000285736 001__ 285736
000285736 005__ 20260320150651.0
000285736 0247_ $$2doi$$a10.1109/ICTAI66417.2025.00117
000285736 037__ $$aDZNE-2026-00293
000285736 041__ $$aEnglish
000285736 1001_ $$0P:(DE-2719)9002891$$aSaraiva, Joao$$b0$$udzne
000285736 1112_ $$a2025 IEEE 37th International Conference on Tools with Artificial Intelligence$$cAthens$$d2025-11-03 - 2025-11-05$$gICTAI$$wGreece
000285736 245__ $$aIntegrating Large Language Models with Formal Planning to Automate the Design and Validation of Biosignal Processing Pipelines
000285736 260__ $$bIEEE$$c2025
000285736 29510 $$a2025 IEEE 37th International Conference on Tools with Artificial Intelligence (ICTAI) : [Proceedings] - IEEE, 2025. - ISBN 979-8-3315-4919-0 - doi:10.1109/ICTAI66417.2025.00117
000285736 300__ $$a806 - 812
000285736 3367_ $$2ORCID$$aCONFERENCE_PAPER
000285736 3367_ $$033$$2EndNote$$aConference Paper
000285736 3367_ $$2BibTeX$$aINPROCEEDINGS
000285736 3367_ $$2DRIVER$$aconferenceObject
000285736 3367_ $$2DataCite$$aOutput Types/Conference Paper
000285736 3367_ $$0PUB:(DE-HGF)8$$2PUB:(DE-HGF)$$aContribution to a conference proceedings$$bcontrib$$mcontrib$$s1774015444_12445
000285736 3367_ $$0PUB:(DE-HGF)7$$2PUB:(DE-HGF)$$aContribution to a book$$mcontb
000285736 520__ $$aRobust analysis of biosignals hinges on well-crafted processing pipelines, yet assembling them is still a slow, errorprone exercise, requiring both domain expertise and programming skills. While Large Language Models (LLMs) offer promising assistance, they fundamentally lack the combinatorial reasoning capabilities needed for designing reliable and reproducible processing pipelines. We present an innovative hybrid system that harnesses LLMs' natural language understanding while leveraging classical AI planning for logical reasoning and validation. A retrieval-augmented model parses natural language pipeline descriptions into implementation-independent atomic biosignal processing operations, which follow to a Hierarchical Task Network (HTN) domain that can plan and validate processing workflows, as well as flag violations of biosignal processing best practices. Evaluation on a preliminary corpus of scenarios demonstrates 0. 8 9 - 0. 9 8 precision in mapping natural language to processing blocks and 0.97-0.98 F1-score in generating valid 2-15 step pipelines using SHOP3 planner. A drag-and-drop pipeline design interface allows users to build and formally validate their pipeline ideas from scratch, showing potential to democratize and expedite biosignal analysis for scientists of different backgrounds. 
000285736 536__ $$0G:(DE-HGF)POF4-353$$a353 - Clinical and Health Care Research (POF4-353)$$cPOF4-353$$fPOF IV$$x0
000285736 588__ $$aDataset connected to CrossRef Conference
000285736 7001_ $$0P:(DE-2719)2810283$$aDyrba, Martin$$b1$$udzne
000285736 7001_ $$aKirste, Thomas$$b2
000285736 773__ $$a10.1109/ICTAI66417.2025.00117
000285736 8564_ $$uhttps://pub.dzne.de/record/285736/files/DZNE-2026-00293_Restricted.pdf
000285736 8564_ $$uhttps://pub.dzne.de/record/285736/files/DZNE-2026-00293_Restricted.pdf?subformat=pdfa$$xpdfa
000285736 9101_ $$0I:(DE-HGF)0$$6P:(DE-2719)9002891$$aExternal Institute$$b0$$kExtern
000285736 9101_ $$0I:(DE-588)1065079516$$6P:(DE-2719)2810283$$aDeutsches Zentrum für Neurodegenerative Erkrankungen$$b1$$kDZNE
000285736 9131_ $$0G:(DE-HGF)POF4-353$$1G:(DE-HGF)POF4-350$$2G:(DE-HGF)POF4-300$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$aDE-HGF$$bGesundheit$$lNeurodegenerative Diseases$$vClinical and Health Care Research$$x0
000285736 9201_ $$0I:(DE-2719)1510100$$kAG Teipel$$lClinical Dementia Research (Rostock /Greifswald)$$x0
000285736 980__ $$acontrib
000285736 980__ $$aEDITORS
000285736 980__ $$aVDBINPRINT
000285736 980__ $$acontb
000285736 980__ $$aI:(DE-2719)1510100
000285736 980__ $$aUNRESTRICTED