This work presents an explainable affective body expression recognition framework that integrates multi-scale spatiotemporal encoding with LLM-based reasoning. The framework uses MSCMNet to encode body movement patterns across scales, bidirectional …
Affective brain-computer interfaces require models that can integrate EEG information across frequency bands and brain regions while remaining interpretable. This work proposes an explainable multi-frequency and multi-region fusion network (MFMR-FN) …
This work proposes an affective body expression recognition framework that fuses temporal and spatial features from human body movements. The framework combines a body expression energy model, multiscale SPD-based representation learning, and …
Body motion is an important channel for human communication and plays a crucial role in automatic emotion recognition. This work proposes a multiscale spatio-temporal network that captures coarse-grained and fine-grained affective information …
As depression becomes more commonplace in society, the timely and effective detection of the signs of depression for its prevention and early treatment becomes more important. Gait analysis can provide a contactless and low-cost method for depression …
This study investigates whether resting-state EEG signatures can predict the treatment efficacy of transcranial alternating current stimulation (tACS) for refractory auditory hallucinations in schizophrenia. Resting-state EEG data from patients were …
Audio-based depression recognition is a useful auxiliary tool for early screening, but many existing methods focus mainly on speech perception features and overlook vocal-tract changes. This work proposes a machine speech chain model for depression …
Affective computing is important for making computers smarter. When emotion can be quantified, machines can understand it. This study aims to apply affective computing to mental disorders, and to classify healthy people and mentally illnesses. For …
Electroencephalogram (EEG) signals play an important role in the epilepsy detection. In the past decades, the automatic detection system of epilepsy has emerged and performed well. In this paper, a novel sparse representation-based epileptic seizure …