[HTML][HTML] Employing PCA and t-statistical approach for feature extraction and classification of emotion from multichannel EEG signal

MA Rahman, MF Hossain, M Hossain… - Egyptian Informatics …, 2020 - Elsevier
To achieve a highly efficient brain-computer interface (BCI) system regarding emotion
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

MA Rahman, F Khanam, M Ahmad, MS Uddin - Brain informatics, 2020 - Springer
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 …

[HTML][HTML] Emotion recognition from EEG-based relative power spectral topography using convolutional neural network

MA Rahman, A Anjum, MMH Milu, F Khanam… - Array, 2021 - Elsevier
Emotion recognition, a challenging computational issue, finds interesting applications in
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

F Khanam, ABMA Hossain, M Ahmad - Brain-Computer Interfaces, 2023 - Taylor & Francis
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 …

Modeling and classification of voluntary and imagery movements for brain–computer interface from fNIR and EEG signals through convolutional neural network

MA Rahman, MS Uddin, M Ahmad - Health Information Science and …, 2019 - Springer
Practical brain–computer interface (BCI) demands the learning-based adaptive model that
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 …

Machine Learning-Based Stress Level Detection from EEG Signals

A Nirabi, F Abd Rahman, MH Habaebi… - 2021 IEEE 7th …, 2021 - ieeexplore.ieee.org
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 …

A Supervised Information Enhanced Multi-Granularity Contrastive Learning Framework for EEG Based Emotion Recognition

X Li, J Song, Z Zhao, C Wang… - ICASSP 2024-2024 …, 2024 - ieeexplore.ieee.org
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 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 …

Towards the effective intrinsic mode functions for motor imagery EEG signal classification

T Nazneen, MA Rahman… - … Conference on Electrical …, 2019 - ieeexplore.ieee.org
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 …