Suchergebnisse - "Brain-Computer Interfaces in Neuroscience and Medicine"

Revisión Bibliográfica: La Epilepsia

Autoren: Allison Belén Cevallos Anchundia Fajardo González Mariana Jisara Preciado Ayovi

Quelle: Ciencia Latina Revista Científica Multidisciplinar. 8:1979-1987

Schlagwörter: FOS: Psychology, Psychiatry, Epilepsy, Cognitive Neuroscience, Life Sciences, Psychology, Medicine, Brain-Computer Interfaces in Neuroscience and Medicine, Epilepsy Detection, Neuroscience

Neurophysiologically Meaningful Motor Imagery EEG Simulation With Applications to Data Augmentation

Autoren: Catalina María Galván Rubén D. Spies Diego H. Milone et al.

Quelle: IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol 32, Pp 2346-2355 (2024)

Schlagwörter: Brain modeling, Artificial intelligence, Data augmentation, Decoding, Cognitive Neuroscience, RM1-950, EEG Analysis, motor imagery, Deep Learning, Motor imagery, Cognitive psychology, Medical technology, Humans, Psychology, Computer Simulation, Deep Learning for EEG, R855-855.5, Data models, deep learning, Life Sciences, Electroencephalography, Brain-Computer Interfaces in Neuroscience and Medicine, Computer science, EEG simulation, FOS: Psychology, Brain–computer interface, surface EEG, Neural Ensemble Physiology, Brain-Computer Interfaces, Imagination, Therapeutics. Pharmacology, Motor Imagery, Brain-computer interfaces, Algorithms, data augmentation, Neuroscience

Dateibeschreibung: application/pdf

Zugangs-URL: https://pubmed.ncbi.nlm.nih.gov/38900612
https://doaj.org/article/d9067cc0dfc844de88495be9afb91f75
http://hdl.handle.net/11336/254693

EMG gesture signal analysis towards diagnosis of upper limb using dual-pathway convolutional neural network

Autoren: Hafiz Ghulam Murtza Qamar Muhammad Farrukh Qureshi Zohaib Mushtaq et al.

Quelle: Mathematical Biosciences and Engineering, Vol 21, Iss 4, Pp 5712-5734 (2024)
Qamar, H G M, Qureshi, M F, Mushtaq, Z, Zubariah, Z, Rehman, M Z U, Samee, N A, Mahmoud, N F, Gu, Y H & Al-Masni, M A 2024, 'EMG gesture signal analysis towards diagnosis of upper limb using dual-pathway convolutional neural network', Mathematical Biosciences and Engineering, vol. 21, no. 4, pp. 5712-5734. https://doi.org/10.3934/mbe.2024252

Schlagwörter: Male, electromyography, Artificial intelligence, log mel spectrogram, convolutional neural network, 02 engineering and technology, Surface EMG, Pattern recognition (psychology), Epilepsy Detection, EEG Analysis, 0302 clinical medicine, Engineering, Cognitive psychology, 0202 electrical engineering, electronic engineering, information engineering, Psychology, Gestures, Life Sciences, Signal Processing, Computer-Assisted, Neural Interface Technology, Middle Aged, Brain-Computer Interfaces in Neuroscience and Medicine, FOS: Psychology, Analysis of Electromyography Signal Processing, Physical Sciences, nina pro, Medicine, Female, Algorithms, Biotechnology, Adult, Cognitive Neuroscience, Biomedical Engineering, Convolutional neural network, upper limb, Speech recognition, FOS: Medical engineering, Upper Extremity, Young Adult, 03 medical and health sciences, Cellular and Molecular Neuroscience, Amputees, QA1-939, Humans, Deep Learning for EEG, gesture recognition, Electromyography, Reproducibility of Results, Computer science, Musculoskeletal Modeling, Physical medicine and rehabilitation, Recall, Neural Networks, Computer, TP248.13-248.65, Mathematics, Neuroscience

