AIMC Topic: Electroencephalography

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On the Vulnerability of CNN Classifiers in EEG-Based BCIs.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society Apr 2, 2019
Deep learning has been successfully used in numerous applications because of its outstanding performance and the ability to avoid manual feature engineering. One such application is electroencephalogram (EEG)-based brain-computer interface (BCI), whe...
Deep Learning Models, Theoretical Electroencephalography Signal Processing, Computer-Assisted Neural Networks, Computer Machine Learning Algorithms Reproducibility of Results Brain-Computer Interfaces Event-Related Potentials, P300 Imagination Humans
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DOSED: A deep learning approach to detect multiple sleep micro-events in EEG signal.

Journal of neuroscience methods Apr 1, 2019
BACKGROUND: Electroencephalography (EEG) monitors brain activity during sleep and is used to identify sleep disorders. In sleep medicine, clinicians interpret raw EEG signals in so-called sleep stages, which are assigned by experts to every 30s windo...
Arousal Sleep Stages Electroencephalography Signal Processing, Computer-Assisted Sleep Polysomnography Humans Brain Female Young Adult Adult Male Deep Learning
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Common spatial pattern and wavelet decomposition for motor imagery EEG- fTCD brain-computer interface.

Journal of neuroscience methods Apr 1, 2019
BACKGROUND: Recently, hybrid brain-computer interfaces (BCIs) combining more than one modality have been investigated with the aim of boosting the performance of the existing single-modal BCIs in terms of accuracy and information transfer rate (ITR)....
Brain Female Adult Male Humans Ultrasonography, Doppler, Transcranial Brain-Computer Interfaces Functional Neuroimaging Motor Activity Support Vector Machine Visual Perception Electroencephalography Imagination Pattern Recognition, Automated
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Using natural language processing to extract structured epilepsy data from unstructured clinic letters: development and validation of the ExECT (extraction of epilepsy clinical text) system.

BMJ open Apr 1, 2019
OBJECTIVE: Routinely collected healthcare data are a powerful research resource but often lack detailed disease-specific information that is collected in clinical free text, for example, clinic letters. We aim to use natural language processing techn...
Humans Algorithms Epilepsy Seizures Information Storage and Retrieval Medical Records Wales Electronic Health Records Magnetic Resonance Imaging Natural Language Processing Electroencephalography
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Use of machine learning in predicting clinical response to transcranial magnetic stimulation in comorbid posttraumatic stress disorder and major depression: A resting state electroencephalography study.

Journal of affective disorders Mar 30, 2019
BACKGROUND: Repetitive transcranial magnetic stimulation (TMS) is clinically effective for major depressive disorder (MDD) and investigational for other conditions including posttraumatic stress disorder (PTSD). Understanding the mechanisms of TMS ac...
Prefrontal Cortex Stress Disorders, Post-Traumatic Treatment Outcome Support Vector Machine Predictive Value of Tests Electroencephalography Self Report Transcranial Magnetic Stimulation Male Middle Aged Depressive Disorder, Major Comorbidity Humans Rhode Island Machine Learning Female Sensitivity and Specificity
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Information Theoretic Feature Transformation Learning for Brain Interfaces.

IEEE transactions on bio-medical engineering Mar 28, 2019
OBJECTIVE: A variety of pattern analysis techniques for model training in brain interfaces exploit neural feature dimensionality reduction based on feature ranking and selection heuristics. In the light of broad evidence demonstrating the potential s...
Brain-Computer Interfaces Imagination Humans Adult Machine Learning Male Electroencephalography Algorithms Signal Processing, Computer-Assisted Brain Female Young Adult
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Multisource Transfer Learning for Cross-Subject EEG Emotion Recognition.

IEEE transactions on cybernetics Mar 27, 2019
Electroencephalogram (EEG) has been widely used in emotion recognition due to its high temporal resolution and reliability. Since the individual differences of EEG are large, the emotion recognition models could not be shared across persons, and we n...
Pattern Recognition, Automated Brain-Computer Interfaces Young Adult Adult Emotions Humans Electroencephalography Machine Learning Brain
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A deep learning-based decision support system for diagnosis of OSAS using PTT signals.

Medical hypotheses Mar 27, 2019
Sleep disorders, which negatively affect an individual's daily quality of life, are a common problem for most of society. The most dangerous sleep disorder is obstructive sleep apnea syndrome (OSAS), which manifests itself during sleep and can cause ...
Electrocardiography Support Vector Machine Humans Electroencephalography Algorithms Healthy Volunteers Electromyography Neural Networks, Computer Male Signal Processing, Computer-Assisted Female Motivation Sleep Apnea, Obstructive Aged Middle Aged Medical Informatics Carbon Dioxide Deep Learning Sleep Wake Disorders Adult Heart Rate Decision Support Systems, Clinical
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Latent Phase Detection of Hypoxic-Ischemic Spike Transients in the EEG of Preterm Fetal Sheep Using Reverse Biorthogonal Wavelets & Fuzzy Classifier.

International journal of neural systems Mar 26, 2019
Hypoxic-ischemic (HI) studies in preterms lack reliable prognostic biomarkers for diagnostic tests of HI encephalopathy (HIE). Our group's observations from fetal sheep models suggest that potential biomarkers of HIE in the form of developing HI mic...
Hypoxia-Ischemia, Brain Electrodes, Implanted Algorithms Signal Processing, Computer-Assisted Animals Fetus Fuzzy Logic Wavelet Analysis Electroencephalography Sheep, Domestic
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EEG characteristics of children with attention-deficit/hyperactivity disorder.

Neuroscience Mar 26, 2019
The electroencephalogram (EEG) is an informative neuroimaging tool for studying attention-deficit/hyperactivity disorder (ADHD); one main goal is to characterize the EEG of children with ADHD. In this study, we employed the power spectrum, complexity...
Brain Female Male Child Electroencephalography Brain Waves Attention Deficit Disorder with Hyperactivity Humans Machine Learning
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