TinyML
EEG-TCNet
Temporal convolutional network that classifies motor-imagery EEG signals for brain-machine interfaces with very few parameters.
Levelmodel
ClassTinyML
Parameters4.1K
Contextnot in the notes
Released31 May 2020
Opennessunknown
Runs onmobile or NPU
Statusactive
R&D Topics
Identity
- Developer
- ETH Zurich
- Family
- not in the notes
- Released
- 31 May 2020 sourcesannouncement page
- Weights published
- –
- Record checked
- 12 Oct 2026
Where this comes from
Developer and dates from the research notes. Release date is the vendor's public announcement; weights date is when open weights were published.
Architecture and Size
- Architecture
- CNN Temporal convolutional network
- Total parameters
- 4.1K sources
- Active parameters
- –
- Checkpoint parameters
- –
- Context length
- not in the notes
- Input
- sensor
- Output
- labels and boxes
Where this comes from
Parameter counts as the developer states them. Where the checkpoint holds a different total, both are shown (register A16).
Openness and Access
- Openness
- unknown
- Licence
- not in the notes sources
- Access routes
- open weights download
Where this comes from
The licence file governs over registry tags and aggregator labels.
Deployment
- Runs on
- mobile or NPU
- Footprint
- NXP eIQ lists 4,096 parameters, 28 KB INT8, compatible with i.MX 8MP and i.MX 93. The paper targets embedded classification on resource-limited edge devices.
- Runtimes
- not in the notes
Where this comes from
Deployment tiers and runtimes as named in the notes. Footprint is the notes' figure, not a measurement by AURION.
Indonesia
- Indonesian support
- not stated
- Regional languages
- not in the notes
- Jakarta hosting
- unknown
- Languages
- not in the notes
Where this comes from
Indonesia fields are carried on every model; unknown is an allowed value.
Benchmark Results
The notes record no benchmark result for this model.
Lineage
The notes record no lineage for this model.
Industry Use Cases
No industry use case links this model.
Links
DeveloperETH Zurichuniversity, Switzerland
ETH Zurichuniversity, SwitzerlandModelEEG-TCNetETH Zurich, TinyML
EEG-TCNetETH Zurich, TinyMLR&D TopicsTinyML on Microcontrollers
Use Casesnone recorded
Who Runs Itnone recorded
Organisations 1
Sources
- arxiv.org/abs/2006.00622 https://arxiv.org/abs/2006.00622
- github.com/NXP/eiq-model-zoo/tree/main/tasks/misc https://github.com/NXP/eiq-model-zoo/tree/main/tasks/misc
To cite this page: Project AURION, AI Use-Cases Research & Innovation, "EEG-TCNet", https://aurion.semai.id/models/eeg-tcnet/, records of October 2026.