[HTML][HTML] Employing PCA and t-statistical approach for feature extraction and classification of emotion from multichannel EEG signal
To achieve a highly efficient brain-computer interface (BCI) system regarding emotion
recognition from electroencephalogram (EEG) signal, the most crucial issues are feature …
recognition from electroencephalogram (EEG) signal, the most crucial issues are feature …
Multiclass EEG signal classification utilizing Rényi min-entropy-based feature selection from wavelet packet transformation
This paper proposes a novel feature selection method utilizing Rényi min-entropy-based
algorithm for achieving a highly efficient brain–computer interface (BCI). Usually, wavelet …
algorithm for achieving a highly efficient brain–computer interface (BCI). Usually, wavelet …
[HTML][HTML] Emotion recognition from EEG-based relative power spectral topography using convolutional neural network
Emotion recognition, a challenging computational issue, finds interesting applications in
diverse fields. Usually, feature-based machine-learning methods have been used for …
diverse fields. Usually, feature-based machine-learning methods have been used for …
Electroencephalogram-based cognitive load level classification using wavelet decomposition and support vector machine
Cognitive load level identification is an interesting challenge in the field of brain-computer-
interface. The sole objective of this work is to classify different cognitive load levels from …
interface. The sole objective of this work is to classify different cognitive load levels from …
Modeling and classification of voluntary and imagery movements for brain–computer interface from fNIR and EEG signals through convolutional neural network
Practical brain–computer interface (BCI) demands the learning-based adaptive model that
can handle diverse problems. To implement a BCI, usually functional near-infrared …
can handle diverse problems. To implement a BCI, usually functional near-infrared …
Effects of stimulating frequency of NIR LEDs light irradiation on forehead as quantified by EEG measurements
L Yao, Z Qian, Y Liu, Z Fang, W Li… - Journal of Innovative …, 2021 - World Scientific
Near-infrared (NIR) light has been shown to produce a range of physiological effects in
humans, however, there is still no agreement on whether and how a single parameter, like …
humans, however, there is still no agreement on whether and how a single parameter, like …
Machine Learning-Based Stress Level Detection from EEG Signals
Recent statistical studies indicate an increase in mental stress in human beings around the
world. Due to the recent pandemic and the subsequent lockdowns, people are suffering from …
world. Due to the recent pandemic and the subsequent lockdowns, people are suffering from …
A Supervised Information Enhanced Multi-Granularity Contrastive Learning Framework for EEG Based Emotion Recognition
This study introduces a novel Supervised Info-enhanced Contrastive Learning framework for
EEG based Emotion Recognition (SI-CLEER). SI-CLEER employs multi-granularity …
EEG based Emotion Recognition (SI-CLEER). SI-CLEER employs multi-granularity …
EEG based Parkinson Detection through Supervised Information Enhanced Contrastive Learning
J Song, X Li, W Jiang, C Wang, Z Zhao… - … on Bioinformatics and …, 2023 - ieeexplore.ieee.org
This study presents a novel Supervised Information Enhanced Contrastive Learning
Algorithm for Parkinson's Disease Detection (SI-CLAPD) based on Electroencephalography …
Algorithm for Parkinson's Disease Detection (SI-CLAPD) based on Electroencephalography …
Towards the effective intrinsic mode functions for motor imagery EEG signal classification
To better utilize one of the most powerful signal decomposition methods called Empirical
Mode Decomposition (EMD) in the field of a brain-computer interface, a better …
Mode Decomposition (EMD) in the field of a brain-computer interface, a better …