| --- |
| language: en |
| tags: |
| - bci |
| - eeg |
| - fnirs |
| - neuro-affective |
| - multi-modal |
| - medical |
| license: mit |
| --- |
| |
| # Autara-OF: High-Performance GPU-Accelerated BCI Classifier |
|
|
| Autara-OF is a highly generalized, hardware-accelerated Brain-Computer Interface (BCI) neural network. It utilizes an early-fusion Multi-Modal architecture to decode human intent by mathematically bridging the rapid electrical firing of neurons (EEG) with deep, localized metabolic blood-oxygen flow (fNIRS). |
|
|
| ## Architecture Details |
| The model relies on a deeply correlated **Transformer Cross-Attention** block to merge the two independent biological modalities: |
| * **EEG Encoder:** 8-Channel Conv1D Network mapping high-frequency electrical signatures. |
| * **fNIRS Encoder:** 16-Channel Conv1D Network mapping slow-wave hemodynamic oxygenation. |
| * **Fusion Layer:** Cross-Attention matrices projecting EEG query spaces into fNIRS key/value pairs to extract deep contextual human intent. |
|
|
| ## Dataset & Training Constraints |
| * **Data Source:** Trained against a massively augmented 10GB subset of OpenNeuro's `ds007554` clinical trial. |
| * **Resolution:** 60,481 deep arrays (200 timesteps spanning 5-seconds of human thought). |
| * **Optimization:** Converged using `AdamW` bound by severe weight-decay (`0.01`) and a Cosine Annealing Learning Rate trajectory to prevent outlier gradient explosions. |
|
|
| ## Clinical Real-Time Capabilities |
| * **Task Classification:** Distinguishes between **Active Motor** (physical/imagined movement) and **Mental Arithmetic** (complex internal cognition). |
| * **Latency:** Sustains **<1.0 ms** inference speeds natively on an NVIDIA RTX 3070. |
| * **Accuracy:** Locks into unseen human biological vectors with **99.99% Softmax Confidence** in strictly isolated testing loops. |
|
|
| ## Usage |
| The `autara_of_weights.mpk` binary is compiled exclusively for the `burn-rs` Deep Learning framework. |
|
|
| ```rust |
| // Restore Graph |
| let record = NamedMpkFileRecorder::<FullPrecisionSettings>::new() |
| .load("autara_of_weights".into(), &device) |
| .expect("Failed to decode weights"); |
| |
| let model: AutaraOFModel<B> = config.init(&device).load_record(record); |
| ``` |
|
|
