Multimodal transformer for depression detection based on EEG and interview data
Nima Esmi (University Medical Center Groningen, Khazar University)
Asadollah Shahbahrami (University of Guilan, Khazar University)
G. Gaydadjiev (TU Delft - Computer Engineering)
Peter de Jonge (University Medical Center Groningen)
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Abstract
Depression detection benefits from combining neurological and behavioral indicators, yet integrating heterogeneous modalities such as EEG and interview audio remains challenging. We propose a transformer-based multimodal framework that jointly models spectral, spatial, and temporal EEG features alongside linguistic and paralinguistic cues from interviews. By employing synchronized multi-head cross-attention and self-attention mechanisms, the model effectively captures intra- and inter-modal correlations. In addition, a flexible temporal sequence matching strategy reduces EEG channel requirements, enhancing device portability. Evaluated on the MODMA and DAIC-WOZ datasets, our approach achieves superior performance compared to state-of-the-art models, with a 4.7% improvement in accuracy and a 10% increase in precision. These results demonstrate the potential of the proposed framework for accurate, scalable, and cost-effective depression detection in both clinical and real-world settings.
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