[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 novel deep learning approach for classification of EEG motor imagery signals
Objective. Signal classification is an important issue in brain computer interface (BCI)
systems. Deep learning approaches have been used successfully in many recent studies to …
systems. Deep learning approaches have been used successfully in many recent studies to …
An overview of biometrics methods
Biometrics is becoming an important technology in automated person recognition. With the
help of biometrics, the individuals are recognized through their unique characteristics and …
help of biometrics, the individuals are recognized through their unique characteristics and …
Multi-kernel extreme learning machine for EEG classification in brain-computer interfaces
One of the most important issues for the development of a motor-imagery based brain-
computer interface (BCI) is how to design a powerful classifier with strong generalization …
computer interface (BCI) is how to design a powerful classifier with strong generalization …
A novel deep learning approach with data augmentation to classify motor imagery signals
Z Zhang, F Duan, J Sole-Casals… - IEEE …, 2019 - ieeexplore.ieee.org
Brain-computer interface provides a new communication bridge between the human mind
and devices, depending largely on the accurate classification and identification of non …
and devices, depending largely on the accurate classification and identification of non …
A cross-space CNN with customized characteristics for motor imagery EEG classification
Y Hu, Y Liu, S Zhang, T Zhang, B Dai… - … on Neural Systems …, 2023 - ieeexplore.ieee.org
The classification of motor imagery-electroencephalogram (MI-EEG) based brain-computer
interface (BCI) can be used to decode neurological activities, which has been widely applied …
interface (BCI) can be used to decode neurological activities, which has been widely applied …
Statistically significant features improve binary and multiple Motor Imagery task predictions from EEGs
In recent studies, in the field of Brain-Computer Interface (BCI), researchers have focused on
Motor Imagery tasks. Motor Imagery-based electroencephalogram (EEG) signals provide the …
Motor Imagery tasks. Motor Imagery-based electroencephalogram (EEG) signals provide the …
Optimized bi-objective EEG channel selection and cross-subject generalization with brain–computer interfaces
VS Handiru, VA Prasad - IEEE Transactions on Human …, 2016 - ieeexplore.ieee.org
Electroencephalography (EEG) signal processing to decode motor imagery (MI) involves
high-dimensional features, which increases the computational complexity. To reduce this …
high-dimensional features, which increases the computational complexity. To reduce this …
Brain Computer Interface system based on indoor semi-autonomous navigation and motor imagery for Unmanned Aerial Vehicle control
This paper proposes a non-invasive Electroencephalogram (EEG)-based Brain Computer
Interface (BCI) system to achieve the easy-to-use and stable control of a low speed …
Interface (BCI) system to achieve the easy-to-use and stable control of a low speed …
Brain computer interface: a review
A brain-computer interface (BCI) systems permit encephalic activity to solely control
computers or external devices. Accordingly, people suffering from neuromuscular diseases …
computers or external devices. Accordingly, people suffering from neuromuscular diseases …