Multi-modal Adversarial Regressive Transformer for Cross-subject Fatigue Detection.

Fatigue driving constitutes a factor to traffic accidents, necessitating the importance of fatigue detection. In this paper, we propose leveraging Electroencephalogram (EEG) and Electrooculogram (EOG) data for fatigue detection, developing the Multi-modal Adversarial Regressive Transformer (MART) model. MART encodes and concatenates multiple modalities into an embedded sequence, processed by Transformer focusing on isolated and mixed modalities.

A Multimodal Myanmar Emotion Dataset for Emotion Recognition.

Effective emotion recognition is vital for human interaction and has an impact on several fields such as psychology, social sciences, human-computer interaction, and emotional artificial intelligence. This study centers on the innovative contribution of a novel Myanmar emotion dataset to enhance emotion recognition technology in diverse cultural contexts. Our unique dataset is derived from a carefully designed emotion elicitation paradigm, using 15 video clips per session for three emotions (positive, neutral, and negative), with five clips per emotion.

Contrastive Self-supervised EEG Representation Learning for Emotion Classification.

Self-supervised learning provides an effective approach to leverage a large amount of unlabeled data. Numerous previous studies have indicated that applying self-supervision to physiological signals can yield better representations of the signals. In the paper, we aim to apply this method to the crucial field of emotion recognition.