Design of subject independent 3D VAD emotion detection system using EEG signals and machine learning algorithms
This work aims to develop a subject-independent Emotion Detection System (EDS) based
on EEG signals and the 3D Valence-Arousal-Dominance (VAD) model. The DEAP database …
on EEG signals and the 3D Valence-Arousal-Dominance (VAD) model. The DEAP database …
Optimization of preprocessing stage in EEG based BCI systems in terms of accuracy and timing cost
Performance of the motor imagery-based brain computer interface (MI-BCI) systems has
been tried to improve by the researchers with novel approaches and methods used on …
been tried to improve by the researchers with novel approaches and methods used on …
Determination of effective signal processing stages for brain computer interface on BCI competition IV data set 2b: a review study
Considering the entire BCI system, a big challenge is that information can be extracted from
brain signals in a meaningful way. Therefore, most BCI studies are focused on brain signal …
brain signals in a meaningful way. Therefore, most BCI studies are focused on brain signal …
CSP-Ph-PS: Learning CSP-phase space and Poincare sections based on evolutionary algorithm for EEG signals recognition
Abstract Background Brain-Computer Interface (BCI) based on Motor Imagery (MI) is one of
the emerging technology that has been used in smart healthcare applications that help …
the emerging technology that has been used in smart healthcare applications that help …
A novel OpenBCI framework for EEG-based neurophysiological experiments
YN Cardona-Álvarez, AM Álvarez-Meza… - Sensors, 2023 - mdpi.com
An Open Brain–Computer Interface (OpenBCI) provides unparalleled freedom and flexibility
through open-source hardware and firmware at a low-cost implementation. It exploits robust …
through open-source hardware and firmware at a low-cost implementation. It exploits robust …
A multi-classification algorithm based on multi-domain information fusion for motor imagery BCI
J Wang, W Chen, M Li - Biomedical Signal Processing and Control, 2023 - Elsevier
The current problem of motor imagery Electroencephalogram (EEG) signal classification is
low classification accuracy and fixed EEG channel selection. We proposed a novel …
low classification accuracy and fixed EEG channel selection. We proposed a novel …
Classification of lower limb motor imagery based on iterative EEG source localization and feature fusion
X Peng, J Liu, Y Huang, Y Mao, D Li - Neural Computing and Applications, 2023 - Springer
Motor imagery (MI) brain–computer interface (BCI) systems have broad application
prospects in rehabilitation and other fields. However, to achieve accurate and practical MI …
prospects in rehabilitation and other fields. However, to achieve accurate and practical MI …
Multi-Tiered CNN Model for Motor Imagery Analysis: Enhancing UAV Control in Smart City Infrastructure for Industry 5.0
The concept of brain-controlled UAVs, pioneered by researchers at the University of
Minnesota, initiated a series of investigations. These early efforts laid the foundation for …
Minnesota, initiated a series of investigations. These early efforts laid the foundation for …
Motor imagery classification using sparse representations: an exploratory study
The non-stationary nature of the EEG signal poses challenges for the classification of motor
imagery. sparse representation classification (SRC) appears as an alternative for …
imagery. sparse representation classification (SRC) appears as an alternative for …
Premature Ventricular Contractions Detection by Multi-Domain Feature Extraction and Auto-Encoder-based Feature Reduction
Cardiovascular disorders are known to be among the most severe diseases and the leading
causes of mortality all over the globe. Premature ventricular contractions (PVC) are one of …
causes of mortality all over the globe. Premature ventricular contractions (PVC) are one of …