AIMC Topic: Electroencephalography

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Machine learning and wearable devices of the future.

Epilepsia Jul 26, 2020
Machine learning (ML) is increasingly recognized as a useful tool in healthcare applications, including epilepsy. One of the most important applications of ML in epilepsy is seizure detection and prediction, using wearable devices (WDs). However, not...
Electroencephalography Seizures Humans Forecasting Wearable Electronic Devices Machine Learning
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The Effect of the Graphic Structures of Humanoid Robot on N200 and P300 Potentials.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society Jul 20, 2020
Humanoid robots are widely used in brain computer interface (BCI). Using a humanoid robot stimulus could increase the amplitude of event-related potentials (ERPs), which improves BCI performance. Since a humanoid robot contains many human elements, t...
Event-Related Potentials, P300 Robotics Electroencephalography Photic Stimulation Evoked Potentials Brain-Computer Interfaces Humans Brain
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Spatial-Frequency Feature Learning and Classification of Motor Imagery EEG Based on Deep Convolution Neural Network.

Computational and mathematical methods in medicine Jul 20, 2020
EEG pattern recognition is an important part of motor imagery- (MI-) based brain computer interface (BCI) system. Traditional EEG pattern recognition algorithm usually includes two steps, namely, feature extraction and feature classification. In feat...
Mathematical Concepts Imagination Models, Neurological Algorithms Electroencephalography Humans Brain-Computer Interfaces Databases, Factual Pattern Recognition, Automated Neural Networks, Computer Deep Learning Motor Cortex
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Multi-class motor imagery EEG classification using collaborative representation-based semi-supervised extreme learning machine.

Medical & biological engineering & computing Jul 16, 2020
Both labeled and unlabeled data have been widely used in electroencephalographic (EEG)-based brain-computer interface (BCI). However, labeled EEG samples are generally scarce and expensive to collect, while unlabeled samples are considered to be abun...
Humans Neural Networks, Computer Algorithms Least-Squares Analysis Electroencephalography Benchmarking Databases, Factual Support Vector Machine Imagination Biomedical Engineering Supervised Machine Learning Brain-Computer Interfaces
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Modeling of Human Operator Behavior for Brain-Actuated Mobile Robots Steering.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society Jul 15, 2020
Human operator control of brain-actuated robot steering based on electroencephalograph (EEG)-signals is a complex behavior consisting of surroundings perceiving, decision making, and commands issuing and differs among individual operators. However, n...
Brain-Computer Interfaces Brain Robotics Electroencephalography Humans
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Convolutional neural networks and genetic algorithm for visual imagery classification.

Physical and engineering sciences in medicine Jul 13, 2020
Brain-Computer Interface (BCI) systems establish a channel for direct communication between the brain and the outside world without having to use the peripheral nervous system. While most BCI systems use evoked potentials and motor imagery, in the pr...
Signal Processing, Computer-Assisted Electrodes Imagery, Psychotherapy Male Electroencephalography Neural Networks, Computer Algorithms Female Reproducibility of Results Humans Adult
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Deep learning-based BCI for gait decoding from EEG with LSTM recurrent neural network.

Journal of neural engineering Jul 13, 2020
OBJECTIVE: Mobile Brain/Body Imaging (MoBI) frameworks allowed the research community to find evidence of cortical involvement at walking initiation and during locomotion. However, the decoding of gait patterns from brain signals remains an open chal...
Electroencephalography Gait Brain-Computer Interfaces Humans Neural Networks, Computer Deep Learning
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Brain-Computer Interface-Based Humanoid Control: A Review.

Sensors (Basel, Switzerland) Jun 27, 2020
A Brain-Computer Interface (BCI) acts as a communication mechanism using brain signals to control external devices. The generation of such signals is sometimes independent of the nervous system, such as in Passive BCI. This is majorly beneficial for ...
Humans Robotics Machine Learning Algorithms Electroencephalography Brain Brain-Computer Interfaces
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Dynamical system based compact deep hybrid network for classification of Parkinson disease related EEG signals.

Neural networks : the official journal of the International Neural Network Society Jun 25, 2020
Electroencephalogram (EEG) signals accumulate the brain's spiking activities using standardized electrodes placed at the scalp. These cumulative brain signals are chaotic in nature and vary depending upon current physical and/or mental activities. Th...
Humans Neural Networks, Computer Brain Nonlinear Dynamics Parkinson Disease Electroencephalography
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Neonatal EEG sleep stage classification based on deep learning and HMM.

Journal of neural engineering Jun 25, 2020
OBJECTIVE: Automatic sleep stage scoring is of great importance for investigating sleep architecture during infancy. In this work, we introduce a novel multichannel approach based on deep learning networks and hidden Markov models (HMM) to improve th...
Humans Infant, Newborn Deep Learning Electroencephalography Infant Signal Processing, Computer-Assisted Sleep Stages Sleep
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