arXiv:2506. 01353v3 Announce Type: replace Abstract: The integration of brain-computer interfaces (BCIs), in particular electroencephalography (EEG), with artificial intelligence (AI) has shown tremendous promise in decoding human cognition and behavior from neural signals.
Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.
arXiv:2607. 00547v1 Announce Type: cross Abstract: Existing egocentric benchmarks have primarily constructed the egocentric setting from first-person-view data, which makes it difficult to evaluate egocentric perspective itself in isolation.
arXiv:2607. 02542v1 Announce Type: new Abstract: General-purpose embodied agents must understand multimodal instructions, anticipate how their environment will evolve, and produce precise control actions over extended horizons.
arXiv:2511. 17581v3 Announce Type: replace Abstract: Modeling the cognitive and experiential factors of human navigation is central to deepening our understanding of human-environment interaction and to enabling safe social navigation and effective assistive wayfinding.
arXiv:2607. 24126v1 Announce Type: cross Abstract: Brain-machine interfaces provide a link between neural activity and external devices, enabling restoration of motor function and advancing human-machine interaction using non-invasive electroencephalography (EEG).
arXiv:2608. 15999v1 Announce Type: new Abstract: Automatic emotion assessment can benefit from combining neural and behavioral signals, but many multimodal approaches rely on separate, modality-specific feature-extraction pipelines before fusion.
arXiv:2603. 16970v2 Announce Type: replace-cross Abstract: Multimodal egocentric activity recognition integrates visual and inertial cues for robust first-person behavior understanding.