Deep learning for electroencephalogram (EEG) classification tasks: a review
Objective. Electroencephalography (EEG) analysis has been an important tool in
neuroscience with applications in neuroscience, neural engineering (eg Brain–computer …
neuroscience with applications in neuroscience, neural engineering (eg Brain–computer …
Neural decoding of EEG signals with machine learning: a systematic review
Electroencephalography (EEG) is a non-invasive technique used to record the brain's
evoked and induced electrical activity from the scalp. Artificial intelligence, particularly …
evoked and induced electrical activity from the scalp. Artificial intelligence, particularly …
A review of emotion recognition using physiological signals
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 …
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
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 …
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 …
medium of choice for emotion identification research. Currently, EEG-based emotion …
Beyond mobile apps: a survey of technologies for mental well-being
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 …
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
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 …
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
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 …
crucial in order to provide high-quality intelligent services. However, sensor data delivery …
A Survey of Cutting-edge Multimodal Sentiment Analysis
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 …
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
Despite many studies reporting hemispheric asymmetry in the representation and
processing of emotions, the essence of the asymmetry remains controversial. Brain network …
processing of emotions, the essence of the asymmetry remains controversial. Brain network …