EEG-based brain-computer interfaces using motor-imagery: Techniques and challenges
Electroencephalography (EEG)-based brain-computer interfaces (BCIs), particularly those
using motor-imagery (MI) data, have the potential to become groundbreaking technologies …
using motor-imagery (MI) data, have the potential to become groundbreaking technologies …
A recent investigation on detection and classification of epileptic seizure techniques using EEG signal
The benefits of early detection and classification of epileptic seizures in analysis, monitoring
and diagnosis for the realization and actualization of computer-aided devices and recent …
and diagnosis for the realization and actualization of computer-aided devices and recent …
Comparison of signal decomposition methods in classification of EEG signals for motor-imagery BCI system
In this study, three popular signal processing techniques (Empirical Mode Decomposition,
Discrete Wavelet Transform, and Wavelet Packet Decomposition) were investigated for the …
Discrete Wavelet Transform, and Wavelet Packet Decomposition) were investigated for the …
Review of challenges associated with the EEG artifact removal methods
Electroencephalography (EEG), as a non-invasive modality, enables the representation of
the underlying neuronal activities as electrical signals with high temporal resolution. In …
the underlying neuronal activities as electrical signals with high temporal resolution. In …
DWT based detection of epileptic seizure from EEG signals using naive Bayes and k-NN classifiers
A Sharmila, P Geethanjali - Ieee Access, 2016 - ieeexplore.ieee.org
Electroencephalogram (EEG) comprises valuable details related to the different
physiological state of the brain. In this paper, a framework is offered for detecting the …
physiological state of the brain. In this paper, a framework is offered for detecting the …
Automatic epileptic seizure detection in EEG signals using multi-domain feature extraction and nonlinear analysis
Epileptic seizure detection is commonly implemented by expert clinicians with visual
observation of electroencephalography (EEG) signals, which tends to be time consuming …
observation of electroencephalography (EEG) signals, which tends to be time consuming …
Classification of epileptic EEG recordings using signal transforms and convolutional neural networks
R San-Segundo, M Gil-Martín… - Computers in biology …, 2019 - Elsevier
This paper describes the analysis of a deep neural network for the classification of epileptic
EEG signals. The deep learning architecture is made up of two convolutional layers for …
EEG signals. The deep learning architecture is made up of two convolutional layers for …
Discrimination and classification of focal and non-focal EEG signals using entropy-based features in the EMD-DWT domain
AB Das, MIH Bhuiyan - biomedical signal processing and control, 2016 - Elsevier
In this paper, a comprehensive analysis of focal and non-focal electroencephalography is
carried out in the empirical mode decomposition and discrete wavelet transform domains. A …
carried out in the empirical mode decomposition and discrete wavelet transform domains. A …
A 2D CNN-LSTM hybrid algorithm using time series segments of EEG data for motor imagery classification
J Wang, S Cheng, J Tian, Y Gao - Biomedical Signal Processing and …, 2023 - Elsevier
Motor imagery-based brain–computer interaction (MI-BCI) converts human neural activity
into computational information, often used as commands, by recognizing …
into computational information, often used as commands, by recognizing …
Modified binary salp swarm algorithm in EEG signal classification for epilepsy seizure detection
Epilepsy is a brain disorder characterized by sudden seizures, periodic abnormal and
inappropriate behaviour, and an altered state of consciousness. The visual diagnosis of …
inappropriate behaviour, and an altered state of consciousness. The visual diagnosis of …