The Application of Entropy in Motor Imagery Paradigms of Brain–Computer Interfaces

Brain Sciences, Год журнала: 2025, Номер 15(2), С. 168 - 168

Опубликована: Фев. 8, 2025

Background: In motor imagery brain-computer interface (MI-BCI) research, electroencephalogram (EEG) signals are complex and nonlinear. This complexity nonlinearity render signal processing classification challenging when employing traditional linear methods. Information entropy, with its intrinsic nonlinear characteristics, effectively captures the dynamic behavior of EEG signals, thereby addressing limitations methods in capturing features. However, multitude entropy types leads to unclear application scenarios, a lack systematic descriptions. Methods: study conducted review 63 high-quality research articles focused on MI-BCI, published between 2019 2023. It summarizes names, functions, scopes 13 commonly used measures. Results: The findings indicate that sample (16.3%), Shannon (13%), fuzzy (12%), permutation (9.8%), approximate (7.6%) most frequently utilized features MI-BCI. majority studies employ single feature (79.7%), dual (9.4%) triple (4.7%) being prevalent combinations multiple applications. incorporation can significantly enhance pattern accuracy (by 8-10%). Most (67%) utilize public datasets for verification, while minority design conduct experiments (28%), only 5% combine both Conclusions: Future should delve into effects various specific problems clarify their scenarios. As methodologies continue evolve advance, poised play significant role wide array fields contexts.

Язык: Английский

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

Frontiers in Human Neuroscience, Год журнала: 2024, Номер 18

Опубликована: Май 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.

Язык: Английский

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Fractal dimension and clinical neurophysiology fusion to gain a deeper brain signal understanding: A systematic review

Information Fusion, Год журнала: 2025, Номер unknown, С. 102936 - 102936

Опубликована: Янв. 1, 2025

Язык: Английский

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A Spiking Neural Network Approach for Classifying Hand Movement and Relaxation from EEG Signal using Time Domain Features

WSEAS TRANSACTIONS ON BIOLOGY AND BIOMEDICINE, Год журнала: 2025, Номер 22, С. 133 - 151

Опубликована: Янв. 21, 2025

High-performance prosthetic and exoskeleton systems based on EEG signals can improve the quality of life hand-impaired people. Effective controlling these assistive devices requires accurate signal classification. Although there have been advancements in Brain-Computer Interface (BCI) systems, still classifying with high accuracy is a great challenge. The objective this research to investigate classification Spiking Neural Network (SNN) classifier for factual exact control individuals hand impairment. dataset has taken from BNCI Horizon 2020 website, which movement-relax events patient spinal cord injury (SCI) operate neuro-prosthetic device attached paralyzed right upper limb. fusion Dispersion Entropy (DE), Fuzzy (FE), Fluctuation (FDE) mean skewness features are extracted Motor Imagery (MI) applied classifier. To compare performance algorithm, same used Convolutional (CNN), Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Logistic Regression (LR) classifiers. It found that SNN given highest 80% precision 80.95%, recall 77.28%, F1-score 79.07%. This indicates five greater potential BCI system-based applications.

Язык: Английский

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Decoding the Mind: Translating Human Thought with EEG Signals

Communications in computer and information science, Год журнала: 2025, Номер unknown, С. 180 - 190

Опубликована: Янв. 1, 2025

Язык: Английский

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Brain–computer interfaces in 2023–2024

Brain‐X, Год журнала: 2025, Номер 3(1)

Опубликована: Март 1, 2025

Abstract Brain–computer interfaces (BCIs) have advanced at a rapid pace in recent years, particularly the medical domain. This review provides comprehensive summary of progress made BCIs during 2023–2024 period, covering wide range topics from invasive to non‐invasive techniques, and fundamental mechanisms clinical applications. The period saw numerous research breakthroughs applications BCI technology. As hardware software continue evolve, as understanding basic principles deepens, expectation is that innovative inventions will increasingly be introduced practice. Both technologies are paving way for broader It anticipated offer greater hope disease treatment, provide additional methods enhancing human bodily functions, ultimately improve quality life.

Язык: Английский

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Post-stroke spontaneous motor recovery in mice can be predicted from acute-phase local field potential using machine learning

APL Bioengineering, Год журнала: 2025, Номер 9(2)

Опубликована: Апрель 22, 2025

Stroke remains a leading cause of long-term disability, underscoring the urgent need for effective predictors motor recovery. Understanding electrophysiological changes underlying spontaneous recovery could offer critical insight into mechanisms and aid in predicting individual rehabilitation trajectories. In this study, we investigated predictive power local field potentials recorded 2 days post-stroke to forecast 1 month mouse model ischemic stroke. By employing comprehensive machine learning approach, identified key features that significantly enhanced prediction accuracy. Through nested leave-one-animal-out cross-validation, achieved high accuracy, correctly identifying status 15 out 16 mice. Our findings also revealed pre-stroke brain activity did not contribute suggesting dynamics are primary determinants Notably, found from contralesional hemisphere were particularly influential outcomes, role non-lesioned data-driven methodology underscores importance balancing feature selection optimize performance, context recovery, where natural processes can guide development targeted strategies. Ultimately, our advocate deeper understanding improve clinical outcomes stroke patients.

Язык: Английский

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Optimized deep learning models for stress-based stroke prediction from EEG signals

Deleted Journal, Год журнала: 2025, Номер 7(6)

Опубликована: Май 23, 2025

Язык: Английский

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EEGGAN-Net: enhancing EEG signal classification through data augmentation

Frontiers in Human Neuroscience, Год журнала: 2024, Номер 18

Опубликована: Июнь 21, 2024

Emerging brain-computer interface (BCI) technology holds promising potential to enhance the quality of life for individuals with disabilities. Nevertheless, constrained accuracy electroencephalography (EEG) signal classification poses numerous hurdles in real-world applications.

Язык: Английский

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A Post-Stroke Rehabilitation System With Compensatory Movement Detection Using Virtual Reality and Electroencephalogram Technologies

IEEE Access, Год журнала: 2024, Номер 12, С. 61418 - 61432

Опубликована: Янв. 1, 2024

Stroke is a leading cause of global population mortality and disability, imposing burdens on patients caregivers, significantly affecting the quality life patients. Therefore, in this study, we aimed to explore application virtual reality technology physical therapy by using immersive interactive training designing rehabilitation modes for individual group settings. We also provide with stroke comprehensive home-based treatment plan, ultimately enhancing effectiveness. Patients can engage through system undergo functional, mirror, constraint-induced therapies tailored different task contents. Simultaneously, brain-computer interface technology, an emotion analysis mechanism was designed map patients' brainwave signal data onto two-dimensional space positive-negative valence arousal; approach enable remote therapists discern emotional states during process spaces, facilitating timely adjustments tasks. Moreover, prevent compromised effectiveness owing improper postures compensation, offers real-time identification recording, promptly issuing alerts when compensation occurs. The provides multiuser space, enabling corrections observations, offering program, thereby realizing localized aging care model.

Язык: Английский

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