Evolutionary computation algorithms for feature selection of EEG-based emotion recognition using mobile sensors
There is currently no standard or widely accepted subset of features to effectively classify
different emotions based on electroencephalogram (EEG) signals. While combining all …
different emotions based on electroencephalogram (EEG) signals. While combining all …
[PDF][PDF] Analysis of EEG signals using nonlinear dynamics and chaos: a review
G Rodriguez-Bermudez… - Applied mathematics …, 2015 - naturalspublishing.com
Nonlinear dynamics and chaos theory have been used in neurophysiology with the aim to
understand the complex brain activity from electroencephalographic (EEG) signals …
understand the complex brain activity from electroencephalographic (EEG) signals …
A high-speed brain speller using steady-state visual evoked potentials
Implementing a complex spelling program using a steady-state visual evoked potential
(SSVEP)-based brain–computer interface (BCI) remains a challenge due to difficulties in …
(SSVEP)-based brain–computer interface (BCI) remains a challenge due to difficulties in …
Computer-aided diagnosis of depression using EEG signals
The complex, nonlinear and non-stationary electroencephalogram (EEG) signals are very
tedious to interpret visually and highly difficult to extract the significant features from them …
tedious to interpret visually and highly difficult to extract the significant features from them …
Towards correlation-based time window selection method for motor imagery BCIs
The start of the cue is often used to initiate the feature window used to control motor imagery
(MI)-based brain-computer interface (BCI) systems. However, the time latency during an MI …
(MI)-based brain-computer interface (BCI) systems. However, the time latency during an MI …
Long short term memory hyperparameter optimization for a neural network based emotion recognition framework
Recently, emotion recognition using low-cost wearable sensors based on
electroencephalogram and blood volume pulse has received much attention. Long short …
electroencephalogram and blood volume pulse has received much attention. Long short …
Deep convolution generative adversarial network-based electroencephalogram data augmentation for post-stroke rehabilitation with motor imagery
The motor imagery brain–computer interface (MI-BCI) system is currently one of the most
advanced rehabilitation technologies, and it can be used to restore the motor function of …
advanced rehabilitation technologies, and it can be used to restore the motor function of …
A P300 brain–computer interface based on a modification of the mismatch negativity paradigm
The P300-based brain–computer interface (BCI) is an extension of the oddball paradigm,
and can facilitate communication for people with severe neuromuscular disorders. It has …
and can facilitate communication for people with severe neuromuscular disorders. It has …
Nonlinear dynamics measures for automated EEG-based sleep stage detection
Background: The brain's continuous neural activity during sleep can be monitored by
electroencephalogram (EEG) signals. The EEG wave pattern and frequency vary during five …
electroencephalogram (EEG) signals. The EEG wave pattern and frequency vary during five …
The dynamic brain networks of motor imagery: time-varying causality analysis of scalp EEG
Motor imagery (MI) requires subjects to visualize the requested motor behaviors, which
involves a large-scale network that spans multiple brain areas. The corresponding cortical …
involves a large-scale network that spans multiple brain areas. The corresponding cortical …