AIMC Topic: Electroencephalography
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An Investigation of Various Machine and Deep Learning Techniques Applied in Automatic Fear Level Detection and Acrophobia Virtual Therapy.
Sensors (Basel, Switzerland)
Jan 15, 2020
In this paper, we investigate various machine learning classifiers used in our Virtual Reality (VR) system for treating acrophobia. The system automatically estimates fear level based on multimodal sensory data and a self-reported emotion assessment....
A convolutional neural network outperforming state-of-the-art sleep staging algorithms for both preterm and term infants.
Journal of neural engineering
Jan 14, 2020
OBJECTIVE: To classify sleep states using electroencephalogram (EEG) that reliably works over a wide range of preterm ages, as well as term age.
EEG-Controlled Wall-Crawling Cleaning Robot Using SSVEP-Based Brain-Computer Interface.
Journal of healthcare engineering
Jan 11, 2020
The assistive, adaptive, and rehabilitative applications of EEG-based robot control and navigation are undergoing a major transformation in dimension as well as scope. Under the background of artificial intelligence, medical and nonmedical robots hav...
Comparison of different input modalities and network structures for deep learning-based seizure detection.
Scientific reports
Jan 10, 2020
The manual review of an electroencephalogram (EEG) for seizure detection is a laborious and error-prone process. Thus, automated seizure detection based on machine learning has been studied for decades. Recently, deep learning has been adopted in ord...
Neuronal population correlates of target selection and distractor filtering.
NeuroImage
Jan 8, 2020
Frontal Eye Field (FEF) neurons discriminate between relevant and irrelevant visual stimuli and their response magnitude predicts conscious perception. How this is reflected in the spatial representation of a visual stimulus at the neuronal populatio...
Constructing a Consciousness Meter Based on the Combination of Non-Linear Measurements and Genetic Algorithm-Based Support Vector Machine.
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Jan 8, 2020
OBJECTIVE: Constructing a framework to evaluate consciousness is an important issue in neuroscience research and clinical practice. However, there is still no systematic framework for quantifying altered consciousness along the dimensions of both lev...
Adaptive feature extraction in EEG-based motor imagery BCI: tracking mental fatigue.
Journal of neural engineering
Jan 6, 2020
OBJECTIVE: Electroencephalogram (EEG) signals are non-stationary. This could be due to internal fluctuation of brain states such as fatigue, frustration, etc. This necessitates the development of adaptive brain-computer interfaces (BCI) whose perform...
HS-CNN: a CNN with hybrid convolution scale for EEG motor imagery classification.
Journal of neural engineering
Jan 6, 2020
OBJECTIVE: Electroencephalography (EEG) motor imagery classification has been widely used in healthcare applications such as mobile assistive robots and post-stroke rehabilitation. Recently, EEG motor imagery classification methods based on convoluti...
Interpreting neural decoding models using grouped model reliance.
PLoS computational biology
Jan 6, 2020
Machine learning algorithms are becoming increasingly popular for decoding psychological constructs based on neural data. However, as a step towards bridging the gap between theory-driven cognitive neuroscience and data-driven decoding approaches, th...
Use of Machine Learning for Predicting Escitalopram Treatment Outcome From Electroencephalography Recordings in Adult Patients With Depression.
JAMA network open
Jan 3, 2020
IMPORTANCE: Social and economic costs of depression are exacerbated by prolonged periods spent identifying treatments that would be effective for a particular patient. Thus, a tool that reliably predicts an individual patient's response to treatment ...
Biomarkers
Prognosis
Female
Reproducibility of Results
Humans
Machine Learning
Depressive Disorder, Major
Canada
Treatment Outcome
Support Vector Machine
Adult
Sensitivity and Specificity
Male
Middle Aged
Antidepressive Agents, Second-Generation
Predictive Value of Tests
Electroencephalography
Citalopram
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