MycoKit: Bioelectric Recording Kits from Lab to Forest

This demo introduces MycoKit, a portable, low-cost citizen-science platform for recording, visualising, and sonifying fungal mycelial bioelectrical activity in real-world environments and the iNaturalist MycoWave project. The goal is to make a largely hidden form of biological signalling accessible to researchers, mycologists, educators, and members of the public, while supporting the collection of diverse, ecologically grounded fungal biosignal datasets.

Fungi are one of the major multicellular kingdoms of life, yet their electrical dynamics remain comparatively underexplored. Fungal mycelium – the fine network of hyphae that forms most of a fungus’s body – produces bioelectrical activity including oscillations and spike-like events. These signals can vary across a wide range of time scales, from milliseconds to days. Although some waveforms share broad features with neural spikes, fungal activity is slower, highly context-dependent, and poorly represented in existing research-grade datasets. Field-based recording is especially important because many fungal species are difficult or impossible to cultivate outside their natural ecological and symbiotic relationships.

Attendees will observe the full MycoKit workflow, from electrode placement and signal acquisition through to real-time streaming, visualisation, metadata capture, auditory display, and upload. We will bring live fruiting fungi and mycelial blocks for hands-on demonstration. Participants will see both live and accelerated time-lapse recordings, allowing them to examine patterns that unfold too slowly to perceive during a short demonstration. The MycoKit system is an evolving family of kits using accessible off-the-shelf hardware in different combinations (including Raspberry Pi’s, M5Stack2 Cores, NI mioDAQ USBs, SparkFun Qwiic analogue-to-digital converters), with open-source software and a companion smartphone application. The interface enables users to monitor recordings during collection, check signal quality, and enter field metadata, which can then be uploaded to the MycoWave project on iNaturalist. MycoWave extends conventional fungal citizen science beyond photographs and species observations by supporting electrical recordings and sonified data. The kit and associated designs are released under a CC BY 4.0 licence, enabling reuse, adaptation, and commercial application.

A central interactive feature is the sonification of fungal activity. Participants will hear live and time-lapse mycelial recordings, making changes in rhythm, activity, and signal structure directly perceptible. Building on established auditory approaches in neural recording, the system extends simple spike-triggered clicks by mapping spike amplitudes to tonal variation and enabling time-warped playback. Accelerated listening can reveal temporal relationships among spike sequences that may be difficult to identify visually or in real time. This uses the human auditory system’s strong capacity for detecting temporal structure as a complementary tool for data exploration and public engagement.

The demo is relevant to workshop themes concerning biosignal data systems, temporal data, human-centred tools, and real-world impact. It presents an end-to-end workflow that joins field instrumentation, streaming software, mobile interaction, data-quality support, metadata collection, and auditory display for an underrepresented biosignal domain. It also addresses a key data challenge for temporal foundation models: the need for broad and diverse signal contexts. Bioelectrical spikes and oscillations occur across fungi, plants, and animals; scalable datasets spanning these groups could support more general models of biological temporal dynamics.

Exploring Underground Brains: Interactive Visual Analytics for Mycelial Bioelectric Signals

We demonstrate an interactive visual analytics system for exploring event dynamics in mycelial bioelectric activity. Fungal electrophysiology is a challenging, underexplored biosignal domain: events may be rare, occur across time scales, contain smaller patterns within larger waveforms, and differ between sessions, channels, substrates, and environmental conditions. It is often difficult to separate biologically meaningful activity from recording artefacts.

Doing so usually requires knowledge of the experimental context, so a fixed rule cannot reliably identify events before researchers inspect the data. For these reasons, standard one-size-fits-all analysis pipelines are not well suited to early exploration or dataset curation. Our system keeps the researcher involved throughout the analysis. It makes the consequences of analytic choices visible and shows the evidence supporting each candidate event.

The demonstration presents a browser-based environment organised around four connected activities: Explore, Analyse, Discover, and Library. In Explore, users navigate recordings ranging from long-duration datasets to short signal windows, inspect one or more channels, review existing annotations, and identify areas that remain unexplored. In Analyse, users construct ordered workflows from signal-processing and time-series analysis stages, such as filtering, preprocessing, encoding, similarity search, clustering, matrix-profile analysis, and frequency-domain analysis. Each stage exposes relevant parameters and displays its intermediate result alongside the signal. This lets users change preprocessing decisions, observe effects on later representations or detections, and compare analysis paths without repeatedly rerunning bespoke scripts.

Validated workflows can be saved as reusable templates. In Discover, these templates can be applied across selected channels or longer recordings to identify candidate events at scale. Users can compare outputs from multiple workflows, inspect agreement and disagreement among detection methods, or select an exemplar event and search for similar patterns elsewhere in the dataset. Candidate events can then be reviewed and added to the Library, where they are grouped into researcher-defined event families. The library supports the gradual construction of a domain vocabulary for recurring waveform types while retaining links to the source recording, analysis workflow, and visual evidence that motivated each classification.

During the live demo, participants can explore waveform discovery. We will provide multichannel fungal recordings containing heterogeneous activity and potential artefacts. Participants will select a short segment or annotated exemplar, construct or modify an analysis workflow, and observe how changing preprocessing and detection parameters affects intermediate representations and candidate events in real-time. They could apply the resulting template to other recordings, compare alternative workflows, and populate the event library. The demo will show how exploratory interactions can become an inspectable and repeatable detection procedure rather than remaining an undocumented, dataset-specific scripting experiment.

Originally developed for the fungi biosignal analysis, the workflow addresses a broader problem in brain- and body-derived time series: heterogeneous biological data often require the combination of automated pattern discovery with human judgement about signal context, artefact, and scientific relevance. The system offers a transferable interface pattern for researchers who understand their biological recordings but may not be specialist programmers or signal-processing experts. It makes analysis pipelines visible, adjustable, and reusable, supporting discovery while retaining domain expertise in interpretation.