TriNet-MTL: A Multi-Branch Deep Learning Framework for Biometric Identification and Cognitive State Inference from Auditory-Evoked EEG

Electroencephalography (EEG) signals, particularly those elicited by auditory stimuli, provide a rich window into both cognitive processing and physiological traits. This dual nature makes auditory-evoked EEG highly promising for diverse applications ranging from biometric authentication to cognitive state inference. However, most existing approaches treat these tasks in isolation and rely on unimodal or task-specific models, which limits their robustness and generalization in real-world, noisy environments. In this work, we introduce TriNet-MTL (Triple-Task Neural Transformer for Multitask Learning). This unified deep learning framework simultaneously addresses three complementary objectives: (i) biometric user identification, (ii) auditory stimulus language classification (native vs. non-native), and (iii) device modality recognition (in-ear vs. bone-conduction). The proposed architecture combines a shared temporal encoder with a Transformer-based sequence representation module, followed by three specialized task heads. This design allows the model to leverage shared representations while still optimizing for the unique characteristics of each task. Training is conducted with a sliding-window strategy and a joint cross-entropy loss function to balance task performance. Extensive experiments demonstrate that TriNet-MTL achieves strong performance across all tasks, including over 91% accuracy in user identification, high precision in language discrimination, and reliable device modality classification. Notably, multitask learning not only improves individual task outcomes but also enhances feature sharing across tasks, reducing redundancy and mitigating interference. Our findings highlight the potential of multitask deep learning as a powerful paradigm for EEG-based analysis, paving the way toward integrated neurotechnology solutions that unify biometric authentication, brain–computer interface (BCI) systems, and cognitive monitoring in a single framework.