AIMC Topic: Brain Waves
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Time-Frequency Analysis of Scalp EEG With Hilbert-Huang Transform and Deep Learning.
IEEE journal of biomedical and health informatics
Apr 14, 2022
Electroencephalography (EEG) is a brain imaging approach that has been widely used in neuroscience and clinical settings. The conventional EEG analyses usually require pre-defined frequency bands when characterizing neural oscillations and extracting...
A neuromorphic spiking neural network detects epileptic high frequency oscillations in the scalp EEG.
Scientific reports
Feb 2, 2022
Interictal High Frequency Oscillations (HFO) are measurable in scalp EEG. This development has aroused interest in investigating their potential as biomarkers of epileptogenesis, seizure propensity, disease severity, and treatment response. The deman...
Predictive Value of Tests
Electroencephalography
Signal Processing, Computer-Assisted
Artifacts
Humans
Brain Waves
Neural Networks, Computer
Time Factors
Brain
Child, Preschool
Reproducibility of Results
Female
Child
Drug Resistant Epilepsy
Male
Treatment Outcome
Epilepsies, Partial
Infant
Adolescent
Wearable Electronic Devices
Interaction of Indirect and Hyperdirect Pathways on Synchrony and Tremor-Related Oscillation in the Basal Ganglia.
Neural plasticity
Mar 13, 2021
Low-frequency oscillatory activity (3-9 Hz) and increased synchrony in the basal ganglia (BG) are recognized to be crucial for Parkinsonian tremor. However, the dynamical mechanism underlying the tremor-related oscillations still remains unknown. In ...
Objective characterization of hip pain levels during walking by combining quantitative electroencephalography with machine learning.
Scientific reports
Feb 4, 2021
Pain is an undesirable sensory experience that can induce depression and limit individuals' activities of daily living, in turn negatively impacting the labor force. Affected people frequently feel pain during activity; however, pain is subjective an...
Pain phenotypes classified by machine learning using electroencephalography features.
NeuroImage
Aug 29, 2020
Pain is a multidimensional experience mediated by distributed neural networks in the brain. To study this phenomenon, EEGs were collected from 20 subjects with chronic lumbar radiculopathy, 20 age and gender matched healthy subjects, and 17 subjects ...
Decoding attention control and selection in visual spatial attention.
Human brain mapping
Jun 16, 2020
Event-related potentials (ERPs) are used extensively to investigate the neural mechanisms of attention control and selection. The univariate ERP approach, however, has left important questions inadequately answered. We addressed two questions by appl...
Predictive regression modeling with MEG/EEG: from source power to signals and cognitive states.
NeuroImage
May 18, 2020
Predicting biomedical outcomes from Magnetoencephalography and Electroencephalography (M/EEG) is central to applications like decoding, brain-computer-interfaces (BCI) or biomarker development and is facilitated by supervised machine learning. Yet, m...
EEG Signal and Feature Interaction Modeling-Based Eye Behavior Prediction Research.
Computational and mathematical methods in medicine
May 16, 2020
In recent years, with the development of brain science and biomedical engineering, as well as the rapid development of electroencephalogram (EEG) signal analysis methods, using EEG signals to monitor human health has become a very popular research fi...
Applying deep learning to single-trial EEG data provides evidence for complementary theories on action control.
Communications biology
Mar 9, 2020
Efficient action control is indispensable for goal-directed behaviour. Different theories have stressed the importance of either attention or response selection sub-processes for action control. Yet, it is unclear to what extent these processes can b...
A deep CNN approach to decode motor preparation of upper limbs from time-frequency maps of EEG signals at source level.
Neural networks : the official journal of the International Neural Network Society
Jan 31, 2020
A system that can detect the intention to move and decode the planned movement could help all those subjects that can plan motion but are unable to implement it. In this paper, motor planning activity is investigated by using electroencephalographic ...
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