AIMC Topic: Electroencephalography

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Molecular and DNA Artificial Neural Networks via Fractional Coding.

IEEE transactions on biomedical circuits and systems
This article considers implementation of artificial neural networks (ANNs) using molecular computing and DNA based on fractional coding. Prior work had addressed molecular two-layer ANNs with binary inputs and arbitrary weights. In prior work using f...

Applying deep learning to single-trial EEG data provides evidence for complementary theories on action control.

Communications biology
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...

An empirical comparison of neural networks and machine learning algorithms for EEG gait decoding.

Scientific reports
Previous studies of Brain Computer Interfaces (BCI) based on scalp electroencephalography (EEG) have demonstrated the feasibility of decoding kinematics for lower limb movements during walking. In this computational study, we investigated offline dec...

Latent Phase Identification of High-Frequency Micro-Scale Gamma Spike Transients in the Hypoxic Ischemic EEG of Preterm Fetal Sheep Using Spectral Analysis and Fuzzy Classifiers.

Sensors (Basel, Switzerland)
Premature babies are at high risk of serious neurodevelopmental disabilities, which in many cases are related to perinatal hypoxic-ischemic encephalopathy (HIE). Studies of neuroprotection in animal models consistently suggest that treatment must be ...

SeizureBank: A Repository of Analysis-ready Seizure Signal Data.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Approximately 60 million people worldwide suffer from epileptic seizures. A key challenge in machine learning ap proaches for epilepsy research is the lack of a data resource of analysis-ready (no additional preprocessing is needed when using the dat...

A Residual Based Attention Model for EEG Based Sleep Staging.

IEEE journal of biomedical and health informatics
Sleep staging is to score the sleep state of a subject into different sleep stages such as Wake and Rapid Eye Movement (REM). It plays an indispensable role in the diagnosis and treatment of sleep disorders. As manual sleep staging through well-train...

Automated classification of five seizure onset patterns from intracranial electroencephalogram signals.

Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology
OBJECTIVE: The electroencephalographic (EEG) signals contain information about seizures and their onset location. There are several seizure onset patterns reported in the literature, and these patterns have clinical significance. In this work, we pro...

Constructing large-scale cortical brain networks from scalp EEG with Bayesian nonnegative matrix factorization.

Neural networks : the official journal of the International Neural Network Society
A large-scale network provides a high hierarchical level for understanding the adaptive adjustment of the human brain during cognition processes. Since high spatial resolution is required, most of the related works are based on functional magnetic re...

A-phase classification using convolutional neural networks.

Medical & biological engineering & computing
A series of short events, called A-phases, can be observed in the human electroencephalogram (EEG) during Non-Rapid Eye Movement (NREM) sleep. These events can be classified in three groups (A1, A2, and A3) according to their spectral contents, and a...

Assistance Robotics and Biosensors 2019.

Sensors (Basel, Switzerland)
This Special Issue is focused on breakthrough developments in the field of assistive and rehabilitation robotics. The selected contributions include current scientific progress from biomedical signal processing and cover applications to myoelectric p...