In this study, we conducted a systematic literature review of 107 primary studies conducted
between 2017 and 2023 to discern trends in datasets, classifiers, and contributions to …

Emotion recognition based on EEG has been implemented in numerous studies. In most of
them, there are two observations made: first, extensive implementation is negatively …

Multimodal deep learning methods capture synergistic features from multiple modalities and
have the potential to improve accuracy for stress detection compared to unimodal methods …

Facial expressions are one of the most effective and straightforward ways of conveying our
emotions and intentions. Therefore, it is crucial to conduct research aimed at developing a …

Pattern recognition based on network connections has recently been applied to the brain-
computer interface (BCI) research, offering new ideas for emotion recognition using …

Emotion recognition based on electroencephalography (EEG) signals has garnered
substantial attention in recent years and finds extensive applications in the domains of …

Abstract RNA modification, N4-acetylcytidine (ac4C), is enzymatically catalyzed by N-
acetyltransferase 10 (NAT10) and plays an essential role across tRNA, rRNA, and mRNA. It …

Currently, most models rarely consider the negative transfer problem in the research field of
cross-subject EEG emotion recognition. To solve this problem, this paper proposes a semi …

Emotion recognition using EEG is a difficult study because the signals' unstable behavior,
which is brought on by the brain's complex neuronal activity, makes it difficult to extract the …

The objective of EEG-based emotion recognition is to classify emotions by decoding signals,
with potential applications in the fields of artificial intelligence and bioinformatics. Cross …