{"ID":6023591,"CreatedAt":"2026-07-08T01:00:23.257252134Z","UpdatedAt":"2026-07-10T13:53:55.553307773Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.06297","arxiv_id":"2607.06297","title":"DS-MTNet:Structured Multi-Task EEG Decoding for Human-Machine Collaboration","abstract":"Current human-machine collaboration (HMC) systems rely on environment-facing sensors to observe visible actions and scene states, but the internal perceptual, intention-related, and state-related processes of operators remain insufficiently integrated into machine perception. Electroencephalography (EEG) provides a non-invasive, time-resolved modality to capture neural activity associated with these processes and can serve as an additional sensing channel in HMC. However, HMC-relevant EEG evidence is often mixed in continuous recordings. Existing EEG decoding methods usually target task-specific classification or aggregate prediction, so multiple HMC-relevant readouts are rarely organized in a unified EEG representation. To address this gap, this paper proposed the Decomposed-Source Multi-Task Network (DS-MTNet), a structured multi-task EEG decoding framework. DS-MTNet integrated three streams, namely EEG waveforms, task-routed source embeddings, and temporal-spectral power features, into reusable slots and used dual gating mechanisms to route task-specific components. The model was tested on a sustained-attention driving EEG dataset with three representative readouts: lane-departure-related epochs for environmental-event processing, steering-response stage for response preparation, and reaction-time-defined alertness state for internal state. DS-MTNet achieved the best mean performance among traditional, single-task deep, and multi-task EEG baselines, with the most robust gains observed for steering-response stage decoding. Ablation and interpretability analyses suggested that DS-MTNet jointly decoded multiple readouts and organized event-related, response-related, and state-related EEG evidence in a unified source-slot representation. These findings provide a computational step toward incorporating operator-related neural evidence into machine perception in HMC.","short_abstract":"Current human-machine collaboration (HMC) systems rely on environment-facing sensors to observe visible actions and scene states, but the internal perceptual, intention-related, and state-related processes of operators remain insufficiently integrated into machine perception. Electroencephalography (EEG) provides a non...","url_abs":"https://arxiv.org/abs/2607.06297","url_pdf":"https://arxiv.org/pdf/2607.06297v1","authors":"[\"Xinjia Yu\",\"Yang Zhou\",\"Jing Yang\",\"Tielin Shi\",\"Tao Cheng\"]","published":"2026-07-07T14:03:33Z","proceeding":"cs.HC","tasks":"[\"cs.HC\"]","methods":"[\"Generative Adversarial Network\"]","has_code":false}