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Brain Waves

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EEG Signal and Feature Interaction Modeling-Based Eye Behavior Prediction Research.

Computational and mathematical methods in medicine
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...

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
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 ...

Predictive regression modeling with MEG/EEG: from source power to signals and cognitive states.

NeuroImage
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...

Decoding attention control and selection in visual spatial attention.

Human brain mapping
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...

Pain phenotypes classified by machine learning using electroencephalography features.

NeuroImage
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 ...

Interaction of Indirect and Hyperdirect Pathways on Synchrony and Tremor-Related Oscillation in the Basal Ganglia.

Neural plasticity
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
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...

A neuromorphic spiking neural network detects epileptic high frequency oscillations in the scalp EEG.

Scientific reports
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...

Time-Frequency Analysis of Scalp EEG With Hilbert-Huang Transform and Deep Learning.

IEEE journal of biomedical and health informatics
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...

LightSeizureNet: A Lightweight Deep Learning Model for Real-Time Epileptic Seizure Detection.

IEEE journal of biomedical and health informatics
The monitoring of epilepsy patients in non-hospital environment is highly desirable, where ultra-low power wearable seizure detection devices are essential in such a system. The state-of-the-art epileptic seizure detection algorithms targeting such d...