Papers for

mental health monitoring teams

Papers whose findings have a practical use for this group, as judged from the abstract. Open a paper to read what it means in practice.

Cross-subject emotion tracking from EEG and fNIRS signals improves accuracy

Modeling Shared and Individual Structure for Cross-Subject Continuous Affect Regression from EEG-fNIRS

Abstract: Continuous, second-by-second valence-arousal estimation from physiological signals is typically studied in a subject-dependent setting, where the model sees labeled data from the same person it is later evaluated on. We study the harder zero-shot cross-subject variant on a synchronized EEG-fNIRS dataset: predict raw-scale ([1, 255]) valence and arousal trajectories for subjects whose labels the model never observes, given only their unlabeled EEG/fNIRS recordings while watching the same video stimuli as a disjoint set of training subjects. We decompose the affect trajectory into a structure shared across subjects who watch the same stimuli and an individual structure estimated for each test subject from a label-free EEG marker (alpha-band cross-channel synchrony), which rescales the shared trajectory around the scale midpoint. We validate the per-subject calibration mechanism on four independent axes: leave-one-subject-out correlation between the marker and each subject's true optimal gain, a functional-form comparison against non-linear alternatives, a repeated leave-4-out component ablation isolating each part of the pipeline's contribution, and a ceiling analysis bounding the remaining headroom for per-subject scaling. On held-out subjects, the model reaches an overall MAE of 25.96 / 22.80 across two evaluation batches (valence 21.94 / 19.6, arousal 29.98 / 26.0), well below EEGNet and ASAC-Net baselines reported for the same subject-independent split (raw scale score 60.6 and 55.0 respectively). We further report a systematic negative-result search across model architectures, feature representations, and prediction targets that found no signal able to improve on the single alpha-synchrony marker.

Fri 2 OctArtificial Intelligence
The gist
Measuring emotions from brain signals usually works best when models see data from the same person they test on. This paper looks at a harder problem: predicting emotions for new people with no labeled data, just unlabeled brain signals while they watch the same videos as others. The authors separate the emotion signals into parts shared across people and individual differences, adjusting the shared part using a brain activity marker. Their method predicts emotion values better than existing approaches for new people without prior labels.
Open → 2610.02796v1

Differential attention improves emotion recognition from eeg and speech fusion

Differential Attention Unlocks Complementary EEG and Speech Fusion for Emotion Recognition

Abstract: Multimodal emotion recognition (MER) increasingly pairs EEG with speech, treating internal neural signals and external vocal expression as informative views of affect. In practice, naive fusion underperforms the stronger single modality, because EEG artifacts inject noise that corrupts the shared representation. We introduce EmoSpeechBrain, a multimodal framework built on the insight that noise suppression is a precondition for effective fusion. Its EEG encoder uses differential attention, taking the difference between two attention maps to cancel shared noise and isolate discriminative neural activity. An attention-based gating adapter aligns both modalities in a shared space and weights each one's contribution to the prediction. On two datasets - PME4 and EAV, EmoSpeechBrain improves MER accuracy by up to 12.9% over other state-of-the-art (SOTA) EEG encoders, and surpasses unimodal speech and EEG baselines by up to 13.1% and 23.1%. These results show that once EEG noise is suppressed, fusion delivers gains that naive combination cannot.

Fri 25 SeptMachine Learning
The gist
Recognizing emotions can be done by looking at brain signals (EEG) and speech, but simply combining these two sources often doesn’t work well because noise in brain signals interferes. The authors created EmoSpeechBrain, a system that first reduces noise in EEG by focusing on differences in attention patterns and then smartly combines brain and voice data. This method improves emotion recognition accuracy significantly compared to using either EEG or speech alone or previous fusion methods. Their work shows that cleaning EEG noise is key to successfully mixing brain and speech signals for understanding emotions.
Open → 2609.31399v1

Snugi AI achieves faster and more accurate early depression detection on Reddit

Snugi-AI-v2 @ eRisk 2026 Task 2: Early Depression Detection via a Learned Stopping Policy with Sustained Confidence Gate

Abstract: We describe the Snugi-AI-v2 submission to eRisk 2026 Task 2, the second edition of contextualized early depression detection from Reddit discussions. Our central contribution is a learned MLP stopping policy trained to directly optimize ERDE50, replacing the fixed and tiered threshold strategies used in all prior eRisk Task 2 submissions. Combined with a sustained confidence gate that commits only after N=3 consecutive rounds of high policy confidence, the system reduces false positives caused by transient emotional posts without sacrificing recall. The pipeline encodes each discussion thread with a frozen MentalRoBERTa model, maps the accumulated representation to a depression probability via an MLP classifier, and delegates the timing decision to the learned policy. Our best run achieves F1 = 0.73 (Run 1) and F_latency = 0.70 (Runs 0 and 3), with a median alert round of 8 out of 500, completing the full evaluation in 1 hour 26 minutes, the fastest among all complete-submission teams. We report a systematic ablation across five runs spanning two encoder variants, four stopping strategies, and three gate values, along with negative results from GRPO policy training, BDI-II post filtering, MentalLongformer encoding, and DeBERTa ensembling. Code: https://github.com/chiuyuwen91/erisk-2026

Tue 8 SeptComputation and LanguageInformation Retrieval
The gist
Detecting depression early from online conversations can help provide support sooner. The team behind Snugi-AI-v2 developed a smart system that decides the best moment to raise an alert by learning from past data. Instead of using fixed thresholds, their method waits until it is confident for three rounds in a row, reducing false alarms from temporary emotional posts. They used a language model to understand Reddit discussions and tested different strategies, finding one that balances speed and accuracy well.
Open → 2609.08161v1