Dateibeschreibung: application/pdf

Zugangs-URL: https://pubmed.ncbi.nlm.nih.gov/38872555
https://doaj.org/article/9d76c19912c04629a23aa47278da8f07
https://vbn.aau.dk/ws/files/708673610/10.3934_mbe.2024252.pdf
http://www.scopus.com/inward/record.url?scp=85191355753&partnerID=8YFLogxK
https://vbn.aau.dk/da/publications/55d5565c-7538-426b-9b88-92f480607225
https://doi.org/10.3934/mbe.2024252

NeuroWave-Net: Enhancing epileptic seizure detection from EEG brain signals via advanced convolutional and long short-term memory networks

Autoren: Md. Mehedi Hassan R. Haque Sheikh Mohammed Shariful Islam et al.

Quelle: AIMS Bioengineering, Vol 11, Iss 1, Pp 85-109 (2024)

Schlagwörter: Artificial intelligence, Feature (linguistics), Classification of Brain Tumor Type and Grade, 02 engineering and technology, Pattern recognition (psychology), 01 natural sciences, Epilepsy Detection, EEG Analysis, Computer security, convolutional neural networks, Psychology, Life Sciences, Electroencephalography, Brain-Computer Interfaces in Neuroscience and Medicine, Long short term memory, FOS: Philosophy, ethics and religion, FOS: Psychology, epileptic seizure detection, Neurology, Physical Sciences, TP155-156, Deep Learning in Computer Vision and Image Recognition, Computer Vision and Pattern Recognition, eeg, long short-term memory, 0210 nano-technology, Biotechnology, Artificial neural network, Cognitive Neuroscience, Recurrent neural network, Convolutional neural network, neurowave-net, Chemical engineering, brain signal preprocessing, Machine learning, Medical technology, Neural Network Architectures, Deep Learning for EEG, R855-855.5, Key (lock), Deep learning, Linguistics, Computer science, 0104 chemical sciences, Philosophy, Neural Ensemble Physiology, Categorization, Computer Science, Epileptic seizure, FOS: Languages and literature, lstm-cnn, TP248.13-248.65, Neuroscience

Zugangs-URL: https://doaj.org/article/88d3b5b205a044a1ae1b51c2b1109f9e

A Comparative ResNet-50, InceptionV3 and EfficientNetB3 with Retinal Disease

Autoren: Warameth Nuipian Phayung Meesad Sarunya Kanjanawattana

Quelle: Proceedings of the 2023 7th International Conference on Natural Language Processing and Information Retrieval. :283-287

Schlagwörter: Artificial neural network, Radiology, Nuclear Medicine and Imaging, Artificial intelligence, Diabetic Retinopathy, Cognitive Neuroscience, Life Sciences, Applications of Deep Learning in Medical Imaging, Brain-Computer Interfaces in Neuroscience and Medicine, Computer science, 7. Clean energy, Retinal Disease, Residual neural network, 3. Good health, Detection and Management of Retinal Diseases, Ophthalmology, Health Sciences, Retinal, Medicine, Neuroscience

Gaze Tracking for Hands-Free Human Using Deep Reinforcement Learning Approach

Autoren: Irfan Ullah Abid Ali Shahid Rasool et al.

Quelle: Journal of Smart Internet of Things. 2023:105-114

Schlagwörter: 0301 basic medicine, Artificial intelligence, Cognitive Neuroscience, Social psychology, 7. Clean energy, 03 medical and health sciences, 0302 clinical medicine, Tracking (education), Reinforcement learning, Eye Tracking in Human-Computer Interaction, Psychology, 10. No inequality, Gaze, Eye tracking, Gaze Estimation, Remote Gaze Estimation, Hands free, Human–computer interaction, Pedagogy, 4. Education, Eye Tracking, Eye Movement Analysis, Life Sciences, Brain-Computer Interfaces in Neuroscience and Medicine, 15. Life on land, Computer science, Tactile Perception and Cross-modal Plasticity, Reinforcement, 3. Good health, Human-Computer Interaction, FOS: Psychology, Head Gesture Recognition, Computer Science, Physical Sciences, Computer vision, Neuroscience

ADHD/CD-NET: automated EEG-based characterization of ADHD and CD using explainable deep neural network technique

Autoren: Hui Wen Loh Chui Ping Ooi Shu Lih Oh et al.

