> ## Documentation Index
> Fetch the complete documentation index at: https://docs.usefusion.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# AI Denoising

> Clean EEG signals with ZUNA foundation model — remove artifacts while preserving neural activity

# AI Denoising with ZUNA

ZUNA is a diffusion-based foundation model trained on large-scale EEG data. It removes noise artifacts from brain recordings while preserving the underlying neural signals.

## What Gets Cleaned

| Artifact Type    | Source                      | Effect on EEG                    |
| ---------------- | --------------------------- | -------------------------------- |
| **Eye blinks**   | Eyelid movement             | Large frontal spikes (50–200 µV) |
| **Saccades**     | Eye movement                | Lateral voltage shifts           |
| **Muscle (EMG)** | Jaw, forehead, neck         | High-frequency broadband noise   |
| **Line noise**   | Power outlets               | 50/60 Hz sinusoidal interference |
| **Drift**        | Electrode impedance changes | Slow baseline wander             |
| **Movement**     | Head or cable motion        | Large transient artifacts        |

## What Gets Preserved

* **Neural oscillations** — Alpha (8–13 Hz), beta (13–30 Hz), theta (4–8 Hz), delta (1–4 Hz), gamma (30+ Hz)
* **Event-related potentials** — P300, N170, N400, and other cognitive markers
* **Resting-state patterns** — Eyes-open vs eyes-closed alpha differences
* **Task-related changes** — Frequency power shifts during cognitive tasks

## Processing Pipeline

```
Raw CSV → Parse channels → Resample to 256 Hz → Filter → Epoch (5s segments)
    → Normalize → ZUNA GPU inference → Denormalize → Reconstruct continuous signal
```

### Technical Details

* **Epoch size**: 5 seconds (1,280 samples at 256 Hz)
* **Overlap**: Adjacent epochs for seamless reconstruction
* **Normalization**: Per-epoch z-score normalization before inference, reversed after
* **GPU**: NVIDIA A100 80GB VRAM

## Using Denoising

### From the Recordings Page

1. Record live EEG or upload a CSV file
2. Click **"🧹 Denoise Recording"**
3. Wait for processing (5–15 seconds if GPU is warm)
4. View the **before/after comparison** — raw signal on the left, denoised on the right
5. Download the cleaned CSV or send to the Analysis page

### From the API

For programmatic access, POST to `/api/eeg-models/inference`:

```bash theme={null}
curl -X POST https://usefusion.ai/api/eeg-models/inference \
  -H "Authorization: Bearer YOUR_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "task": "denoise",
    "eeg": {
      "csv_content": "CP3,C3,F5,PO3,PO4,F6,C4,CP4\n12.3,8.1,...",
      "device_type": "neurosity",
      "sfreq": 256
    },
    "output_format": "json"
  }'
```

See the [API Reference](/model-apis/api-reference) for full endpoint documentation.

## GPU Management

The ZUNA model runs on a dedicated GPU VM that auto-manages its lifecycle:

### Auto-Sleep

The VM deallocates after **30 minutes** of inactivity. This stops compute billing while preserving the disk. The GPU status indicator on the recordings page shows current state.

### Auto-Wake

When you trigger denoising and the GPU is sleeping:

1. The backend detects the VM is deallocated
2. It sends a start command and returns HTTP 202
3. The frontend **automatically retries** every 15 seconds (up to 6 times)
4. You see live status updates: "GPU is starting up. Auto-retrying in 15s..."
5. Once the VM is ready (\~90 seconds), your denoising request processes normally

<Tip>
  If you know you'll need the GPU soon, click **"Wake GPU"** in the status bar at the top of the recordings page. This starts the VM immediately so it's ready when you finish recording.
</Tip>

### Status Indicators

| Indicator           | Meaning                                        |
| ------------------- | ---------------------------------------------- |
| 🟢 Green            | GPU ready — denoising will start immediately   |
| 🟡 Yellow (pulsing) | GPU starting up — will be ready in \~90s       |
| ⚫ Gray              | GPU sleeping — will auto-wake on first request |

## Before / After Comparison

After denoising, the recordings page shows a side-by-side comparison:

* **Left panel (red label)** — Your original raw recording with all artifacts visible
* **Right panel (green label)** — The ZUNA-denoised output

Both use the same amplitude scale and time axis. You can adjust the scale (±50, ±100, ±200 µV) and zoom/pan with the scroll bar to inspect specific time windows.

This comparison helps you:

* Verify that artifacts were removed (look for smoothed-out eye blink spikes)
* Confirm neural features are preserved (alpha oscillations should remain intact)
* Decide if the denoising quality is sufficient for your analysis

## Other ZUNA Tasks

Beyond denoising, ZUNA supports two additional tasks:

### Reconstruct

Recover missing or corrupted EEG channels using the spatial relationships learned from healthy data. Useful when one electrode has poor contact.

### Upsample

Expand a low-density recording (e.g., 4-channel Muse) to estimate what a higher-density montage would look like. This is experimental and best used for exploratory visualization.

Both tasks use the same API endpoint with `task: "reconstruct"` or `task: "upsample"`. See [API Reference](/model-apis/api-reference) for details.