introduction This study conducts a bibliometric analysis on neurofeedback research to assess its current state and potential future developments. Methods It examined 3,626 journal articles from the Web of Science (WoS) using co-citation co-word methods. Results The identified three major clusters: “Real-Time fMRI Neurofeedback Self-Regulation Brain Activity,” “EEG Cognitive Performance Enhancement,” “Treatment ADHD Using Neurofeedback.” highlighted four key “Neurofeedback in Mental Health Research,” “Brain-Computer Interfaces for Stroke Rehabilitation,” Youth,” “Neural Mechanisms Emotion with Advanced Neuroimaging. Discussion in-depth significantly enhances our understanding dynamic field neurofeedback, indicating treating improving performance. offers non-invasive, ethical alternatives conventional psychopharmacology aligns trend toward personalized medicine, suggesting specialized solutions mental health rehabilitation as growing focus medical practice.

Language: Английский

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Cognitive State Classification Using Convolutional Neural Networks on Gamma-Band EEG Signals

Applied Sciences, Journal Year: 2024, Volume and Issue: 14(18), P. 8380 - 8380

Published: Sept. 18, 2024

This study introduces a novel methodology for classifying cognitive states using convolutional neural networks (CNNs) on electroencephalography (EEG) data of 41 students, aimed at streamlining the traditionally labor-intensive analysis procedures utilized in EEGLAB. Concentrating 30–40 Hz frequency range within gamma band, we developed CNN model to analyze EEG signals recorded from inferior parietal lobule during various tasks. The demonstrated substantial efficacy, achieving an accuracy 91.42%, precision 71.41%, and recall 72.51%, effectively distinguishing between high low activity states. performance surpasses traditional machine learning methods analysis, such as support vector machines random forests, which typically achieve accuracies 70–85% similar Our approach offers significant time savings over manual EEGLAB methods. integration event-related spectral perturbation (ERSP) with architecture enables capture both fine-grained broad features, advancing field computational neuroscience. research has implications brain-computer interfaces, clinical diagnostics, monitoring, offering more efficient accurate alternative current

Language: Английский

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Classification of motor imagery tasks in brain–computer interface using ensemble learning

Elsevier eBooks, Journal Year: 2025, Volume and Issue: unknown, P. 99 - 120

Published: Jan. 1, 2025

Language: Английский

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Introduction to brain–computer interface: research trends and applications

Elsevier eBooks, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 20

Published: Jan. 1, 2025

Language: Английский

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Artificial intelligence in stroke rehabilitation: From acute care to long-term recovery

Neuroscience, Journal Year: 2025, Volume and Issue: unknown

Published: March 1, 2025

Language: Английский

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Automated Detection of Neurological and Mental Health Disorders Using EEG Signals and Artificial Intelligence: A Systematic Review

Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery, Journal Year: 2025, Volume and Issue: 15(1)

Published: March 1, 2025

ABSTRACT Mental and neurological disorders significantly impact global health. This systematic review examines the use of artificial intelligence (AI) techniques to automatically detect these conditions using electroencephalography (EEG) signals. Guided by Preferred Reporting Items for Systematic Reviews Meta‐Analysis (PRISMA), we reviewed 74 carefully selected studies published between 2013 August 2024 that used machine learning (ML), deep (DL), or both two methods mental health EEG The most common prevalent disorder types were sourced from major databases, including Scopus, Web Science, Science Direct, PubMed, IEEE Xplore. Epilepsy, depression, Alzheimer's disease are studied meet our evaluation criteria, 32, 12, 10 identified on topics, respectively. Conversely, number meeting criteria regarding stress, schizophrenia, Parkinson's disease, autism spectrum was relatively more average: 6, 4, 3, diseases least met one study each seizure, stroke, anxiety diseases, examining epilepsy together. Support Vector Machines (SVM) widely in ML methods, while Convolutional Neural Networks (CNNs) dominated DL approaches. generally outperformed traditional ML, as they yielded higher performance huge data. We observed complex decision process during feature extraction signals ML‐based models impacted results, DL‐based handled this efficiently. AI‐based analysis shows promise automated detection conditions. Future research should focus multi‐disease studies, standardizing datasets, improving model interpretability, developing clinical support systems assist diagnosis treatment disorders.

Language: Английский

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