Quelle: Cogn Neurodyn

Schlagwörter: Artificial intelligence, Cognitive Neuroscience, Clinical psychology, Geometry, Convolutional neural network, Comorbidity, Analysis of Brain Functional Connectivity Networks, Pattern recognition (psychology), Conduct disorder, Attention deficit hyperactivity disorder, 03 medical and health sciences, EEG Analysis, 0302 clinical medicine, Health Sciences, Machine learning, FOS: Mathematics, ADHD, Psychology, EEG, Deep Learning for EEG, Attention-Deficit/Hyperactivity Disorder, Grad-CAM, Psychiatry, Explainable artificial intelligence (XAI), Life Sciences, Deep learning, Electroencephalography, Brain-Computer Interfaces in Neuroscience and Medicine, Computer science, Correlation, 3. Good health, FOS: Psychology, Psychiatry and Mental health, Brain Network Development, Medicine, Conduct disorde, CNN, Mathematics, Research Article, Neuroscience

Dateibeschreibung: application/pdf

Zugangs-URL: https://pubmed.ncbi.nlm.nih.gov/39104684

Review on remote heart rate measurements using photoplethysmography

Autoren: R J Lee Saaveethya Sivakumar King Hann Lim

Quelle: Multimedia Tools and Applications. 83:44699-44728

Schlagwörter: Artificial intelligence, Photoplethysmogram, Cognitive Neuroscience, Non-contact Physiological Monitoring Technology, Biomedical Engineering, Generalization, Heart Rate Variability, 02 engineering and technology, FOS: Medical engineering, Heartbeat Classification, Mathematical analysis, Pulse Oximetry, Filter (signal processing), Engineering, Health Sciences, Machine learning, 0202 electrical engineering, electronic engineering, information engineering, Arrhythmia Detection, FOS: Mathematics, Photoplethysmography, Life Sciences, Deep learning, Analysis of Electrocardiogram Signals, Brain-Computer Interfaces in Neuroscience and Medicine, Computer science, Physical Sciences, Medicine, Computer vision, Artifact (error), Cardiology and Cardiovascular Medicine, Mathematics, Neuroscience

State-of-the-Art of Stress Prediction from Heart Rate Variability Using Artificial Intelligence

Autoren: Yasmeen Haque Rahat Shahriar Zawad Chowdhury Saleh Ahmed Rony et al.

Quelle: Cognitive Computation. 16:455-481

Schlagwörter: Artificial intelligence, Cognitive Neuroscience, Non-contact Physiological Monitoring Technology, Heart rate, Biomedical Engineering, Heart Rate Variability, 02 engineering and technology, FOS: Medical engineering, 7. Clean energy, 03 medical and health sciences, 0302 clinical medicine, Engineering, Continuous Blood Pressure Estimation, Health Sciences, Machine learning, 11. Sustainability, 0202 electrical engineering, electronic engineering, information engineering, Stress Marker, Data mining, Heart rate variability, Predictive modelling, Life Sciences, Brain-Computer Interfaces in Neuroscience and Medicine, Computer science, 3. Good health, Analysis and Applications of Heart Rate Variability, Physical Sciences, Blood pressure, Medicine, Cardiology and Cardiovascular Medicine, Radiology, Neuroscience

Different Methods of EEG Signal Analysis Using Power Spectral Density, ChronoNet and ResNest

Autoren: Md. Mehedi Hasan Senjuti Rahman Ajay Krishno Sarkar et al.

