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license: bsd-3-clause
library_name: braindecode
pipeline_tag: feature-extraction
tags:
- eeg
- biosignal
- pytorch
- neuroscience
- braindecode
- convolutional
---
# EEGInceptionMI
EEG Inception for Motor Imagery, as proposed in Zhang et al. (2021) [1]
> **Architecture-only repository.** Documents the
> `braindecode.models.EEGInceptionMI` class. **No pretrained weights are
> distributed here.** Instantiate the model and train it on your own
> data.
## Quick start
```bash
pip install braindecode
```
```python
from braindecode.models import EEGInceptionMI
model = EEGInceptionMI(
n_chans=22,
sfreq=250,
input_window_seconds=4.0,
n_outputs=4,
)
```
The signal-shape arguments above are illustrative defaults — adjust to
match your recording.
## Documentation
- Full API reference: <https://braindecode.org/stable/generated/braindecode.models.EEGInceptionMI.html>
- Interactive browser (live instantiation, parameter counts):
<https://huggingface.co/spaces/braindecode/model-explorer>
- Source on GitHub: <https://github.com/braindecode/braindecode/blob/master/braindecode/models/eeginception_mi.py#L14>
## Architecture
## Parameters
| Parameter | Type | Description |
|---|---|---|
| `input_window_seconds` | float, optional | Size of the input, in seconds. Set to 4.5 s as in [1] for dataset BCI IV 2a. |
| `sfreq` | float, optional | EEG sampling frequency in Hz. Defaults to 250 Hz as in [1] for dataset BCI IV 2a. |
| `n_convs` | int, optional | Number of convolution per inception wide branching. Defaults to 5 as in [1] for dataset BCI IV 2a. |
| `n_filters` | int, optional | Number of convolutional filters for all layers of this type. Set to 48 as in [1] for dataset BCI IV 2a. |
| `kernel_unit_s` | float, optional | Size in seconds of the basic 1D convolutional kernel used in inception modules. Each convolutional layer in such modules have kernels of increasing size, odd multiples of this value (e.g. 0.1, 0.3, 0.5, 0.7, 0.9 here for `n_convs=5`). Defaults to 0.1 s. |
| `activation: nn.Module` | — | Activation function. Defaults to ReLU activation. |
## References
1. Zhang, C., Kim, Y. K., & Eskandarian, A. (2021). EEG-inception: an accurate and robust end-to-end neural network for EEG-based motor imagery classification. Journal of Neural Engineering, 18(4), 046014.
## Citation
Cite the original architecture paper (see *References* above) and braindecode:
```bibtex
@article{aristimunha2025braindecode,
title = {Braindecode: a deep learning library for raw electrophysiological data},
author = {Aristimunha, Bruno and others},
journal = {Zenodo},
year = {2025},
doi = {10.5281/zenodo.17699192},
}
```
## License
BSD-3-Clause for the model code (matching braindecode).
Pretraining-derived weights, if you fine-tune from a checkpoint,
inherit the licence of that checkpoint and its training corpus.
|