AIMC Topic: Electroencephalography

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Application of Transfer Learning in EEG Decoding Based on Brain-Computer Interfaces: A Review.

Sensors (Basel, Switzerland) Nov 5, 2020
The algorithms of electroencephalography (EEG) decoding are mainly based on machine learning in current research. One of the main assumptions of machine learning is that training and test data belong to the same feature space and are subject to the s...
Humans Electroencephalography Calibration Brain-Computer Interfaces Machine Learning Algorithms Brain
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Automatic sleep scoring: A deep learning architecture for multi-modality time series.

Journal of neuroscience methods Nov 4, 2020
BACKGROUND: Sleep scoring is an essential but time-consuming process, and therefore automatic sleep scoring is crucial and urgent to help address the growing unmet needs for sleep research. This paper aims to develop a versatile deep-learning archite...
Sleep Stages Electroencephalography Humans Sleep Deep Learning Polysomnography
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Classification of Depression Through Resting-State Electroencephalogram as a Novel Practice in Psychiatry: Review.

Journal of medical Internet research Nov 3, 2020
BACKGROUND: Machine learning applications in health care have increased considerably in the recent past, and this review focuses on an important application in psychiatry related to the detection of depression. Since the advent of computational psych...
Humans Machine Learning Psychiatry Electroencephalography Reproducibility of Results Depression
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A data-driven dimensionality-reduction algorithm for the exploration of patterns in biomedical data.

Nature biomedical engineering Nov 2, 2020
Dimensionality reduction is widely used in the visualization, compression, exploration and classification of data. Yet a generally applicable solution remains unavailable. Here, we report an accurate and broadly applicable data-driven algorithm for d...
Algorithms Tomography, X-Ray Computed Biomedical Research Humans Datasets as Topic Protein Biosynthesis Multifactor Dimensionality Reduction Single-Cell Analysis Sequence Analysis, RNA Electroencephalography
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Sleep stage classification for child patients using DeConvolutional Neural Network.

Artificial intelligence in medicine Nov 2, 2020
Studies from the literature show that the prevalence of sleep disorder in children is far higher than that in adults. Although much research effort has been made on sleep stage classification for adults, children have significantly different characte...
Polysomnography Sleep Stages Electroencephalography Sleep Humans Neural Networks, Computer Aged Child, Preschool Child
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A Novel Method for Sleep-Stage Classification Based on Sonification of Sleep Electroencephalogram Signals Using Wavelet Transform and Recurrent Neural Network.

European neurology Oct 29, 2020
INTRODUCTION: Visual sleep-stage scoring is a time-consuming technique that cannot extract the nonlinear characteristics of electroencephalogram (EEG). This article presents a novel method for sleep-stage differentiation based on sonification of slee...
Music Young Adult Databases, Factual Adult Male Female Electroencephalography Humans Sleep Stages Reproducibility of Results Middle Aged Wavelet Analysis Neural Networks, Computer Adolescent
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Deep Neural Network for Visual Stimulus-Based Reaction Time Estimation Using the Periodogram of Single-Trial EEG.

Sensors (Basel, Switzerland) Oct 27, 2020
Multiplexed deep neural networks (DNN) have engendered high-performance predictive models gaining popularity for decoding brain waves, extensively collected in the form of electroencephalogram (EEG) signals. In this paper, to the best of our knowledg...
Humans Reaction Time Neural Networks, Computer Algorithms Electroencephalography
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Early Emergence of Solid Shape Coding in Natural and Deep Network Vision.

Current biology : CB Oct 22, 2020
Area V4 is the first object-specific processing stage in the ventral visual pathway, just as area MT is the first motion-specific processing stage in the dorsal pathway. For almost 50 years, coding of object shape in V4 has been studied and conceived...
Macaca mulatta Visual Cortex Microscopy, Fluorescence, Multiphoton Electroencephalography Form Perception Deep Learning Electrodes, Implanted Neurons Animals Photic Stimulation Models, Neurological Visual Pathways Male Intravital Microscopy
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EEG-based deep learning model for the automatic detection of clinical depression.

Physical and engineering sciences in medicine Oct 22, 2020
Clinical depression is a neurological disorder that can be identified by analyzing the Electroencephalography (EEG) signals. However, the major drawback in using EEG to accurately identify depression is the complexity and variation that exist in the ...
Depression Electroencephalography Neural Networks, Computer Depressive Disorder, Major Humans Deep Learning
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Predicting memory from study-related brain activity.

Journal of neurophysiology Oct 21, 2020
To isolate brain activity that may reflect effective cognitive processes during the study phase of a memory task, cognitive neuroscientists commonly contrast brain activity during study of later-remembered versus later-forgotten items. This "subseque...
Recognition, Psychology Humans Cerebral Cortex Machine Learning Evoked Potentials Electroencephalography Adult Mental Recall Pattern Recognition, Automated Functional Neuroimaging
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