Quelle: European Journal of Electrical Engineering and Computer Science. 7:20-27

Schlagwörter: Blind Source Separation and Independent Component Analysis, Artificial intelligence, Support vector machine, Cognitive Neuroscience, Memristive Devices for Neuromorphic Computing, 02 engineering and technology, Pattern recognition (psychology), Bayesian probability, Epilepsy Detection, EEG Analysis, Bayes' theorem, 03 medical and health sciences, Engineering, Spectral density, 0302 clinical medicine, Machine learning, FOS: Electrical engineering, electronic engineering, information engineering, Decision tree, FOS: Mathematics, 0202 electrical engineering, electronic engineering, information engineering, Psychology, Deep Learning for EEG, Electrical and Electronic Engineering, Epilepsy, Naive Bayes classifier, 4. Education, Life Sciences, Electroencephalography, Brain-Computer Interfaces in Neuroscience and Medicine, 16. Peace & justice, Computer science, 3. Good health, FOS: Psychology, Neural Ensemble Physiology, Combinatorics, Physical Sciences, Signal Processing, Computer Science, Kernel (algebra), Telecommunications, Feature extraction, Mathematics, Neuroscience, Random forest

New approaches to recovery after stroke

Autoren: Daniel Stiven Marín-Medina Paula A. Arenas-Vargas Juan C. Arias-Botero et al.

Quelle: Neurol Sci

Schlagwörter: Cognitive Neuroscience, FOS: Mechanical engineering, Neural Plasticity, Review Article, Engineering, Health Sciences, Functional electrical stimulation, Humans, Psychology, Rehabilitation Techniques, Stroke recovery, Stroke Recovery, Cerebral Cortex, Neuronal Plasticity, Rehabilitation, Stroke Rehabilitation, Brain, Life Sciences, Principles and Interventions in Stroke Rehabilitation, Electroencephalography, Recovery of Function, Brain-Computer Interfaces in Neuroscience and Medicine, Effects of Brain Stimulation on Motor Cortex, Mechanical engineering, 3. Good health, Stroke, Virtual Reality Rehabilitation, Brain–computer interface, FOS: Psychology, Neurology, Physical medicine and rehabilitation, Brain stimulation, Neurorehabilitation, Stimulation, Medicine, Motor cortex, Neuroplasticity, Stroke (engine), Neuroscience

Zugangs-URL: https://pubmed.ncbi.nlm.nih.gov/37697027

Assessing the Potential of Data Augmentation in EEG Functional Connectivity for Early Detection of Alzheimer’s Disease

Autoren: Jialei Hao Zihao Huang César F. Caiafa et al.

Weitere Verfasser: Jialei Hao Zihao Huang César F. Caiafa et al.

Quelle: CONICET Digital (CONICET)
Consejo Nacional de Investigaciones Científicas y Técnicas

Schlagwörter: Artificial neural network, Aging, Artificial intelligence, Data augmentation, Neuroimaging Data Analysis, Cognitive Neuroscience, Bioengineering, Set (abstract data type), Neurodegenerative, Analysis of Brain Functional Connectivity Networks, Alzheimer's Disease, Pattern recognition (psychology), Epilepsy Detection, Cellular and Molecular Neuroscience, EEG Analysis, 46 Information and Computing Sciences, 4611 Machine Learning, Machine learning, Acquired Cognitive Impairment, Machine Learning and Artificial Intelligence, Psychology, EEG, Deep Learning for EEG, Neurosciences, Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD), Life Sciences, Electroencephalography, Neural Interface Technology, Brain-Computer Interfaces in Neuroscience and Medicine, Computer science, Brain Disorders, Programming language, 3. Good health, FOS: Psychology, Networking and Information Technology R&D (NITRD), Neurological, Brain Network Development, Dementia, Alzheimer disease, Neuroscience

