EASELAN: An Open-Source Framework for Multimodal Biosignal Annotation and Data Management

2025-10-17

AI Summary

To address the lack of a unified framework supporting the full annotation workflow—encompassing annotation preparation, multi-channel synchronization, version control, and post-processing—for multimodal biosignal data, this paper introduces an open-source annotation system. Built upon an extended version of ELAN and integrated with GitHub, it enables collaborative, version-controlled management of annotation files. Its modular architecture supports time-aligned annotation across diverse modalities (e.g., EEG, EMG, video) and provides one-click structured export. The system automates the end-to-end pipeline from raw data loading to annotation post-processing. It has been successfully deployed in the DFG Daily Activities study, enabling the release of the fully annotated Table Setting Database. The core contributions are: (1) the first deep integration of version control into the biosignal annotation workflow, and (2) a scalable, multi-channel synchronized annotation paradigm.

Technology Category

• Computer Vision: Multi-modal Vision
• Machine Learning: Multimodal Learning
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data

Abstract

Recent advancements in machine learning and adaptive cognitive systems are driving a growing demand for large and richly annotated multimodal data. A prominent example of this trend are fusion models, which increasingly incorporate multiple biosignals in addition to traditional audiovisual channels. This paper introduces the EASELAN annotation framework to improve annotation workflows designed to address the resulting rising complexity of multimodal and biosignals datasets. It builds on the robust ELAN tool by adding new components tailored to support all stages of the annotation pipeline: From streamlining the preparation of annotation files to setting up additional channels, integrated version control with GitHub, and simplified post-processing. EASELAN delivers a seamless workflow designed to integrate biosignals and facilitate rich annotations to be readily exported for further analyses and machine learning-supported model training. The EASELAN framework is successfully applied to a high-dimensional biosignals collection initiative on human everyday activities (here, table setting) for cognitive robots within the DFG-funded Collaborative Research Center 1320 Everyday Activity Science and Engineering (EASE). In this paper we discuss the opportunities, limitations, and lessons learned when using EASELAN for this initiative. To foster research on biosignal collection, annotation, and processing, the code of EASELAN is publicly available(https://github.com/cognitive-systems-lab/easelan), along with the EASELAN-supported fully annotated Table Setting Database.

Problem

• Addressing complexity in multimodal biosignals annotation workflows
• Streamlining preparation and management of biosignals datasets
• Facilitating rich annotations for machine learning model training

Innovation

• Extends ELAN tool for multimodal biosignal annotation
• Integrates GitHub version control into annotation workflow
• Streamlines data preparation to post-processing pipeline