Data augmentation for deep-learning-based electroencephalography
Background Data augmentation (DA) has recently been demonstrated to achieve
considerable performance gains for deep learning (DL)—increased accuracy and stability …
considerable performance gains for deep learning (DL)—increased accuracy and stability …
EEG-based brain-computer interfaces (BCIs): A survey of recent studies on signal sensing technologies and computational intelligence approaches and their …
Brain-Computer interfaces (BCIs) enhance the capability of human brain activities to interact
with the environment. Recent advancements in technology and machine learning algorithms …
with the environment. Recent advancements in technology and machine learning algorithms …
Deep learning in physiological signal data: A survey
Deep Learning (DL), a successful promising approach for discriminative and generative
tasks, has recently proved its high potential in 2D medical imaging analysis; however …
tasks, has recently proved its high potential in 2D medical imaging analysis; however …
EEG data augmentation using Wasserstein GAN
G Bouallegue, R Djemal - 2020 20th International Conference …, 2020 - ieeexplore.ieee.org
Electroencephalogram (EEG) presents a challenge during the classification task using
machine learning and deep learning techniques due to the lack or to the low size of …
machine learning and deep learning techniques due to the lack or to the low size of …
Towards effective classification of brain hemorrhagic and ischemic stroke using CNN
Brain stroke is one of the most leading causes of worldwide death and requires proper
medical treatment. Therefore, in this paper, our aim is to classify brain computed tomography …
medical treatment. Therefore, in this paper, our aim is to classify brain computed tomography …
A major depressive disorder classification framework based on EEG signals using statistical, spectral, wavelet, functional connectivity, and nonlinear analysis
RA Movahed, GP Jahromi, S Shahyad… - Journal of Neuroscience …, 2021 - Elsevier
Background Major depressive disorder (MDD) is a prevalent mental illness that is diagnosed
through questionnaire-based approaches; however, these methods may not lead to an …
through questionnaire-based approaches; however, these methods may not lead to an …
Data augmentation for deep neural networks model in EEG classification task: a review
Classification of electroencephalogram (EEG) is a key approach to measure the rhythmic
oscillations of neural activity, which is one of the core technologies of brain-computer …
oscillations of neural activity, which is one of the core technologies of brain-computer …
Recognizing emotions evoked by music using CNN-LSTM networks on EEG signals
Emotion is considered to be critical for the actual interpretation of actions and relationships.
Recognizing emotions from EEG signals is also becoming an important computer-aided …
Recognizing emotions from EEG signals is also becoming an important computer-aided …
A compact and interpretable convolutional neural network for cross-subject driver drowsiness detection from single-channel EEG
Driver drowsiness is one of the main factors leading to road fatalities and hazards in the
transportation industry. Electroencephalography (EEG) has been considered as one of the …
transportation industry. Electroencephalography (EEG) has been considered as one of the …
A single-channel EEG based automatic sleep stage classification method leveraging deep one-dimensional convolutional neural network and hidden Markov model
B Yang, X Zhu, Y Liu, H Liu - Biomedical Signal Processing and Control, 2021 - Elsevier
Sleep stage classification is an essential process for analyzing sleep and diagnosing sleep
related disorders. Sleep staging by visual inspection of expert is a labor-intensive task and …
related disorders. Sleep staging by visual inspection of expert is a labor-intensive task and …