Dateibeschreibung: application/pdf; text/xml

Zugangs-URL: http://hdl.handle.net/11336/221800
https://www.repository.cam.ac.uk/handle/1810/363423
https://doi.org/10.1007/s12559-023-10188-7

Extraction of Fetal Electrocardiogram by Combining Deep Learning and SVD-ICA-NMF Methods

Autoren: Said Ziani Yousef Farhaoui Mohammed Moutaib

Quelle: Big Data Mining and Analytics, Vol 6, Iss 3, Pp 301-310 (2023)

Schlagwörter: Blind Source Separation and Independent Component Analysis, Artificial intelligence, Cognitive Neuroscience, Convolutional neural network, Independent component analysis, Speech recognition, Pattern recognition (psychology), Quantum mechanics, convolutional neural network (cnn), Epilepsy Detection, Matrix decomposition, deep learning (dl), Health Sciences, Arrhythmia Detection, FOS: Mathematics, Deep Learning for EEG, Eigenvalues and eigenvectors, fetal electrocardiogram, feature extraction, Physics, Singular value decomposition, Pure mathematics, Life Sciences, Non-negative matrix factorization, QA75.5-76.95, Analysis of Electrocardiogram Signals, Brain-Computer Interfaces in Neuroscience and Medicine, ECG Signal, Independent Component Analysis, Computer science, Dimensionality reduction, Dimension (graph theory), Electronic computers. Computer science, Signal Processing, Computer Science, Physical Sciences, Medicine, Feature extraction, Cardiology and Cardiovascular Medicine, Mathematics, Neuroscience

Zugangs-URL: https://doaj.org/article/4d9db15e4968489f808cf92c27a8ccc4

Brain-computer interface prototype to support upper limb rehabilitation processes in the human body

Autoren: Daniela Camargo-Vargas Mauro Callejas-Cuervo Andrea Catherine Alarcón-Aldana

Quelle: International Journal of Information Technology. 15:3655-3667

Schlagwörter: Parallel computing, Interface (matter), Artificial intelligence, Linear discriminant analysis, Cognitive Neuroscience, Biomedical Engineering, BCI Communication, 02 engineering and technology, FOS: Medical engineering, Virtual reality, Cellular and Molecular Neuroscience, 03 medical and health sciences, Engineering, 0302 clinical medicine, 0202 electrical engineering, electronic engineering, information engineering, Psychology, Bubble, Psychiatry, Sensory Feedback, Human–computer interaction, Rehabilitation, Life Sciences, Electroencephalography, Neural Interface Technology, Brain-Computer Interfaces in Neuroscience and Medicine, Brain Tissue Response, Avatar, Computer science, Virtual machine, BCI Technology, Brain–computer interface, FOS: Psychology, Operating system, Analysis of Electromyography Signal Processing, Brain-Computer Interfaces, Physical Sciences, Maximum bubble pressure method, Simulation, Neuroscience

A ROBUST FRAMEWORK FOR DRIVER FATIGUE DETECTION FROM EEG SIGNALS USING ENHANCEMENT OF MODIFIED Z-SCORE AND MULTIPLE MACHINE LEARNING ARCHITECTURES

Autoren: Rafiuddin Abdubrani Mahfuzah Mustafa Zarith Liyana Zahari

Quelle: International Islamic University Malaysia Engineering Journal, Vol 24, Iss 2 (2023)

Schlagwörter: Artificial neural network, z-score, Artificial intelligence, Support vector machine, Cognitive Neuroscience, Social Sciences, Experimental and Cognitive Psychology, Convolutional neural network, 02 engineering and technology, Speech recognition, Pattern recognition (psychology), Epilepsy Detection, EEG Analysis, 0502 economics and business, Health Sciences, Machine learning, 0202 electrical engineering, electronic engineering, information engineering, Psychology, Driver Fatigue, Deep Learning for EEG, Psychiatry, 05 social sciences, deep learning, Life Sciences, Electroencephalography, electroencephalogram, Brain-Computer Interfaces in Neuroscience and Medicine, Engineering (General). Civil engineering (General), 16. Peace & justice, Computer science, Programming language, 3. Good health, Effects of Sleep Deprivation and Fatigue on Performance, FOS: Psychology, Driver fatigue, Neural Ensemble Physiology, Analysis and Applications of Heart Rate Variability, Medicine, Feature extraction, TA1-2040, Software portability, Cardiology and Cardiovascular Medicine, Neuroscience

