Reproducing a deep EEG architecture from its paper is a week of work before you learn anything about your own data — and the reimplementation is where the discrepancy between your numbers and the published ones usually hides.
BCILattice ships those architectures as blocks. EEGNet, ShallowConvNet, DeepConvNet, ATCNet and EEGConformer are available directly, alongside sequence models, transformers and quantised edge variants, and the training primitives are blocks on the same canvas rather than a script beside it.
Architectures available as blocks
The Hugging Face surface is available too — transformers, tokenizers, training arguments, PEFT adapters including LoRA, datasets and evaluate — for work that pairs a biosignal encoder with a pretrained model.
| Category | Classes |
|---|---|
| BCI EEG models | EEGNet, ShallowConvNet, DeepConvNet, ATCNet, EEGConformer |
| Sequence models | LSTMClassifier, BiLSTMClassifier, GRUClassifier, TCNClassifier, MambaSSM |
| Transformers | PatchTST, BrainBERT, EEGTransformer, ViTEEG |
| Edge models | MobileEEGNet, QuantizedEEGNet, TinyEEGNet |
| Riemannian | Covariances, ERP covariances, tangent space with MDM, FgMDM, TSClassifier |
The training loop as a graph
DL training primitives are blocks: optimisers, schedulers, loss functions, early stopping, checkpointing. Assembling them on the canvas produces the same thing a PyTorch training script would, with two differences — the graph is the record of what ran, and a parameter you deliberately set to zero stays zero rather than being replaced by a default.
Blocks that need code accept it. Custom Code executes Python you supply, and it is the one block in the catalogue that is plan-gated.
When the model wants an image
Vision architectures expect a 2-D input, and an EEG trial is a set of 1-D time courses. The Data Projection step in the Analysis Suite encodes a time course as a 2-D image so a ViT or a CNN pretrained on images has something shaped correctly to read.
That makes the choice of encoding an explicit, reviewable analysis step with a recorded parameter set, rather than a transform buried in a data loader.
What it runs on
NVIDIA CUDA
Full support, CUDA 11.8 through 12.4, driver 520.61 or newer. RTX 3060 or better recommended for deep training.
AMD
Not supported. AMD GPUs fall back to CPU automatically; ROCm support is planned.
Apple Silicon
macOS builds run where published; PyTorch acceleration depends on the packaged runtime and local MPS support.
CPU only
Works, and is fine for classical pipelines. For deep training an 8-core or better CPU is strongly recommended, with 16–32 GB RAM.
A deep model does not exempt you from the split
The failure mode that produces implausible EEG deep-learning accuracies is almost never the architecture. It is a random split across trials from one subject, or a search evaluated on the fold that selected it.
The same grouped splits, nested search and results validation that apply to a classical pipeline apply here, and the validation tab reads the run design regardless of which block trained the model.
Questions
Which EEG deep learning architecture should I start with?
EEGNet is the usual starting point — small, well-studied, and it trains fast enough to iterate on. Move to ShallowConvNet or DeepConvNet if the paradigm has strong spectral structure, and to a transformer only once a simpler model has given you a baseline worth beating.
Can I fine-tune a pretrained model?
Yes. The Hugging Face transformers, PEFT and training surfaces are available as blocks, including LoRA and the other adapter configurations, so a pretrained encoder can be fine-tuned inside the same pipeline.
Can I run this on a laptop without a GPU?
Classical pipelines, yes, comfortably. Deep training on CPU is possible but slow; the practical minimum is 8 GB RAM, with 16–32 GB and an NVIDIA GPU recommended for deep work.
What happens to the trained weights?
They stay in your project on disk. Publishing a model to BCINexus is a separate, explicit action, and you choose whether the weights go with the study record or stay local.
Read the reference
Related
EEG Machine Learning
A node canvas of 119 blocks covers the pipeline from design matrix to evaluation — and the evaluation half is built to make an inflated score hard to produce by accident.
BCI Domain Adaptation
CORAL, MMD alignment, Transfer Component Analysis, a DANN discriminator, feature alignment and subject adaptation — assembled on the same canvas as the model they feed.
fNIRS Machine Learning
The model canvas is the same one EEG uses. What changes for fNIRS is which features carry information at 10 Hz, how the chromophore enters the design matrix, and how little a small cohort forgives a bad split.
BCI Research Platform
BCILattice runs on your machine and covers import through model training. BCINexus is where the finished study is published, reviewed, cited and reused.