Deep learning for electroencephalogram (EEG) classification tasks: a review

A Craik, Y He, JL Contreras-Vidal - Journal of neural engineering, 2019 - iopscience.iop.org
Objective. Electroencephalography (EEG) analysis has been an important tool in
neuroscience with applications in neuroscience, neural engineering (eg Brain–computer …

Neural decoding of EEG signals with machine learning: a systematic review

M Saeidi, W Karwowski, FV Farahani, K Fiok, R Taiar… - Brain Sciences, 2021 - mdpi.com
Electroencephalography (EEG) is a non-invasive technique used to record the brain's
evoked and induced electrical activity from the scalp. Artificial intelligence, particularly …

A review of emotion recognition using physiological signals

L Shu, J Xie, M Yang, Z Li, Z Li, D Liao, X Xu, X Yang - Sensors, 2018 - mdpi.com
Emotion recognition based on physiological signals has been a hot topic and applied in
many areas such as safe driving, health care and social security. In this paper, we present a …

A review, current challenges, and future possibilities on emotion recognition using machine learning and physiological signals

PJ Bota, C Wang, ALN Fred, HP Da Silva - IEEE access, 2019 - ieeexplore.ieee.org
The seminal work on Affective Computing in 1995 by Picard set the base for computing that
relates to, arises from, or influences emotions. Affective computing is a multidisciplinary field …

Multi-channel EEG emotion recognition based on parallel transformer and 3D-convolutional neural network

J Sun, X Wang, K Zhao, S Hao, T Wang - Mathematics, 2022 - mdpi.com
Due to its covert and real-time properties, electroencephalography (EEG) has long been the
medium of choice for emotion identification research. Currently, EEG-based emotion …

Beyond mobile apps: a survey of technologies for mental well-being

K Woodward, E Kanjo, DJ Brown… - IEEE Transactions …, 2020 - ieeexplore.ieee.org
Mental health problems are on the rise globally and strain national health systems
worldwide. Mental disorders are closely associated with fear of stigma, structural barriers …

Optimization of deep architectures for eeg signal classification: An automl approach using evolutionary algorithms

D Aquino-Brítez, A Ortiz, J Ortega, J León, M Formoso… - Sensors, 2021 - mdpi.com
Electroencephalography (EEG) signal classification is a challenging task due to the low
signal-to-noise ratio and the usual presence of artifacts from different sources. Different …

Handling missing sensors in topology-aware iot applications with gated graph neural network

S Liu, S Yao, Y Huang, D Liu, H Shao, Y Zhao… - Proceedings of the …, 2020 - dl.acm.org
Reliable data collection, transmission, and delivery on Internet of Things (IoT) systems is
crucial in order to provide high-quality intelligent services. However, sensor data delivery …

A Survey of Cutting-edge Multimodal Sentiment Analysis

U Singh, K Abhishek, HK Azad - ACM Computing Surveys, 2024 - dl.acm.org
The rapid growth of the internet has reached the fourth generation, ie, web 4.0, which
supports Sentiment Analysis (SA) in many applications such as social media, marketing, risk …

Hemispheric asymmetry of functional brain networks under different emotions using EEG data

R Cao, H Shi, X Wang, S Huo, Y Hao, B Wang, H Guo… - Entropy, 2020 - mdpi.com
Despite many studies reporting hemispheric asymmetry in the representation and
processing of emotions, the essence of the asymmetry remains controversial. Brain network …