Zugangs-URL: https://doaj.org/article/c3adfee6a0654c048c757540dc262698

Spectral features of resting-state EEG in Parkinson's Disease: A multicenter study using functional data analysis

Autoren: Alberto Jaramillo‐Jimenez Diego Alejandro Tovar‐Rios Johann Alexis Ospina et al.

Quelle: Clinical Neurophysiology

Schlagwörter: MILD COGNITIVE IMPAIRMENT, PREDICTION, Parkinson's disease, Astronomy, Pattern recognition (psychology), Astrophysics, 09 Engineering, Epilepsy Detection, EEG Analysis, Cognitive psychology, Psychology, Disease, BODY, Neuronal Oscillations in Cortical Networks, Internal medicine, 11 Medical and Health Sciences, Spectroscopy, UTILITY, Parkinson's Disease, Construct validity, DEMENTIA, Physics, Statistics, Life Sciences, Parkinson Disease, Electroencephalography, Alpha rhythm, Neural Interface Technology, Audiology, Brain-Computer Interfaces in Neuroscience and Medicine, LEWY BODIES, 17 Psychology and Cognitive Sciences, FOS: Psychology, Medicine, Resting state fMRI, Life Sciences & Biomedicine, Psychometrics, DIAGNOSTIC-CRITERIA, Cognitive Neuroscience, POWER, Clinical Neurology, Spectral analysis, Alpha (finance), MOVEMENT, Cellular and Molecular Neuroscience, Spectral density, QUANTITATIVE EEG, Theta rhythm, FOS: Mathematics, Humans, VDP::Medisinske Fag: 700, Deep Learning for EEG, Science & Technology, Neurology & Neurosurgery, Neurosciences, Reproducibility of Results, Functional Data Analysis, Epoch (astronomy), Functional data analysis, Galaxy, Neural Ensemble Physiology, 3209 Neurosciences, Neurosciences & Neurology, Beta Rhythm, Mathematics, Neuroscience

Dateibeschreibung: application/pdf; 13 páginas

Zugangs-URL: https://pubmed.ncbi.nlm.nih.gov/37146531
https://www.sciencedirect.com/science/article/pii/S1388245723005989?via=ihub
https://hdl.handle.net/11564/809911
https://doi.org/10.1016/j.clinph.2023.03.363
https://lirias.kuleuven.be/handle/20.500.12942/765568
https://doi.org/10.1016/j.clinph.2023.03.363
https://hdl.handle.net/11250/3119190
https://hdl.handle.net/10614/15852
https://doi.org/10.1016/j.clinph.2023.03.363

Design, Analysis, and Control of Biomedical Healthcare Modular Wheelchair with Posture Transformation

Autoren: Muhammad Zia Ur Rahman Imran Ali Asif Ishfaque et al.

Quelle: Complexity, Vol 2023 (2023)

Schlagwörter: Economics, Cognitive Neuroscience, Biomedical Engineering, FOS: Medical engineering, 03 medical and health sciences, EEG Analysis, 0302 clinical medicine, Engineering, Pathology, Eye Tracking in Human-Computer Interaction, Lower Limb Exoskeleton Robotics, Biology, Modular design, Controller (irrigation), Life Sciences, QA75.5-76.95, Wheelchair, Brain-Computer Interfaces in Neuroscience and Medicine, Computer science, Agronomy, Management, Human-Computer Interaction, World Wide Web, Operating system, Center of gravity, Physical medicine and rehabilitation, Electronic computers. Computer science, Computer Science, Physical Sciences, Medicine, 0305 other medical science, Sitting, Simulation, Neuroscience

Dateibeschreibung: text/xhtml

Zugangs-URL: https://doaj.org/article/ceed6058a77c44488fc1f2ea98ea9c14

Towards Improving Motor Imagery Brain–Computer Interface Using Multimodal Speech Imagery

Autoren: Jigang Tong Zhengxing Xing Xiaoying Wei et al.

