BIOOMNIA: An Integrated Platform for Multimodal Experimental Research

Contemporary experimental research increasingly requires the simultaneous recording of heterogeneous data: physiological signals, motor activity, gaze direction and pupil parameters, facial activity, behavioral events, the state of technical or virtual systems, environmental parameters, and questionnaire results. When independent software tools and instruments are used, the main difficulty shifts from signal acquisition itself to temporal synchronization, reconstruction of experimental conditions, version control of the methodology, and provenance tracking. BIOOMNIA is an integrated research software platform in which an experiment is formalized as an interconnected system of a scientific hypothesis, protocol, participants, measurement channels, experimental exposures, events, physical and virtual environment parameters, states of technical systems, and data-processing procedures. The components of a study are linked by a common temporal and event model. The architecture supports equipment of different classes and manufacturers through a unified channel model and adapters. A data source may be a physical instrument, a software or virtual sensor, a video stream, an external measurement-data source, or a previously recorded experimental dataset. Within a single session, BIOOMNIA can jointly record EEG, ECG, EDA, EMG, respiration, temperature, heart rate, body movement, eye tracking, pupil parameters, facial activity, VR/XR or technical-system telemetry, and environmental characteristics. The scientific information layer uses a local index built from OpenAlex and containing approximately 649 million records of different types. Based on available metadata and, where legally permitted, full text, the platform performs automated structural processing of scientific information and forms a linked research space containing publications, hypotheses, methods, claims, evidence, signals, studies, and datasets. The platform can be used in laboratory, virtual, remote, longitudinal, and distributed multicenter studies, as well as in experiments involving interactions between humans and technical, robotic, or autonomous systems. The same temporal and event model can be used to quantitatively compare observable reactions, decisions, action sequences, adaptation time, and error-correction behavior of humans and artificial intelligence systems performing identical or functionally comparable tasks, without assuming cognitive equivalence between them. This report describes the formalized experimental model, architecture, multimodal recording, quality control, work with existing experimental datasets, human–artificial system research scenarios, and an example of a synchronous multimodal study of pilot–air traffic controller interaction. The report is a system-level technical description of the platform and does not present the results of clinical validation or a comparative evaluation of its effectiveness.