Generative technology for human emotion recognition: A scoping review
Affective computing stands at the forefront of artificial intelligence (AI), seeking to imbue
machines with the ability to comprehend and respond to human emotions. Central to this …
machines with the ability to comprehend and respond to human emotions. Central to this …
Self‐training maximum classifier discrepancy for EEG emotion recognition
Even with an unprecedented breakthrough of deep learning in electroencephalography
(EEG), collecting adequate labelled samples is a critical problem due to laborious and time …
(EEG), collecting adequate labelled samples is a critical problem due to laborious and time …
Role of machine learning and deep learning techniques in EEG-based BCI emotion recognition system: a review
Emotion is a subjective psychophysiological reaction coming from external stimuli which
impacts every aspect of our daily lives. Due to the continuing development of non-invasive …
impacts every aspect of our daily lives. Due to the continuing development of non-invasive …
[HTML][HTML] On the effects of data normalization for domain adaptation on EEG data
Abstract In Machine Learning (ML), a well-known problem is the Dataset Shift problem
where the data in the training and test sets can follow different probability distributions …
where the data in the training and test sets can follow different probability distributions …
A novel EEG-based graph convolution network for depression detection: incorporating secondary subject partitioning and attention mechanism
Z Zhang, Q Meng, LC Jin, H Wang, H Hou - Expert Systems with …, 2024 - Elsevier
Electroencephalography (EEG) is capable of capturing the evocative neural information
within the brain. As a result, it has been increasingly used for identifying neurological …
within the brain. As a result, it has been increasingly used for identifying neurological …
Sect: A method of shifted eeg channel transformer for emotion recognition
Z Bai, F Hou, K Sun, Q Wu, M Zhu… - IEEE Journal of …, 2023 - ieeexplore.ieee.org
Recently, electroencephalographic (EEG) emotion recognition attract attention in the field of
human-computer interaction (HCI). However, most of the existing EEG emotion datasets …
human-computer interaction (HCI). However, most of the existing EEG emotion datasets …
Self-supervised utterance order prediction for emotion recognition in conversations
As the order of the utterances in a conversation changes, the meaning of the utterance also
changes, and sometimes, this will cause different semantics or emotions. However, the …
changes, and sometimes, this will cause different semantics or emotions. However, the …
Semi-supervised dual-stream self-attentive adversarial graph contrastive learning for cross-subject eeg-based emotion recognition
Electroencephalography (EEG) is an objective tool for emotion recognition with promising
applications. However, the scarcity of labeled data remains a major challenge in this field …
applications. However, the scarcity of labeled data remains a major challenge in this field …
EEGMatch: Learning With Incomplete Labels for Semisupervised EEG-Based Cross-Subject Emotion Recognition
Electroencephalography (EEG) is an objective tool for emotion recognition and shows
promising performance. However, the label scarcity problem is a main challenge in this field …
promising performance. However, the label scarcity problem is a main challenge in this field …
Employment of domain adaptation techniques in SSVEP-based brain–computer interfaces
This work addresses the employment of Machine Learning (ML) and Domain Adaptation
(DA) in the framework of Brain-Computer Interfaces (BCIs) based on Steady-State Visually …
(DA) in the framework of Brain-Computer Interfaces (BCIs) based on Steady-State Visually …