Quelle: Journal of Medical and Biological Engineering. 43:216-226

Schlagwörter: Parallel computing, Interface (matter), Artificial intelligence, Pronunciation, Pinyin, Cognitive Neuroscience, Memristive Devices for Neuromorphic Computing, 0206 medical engineering, 02 engineering and technology, Speech recognition, 7. Clean energy, 03 medical and health sciences, Cellular and Molecular Neuroscience, 0302 clinical medicine, Engineering, Motor imagery, Reading (process), FOS: Electrical engineering, electronic engineering, information engineering, Psychology, Electrical and Electronic Engineering, Brain-inspired Computing, Neuromorphic Computing, Bubble, Life Sciences, Electroencephalography, Linguistics, Neural Interface Technology, Brain-Computer Interfaces in Neuroscience and Medicine, 16. Peace & justice, Computer science, Chinese characters, BCI Technology, FOS: Philosophy, ethics and religion, Brain–computer interface, FOS: Psychology, Philosophy, Brain-Computer Interfaces, Physical Sciences, FOS: Languages and literature, Motor Imagery, Maximum bubble pressure method, Neuroscience

Propofol Reversibly Attenuates Short-Range Microstate Ordering and 20 Hz Microstate Oscillations

Autoren: Gesine Hermann Inken Tödt Enzo Tagliazucchi et al.

Quelle: Brain Topography. 37:329-342

Schlagwörter: Artificial intelligence, Cognitive Neuroscience, Memristive Devices for Neuromorphic Computing, Quantum mechanics, EEG Analysis, Engineering, FOS: Electrical engineering, electronic engineering, information engineering, FOS: Mathematics, Humans, Cluster Analysis, Ministate, Entropy (arrow of time), Psychology, Electrical and Electronic Engineering, Propofol, Neuronal Oscillations in Cortical Networks, Physics, Brain, Life Sciences, Electroencephalography, Brain-Computer Interfaces in Neuroscience and Medicine, Computer science, FOS: Psychology, Alpha Rhythm, Physical Sciences, Statistical physics, Mathematics, Neuroscience

Zugangs-URL: https://pubmed.ncbi.nlm.nih.gov/38228923

Brain functional connectivity‐based prediction of vagus nerve stimulation efficacy in pediatric pharmacoresistant epilepsy

Autoren: Hao Chen Yi Wang Taoyun Ji et al.

Quelle: CNS Neurosci Ther

Schlagwörter: Drug Resistant Epilepsy, Vagus Nerve Stimulation, Cognitive Neuroscience, Parkinson's disease, Epilepsy Detection, Vagus nerve, Neuro-Immune Modulation via Vagus Nerve Stimulation, EEG Analysis, Epilepsy and Seizures, Epilepsy Treatment, Health Sciences, Deep brain stimulation, Humans, Disease, Child, Ictal, Internal medicine, Psychiatry, Epilepsy, Cohort, Brain, Life Sciences, Electroencephalography, Vagus Nerve, Original Articles, Brain-Computer Interfaces in Neuroscience and Medicine, Neural Reflexes, 3. Good health, Psychiatry and Mental health, Treatment Outcome, Neurology, Stimulation, Medicine, Vagus nerve stimulation, Neuroscience, Temporal lobe

Zugangs-URL: https://pubmed.ncbi.nlm.nih.gov/37170486