AIMC Topic: Electroencephalography
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nCREANN: Nonlinear Causal Relationship Estimation by Artificial Neural Network; Applied for Autism Connectivity Study.
IEEE transactions on medical imaging
May 13, 2019
Quantifying causal (effective) interactions between different brain regions are very important in neuroscience research. Many conventional methods estimate effective connectivity based on linear models. However, using linear connectivity models may o...
Emotion Recognition from Multiband EEG Signals Using CapsNet.
Sensors (Basel, Switzerland)
May 13, 2019
Emotion recognition based on multi-channel electroencephalograph (EEG) signals is becoming increasingly attractive. However, the conventional methods ignore the spatial characteristics of EEG signals, which also contain salient information related to...
A Channel-Projection Mixed-Scale Convolutional Neural Network for Motor Imagery EEG Decoding.
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
May 8, 2019
Motor imagery electroencephalography (EEG) decoding is an essential part of brain-computer interfaces (BCIs) which help motor-disabled patients to communicate with the outside world by external devices. Recently, deep learning algorithms using decomp...
A Convolutional Neural Network for the Detection of Asynchronous Steady State Motion Visual Evoked Potential.
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
May 7, 2019
A key issue in brain-computer interface (BCI) is the detection of intentional control (IC) states and non-intentional control (NC) states in an asynchronous manner. Furthermore, for steady-state visual evoked potential (SSVEP) BCI systems, multiple s...
SleepEEGNet: Automated sleep stage scoring with sequence to sequence deep learning approach.
PloS one
May 7, 2019
Electroencephalogram (EEG) is a common base signal used to monitor brain activities and diagnose sleep disorders. Manual sleep stage scoring is a time-consuming task for sleep experts and is limited by inter-rater reliability. In this paper, we propo...
Adaptive neural network classifier for decoding MEG signals.
NeuroImage
May 4, 2019
We introduce two Convolutional Neural Network (CNN) classifiers optimized for inferring brain states from magnetoencephalographic (MEG) measurements. Network design follows a generative model of the electromagnetic (EEG and MEG) brain signals allowin...
A review of automated sleep stage scoring based on physiological signals for the new millennia.
Computer methods and programs in biomedicine
May 2, 2019
BACKGROUND AND OBJECTIVE: Sleep is an important part of our life. That importance is highlighted by the multitude of health problems which result from sleep disorders. Detecting these sleep disorders requires an accurate interpretation of physiologic...
Transfer Learning for Brain-Computer Interfaces: A Euclidean Space Data Alignment Approach.
IEEE transactions on bio-medical engineering
Apr 29, 2019
OBJECTIVE: This paper targets a major challenge in developing practical electroencephalogram (EEG)-based brain-computer interfaces (BCIs): how to cope with individual differences so that better learning performance can be obtained for a new subject, ...
Assessing cognitive mental workload via EEG signals and an ensemble deep learning classifier based on denoising autoencoders.
Computers in biology and medicine
Apr 29, 2019
To estimate the reliability and cognitive states of operator performance in a human-machine collaborative environment, we propose a novel human mental workload (MW) recognizer based on deep learning principles and utilizing the features of the electr...
Spectral and Temporal Feature Learning With Two-Stream Neural Networks for Mental Workload Assessment.
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Apr 26, 2019
People's mental workload profoundly affects their work efficiency and health. Mental workload assessment can be used to effectively avoid serious accidents caused by excessive mental workload. Both electroencephalogram (EEG) spectral features and its...
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