AIMC Topic: Electroencephalography

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Classification of Alzheimer's Disease with Respect to Physiological Aging with Innovative EEG Biomarkers in a Machine Learning Implementation.

Journal of Alzheimer's disease : JAD Jan 1, 2020
BACKGROUND: Several studies investigated clinical and instrumental differences to make diagnosis of dementia in general and in Alzheimer's disease (AD) in particular with the aim to classify, at the individual level, AD patients and healthy controls ...
Sensitivity and Specificity Aging Support Vector Machine Biomarkers Brain Aged Neuropsychological Tests Electroencephalography Alzheimer Disease Brain Waves Humans
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Assessment of mental workload based on multi-physiological signals.

Technology and health care : official journal of the European Society for Engineering and Medicine Jan 1, 2020
BACKGROUND: Mental workload is one of the contributing factors to human errors in road accidents or other potentially adverse incidents.
Humans Young Adult Workload Attention Image Interpretation, Computer-Assisted Task Performance and Analysis Mental Fatigue Adult Support Vector Machine Male Electroencephalography Electrocardiography Reaction Time
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General principles of machine learning for brain-computer interfacing.

Handbook of clinical neurology Jan 1, 2020
Brain-computer interfaces (BCIs) are systems that translate brain activity patterns into commands that can be executed by an artificial device. This enables the possibility of controlling devices such as a prosthetic arm or exoskeleton, a wheelchair,...
Electroencephalography Signal Processing, Computer-Assisted Brain-Computer Interfaces Machine Learning Humans Algorithms Brain
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Development of Expert-Level Automated Detection of Epileptiform Discharges During Electroencephalogram Interpretation.

JAMA neurology Jan 1, 2020
IMPORTANCE: Interictal epileptiform discharges (IEDs) in electroencephalograms (EEGs) are a biomarker of epilepsy, seizure risk, and clinical decline. However, there is a scarcity of experts qualified to interpret EEG results. Prior attempts to autom...
Sensitivity and Specificity Electroencephalography Epilepsy Signal Processing, Computer-Assisted Humans Neural Networks, Computer Software
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Changes in Functional Connectivity Predict Outcome of Repetitive Transcranial Magnetic Stimulation Treatment of Major Depressive Disorder.

Cerebral cortex (New York, N.Y. : 1991) Dec 17, 2019
Repetitive transcranial magnetic stimulation (rTMS) treatment of major depressive disorder (MDD) is associated with changes in brain functional connectivity (FC). These changes may be related to the mechanism of action of rTMS and explain the variabi...
Prefrontal Cortex Treatment Outcome Electroencephalography Neural Pathways Transcranial Direct Current Stimulation Male Middle Aged Depressive Disorder, Major Machine Learning Brain Female Humans
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Automated sleep stage scoring of the Sleep Heart Health Study using deep neural networks.

Sleep Oct 21, 2019
STUDY OBJECTIVES: Polysomnography (PSG) scoring is labor intensive and suffers from variability in inter- and intra-rater reliability. Automated PSG scoring has the potential to reduce the human labor costs and the variability inherent to this task. ...
Electroencephalography Electromyography Neural Networks, Computer Polysomnography Electrooculography Reproducibility of Results Humans Sleep Stages Deep Learning
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Outcome Prediction in Postanoxic Coma With Deep Learning.

Critical care medicine Oct 1, 2019
OBJECTIVES: Visual assessment of the electroencephalogram by experienced clinical neurophysiologists allows reliable outcome prediction of approximately half of all comatose patients after cardiac arrest. Deep neural networks hold promise to achieve ...
Humans Predictive Value of Tests Electroencephalography Heart Arrest Coma Prospective Studies Male Middle Aged Hypoxia, Brain Female Deep Learning Aged
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Statistical algorithms for emotion classification via functional connectivity.

Journal of integrative neuroscience Sep 30, 2019
Pattern recognition algorithms decode emotional brain states by using functional connectivity measures which are extracted from EEG signals as input to the statistical classifiers. An open-access EEG dataset for emotional state analysis is used to cl...
Signal Processing, Computer-Assisted Humans Neural Pathways Brain Arousal Female Support Vector Machine Adult Emotions Male Electroencephalography
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Percept-related EEG classification using machine learning approach and features of functional brain connectivity.

Chaos (Woodbury, N.Y.) Sep 1, 2019
Machine learning is a promising approach for electroencephalographic (EEG) trials classification. Its efficiency is largely determined by the feature extraction and selection techniques reducing the dimensionality of input data. Dimensionality reduct...
Female Electroencephalography Nerve Net Adult Humans Male Machine Learning Brain
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[A TrAdaBoost-based method for detecting multiple subjects' P300 potentials].

Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi Aug 25, 2019
Individual differences of P300 potentials lead to that a large amount of training data must be collected to construct pattern recognition models in P300-based brain-computer interface system, which may cause subjects' fatigue and degrade the system p...
Discriminant Analysis Event-Related Potentials, P300 Humans Electroencephalography Brain-Computer Interfaces Algorithms Support Vector Machine
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