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API Reference

All endpoints are available through the NeuroFusion backend at https://usefusion.ai/api/eeg-models/ or directly on the GPU server at https://models.usefusion.ai/.

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Health Check

Check GPU availability before sending inference requests.
GET /api/eeg-models/health
Response (GPU running): 
{
  "status": "ok",
  "model": "zuna",
  "zuna_available": true,
  "gpu_name": "NVIDIA A100 80GB PCIe"
}
Response (VM deallocated): 
{
  "status": "unavailable",
  "vm": { "status": "deallocated" },
  "message": "GPU VM is not running"
}

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Wake GPU VM

Start the GPU VM if it’s deallocated. The VM takes ~90 seconds to boot. 
POST /api/eeg-models/wake
Response: 
{
  "status": "starting",
  "message": "GPU VM is starting up",
  "estimatedWaitSeconds": 90
}

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VM Status

GET /api/eeg-models/vm-status
Response: 
{
  "status": "running",
  "vmStatus": "running"
}
Possible vmStatus values: running, deallocated, starting, stopping, deallocating

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Run ZUNA Inference

If the GPU VM is deallocated, this returns HTTP 202 with a retry instruction. Check health first or handle the 202 response.
POST /api/eeg-models/inference

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Request Body

{
  "task": "denoise",
  "eeg": {
    "csv_content": "timestamp,CP3,C3,F5,...\n1700000000,12.5,-3.2,8.1,...\n...",
    "device_type": "neurosity",
    "sfreq": 256
  },
  "output_format": "json"
}
FieldTypeRequiredDescription
taskstring✅"denoise", "reconstruct", or "upsample"
eeg.csv_contentstring✅Raw CSV data with channel columns (values in µV)
eeg.device_typestring✅"neurosity", "muse", or "auto"
eeg.sfreqnumberSampling frequency in Hz (default: 256)
eeg.channel_namesstring[]Override auto-detected channel names
output_formatstring"json" (default) or "csv"
target_channelsstring[]For upsample task: target channel names
bad_channelsstring[]Channels to exclude from processing
gpu_devicenumberGPU device index (default: 0)

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Response (JSON format)

{
  "task": "denoise",
  "model": "zuna",
  "input": {
    "channel_names": ["CP3", "C3", "F5", "PO3", "PO4", "F6", "C4", "CP4"],
    "n_channels": 8,
    "sfreq": 256.0,
    "duration_seconds": 5.996,
    "n_samples": 1536
  },
  "output": {
    "channel_names": ["CP3", "C3", "F5", "PO3", "PO4", "F6", "C4", "CP4"],
    "n_channels": 8,
    "sfreq": 256.0,
    "duration_seconds": 4.996,
    "n_samples": 1280,
    "format": "json"
  },
  "channel_data": {
    "CP3": [1.23, -0.45, 2.67, ...],
    "C3": [0.89, 1.34, -0.78, ...],
    ...
  }
}

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Response (CSV format)

{
  "task": "denoise",
  "model": "zuna",
  "input": { ... },
  "output": {
    ...
    "format": "csv",
    "csv_content": "timestamp_s,CP3,C3,F5,...\n0.0,1.23,0.89,...\n..."
  }
}

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Response (HTTP 202 — GPU warming up)

{
  "status": "warming_up",
  "vmAction": "starting",
  "message": "GPU VM is starting up. Please retry in ~90 seconds.",
  "estimatedWaitSeconds": 90,
  "retryAfter": 15
}

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CSV Input Format

Your CSV should have columns for each EEG channel. An optional timestamp column is supported but not required.

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Neurosity Crown

timestamp,CP3,C3,F5,PO3,PO4,F6,C4,CP4
1700000000.000,12.5,-3.2,8.1,5.6,-7.3,2.1,9.4,-1.8
1700000000.004,13.1,-2.8,7.9,5.2,-6.9,2.5,9.1,-2.1
...

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Muse

timestamp,TP9,AF7,AF8,TP10
1700000000.000,15.2,-4.1,6.3,-8.7
1700000000.004,14.8,-3.9,6.1,-8.2
...
Important: Values should be in microvolts (µV). The API automatically converts to volts for MNE processing and back to µV for the output.

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Error Responses

StatusMeaning
400Invalid request (missing fields, bad task, insufficient duration)
404Dataset not found (for analyze-dataset endpoint)
500Model inference failed
502GPU server unreachable
503EEG Models API not configured

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Code Examples

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JavaScript/TypeScript

import { api } from "~/config/api";

const response = await api.post("/eeg-models/inference", {
  task: "denoise",
  eeg: {
    csv_content: csvString,
    device_type: "neurosity",
    sfreq: 256,
  },
  output_format: "json",
});

const { channel_data, output } = response.data;
console.log(`Denoised ${output.n_channels} channels, ${output.n_samples} samples`);

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Python

import requests

response = requests.post("https://models.usefusion.ai/inference/zuna", json={
    "task": "denoise",
    "eeg": {
        "csv_content": open("recording.csv").read(),
        "device_type": "muse",
        "sfreq": 256,
    },
    "output_format": "json",
})

result = response.json()
print(f"Channels: {result['output']['channel_names']}")
print(f"Samples: {result['output']['n_samples']}")

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cURL

curl -X POST https://models.usefusion.ai/inference/zuna \
  -H "Content-Type: application/json" \
  -d '{
    "task": "denoise",
    "eeg": {
      "csv_content": "timestamp,TP9,AF7,AF8,TP10\n...",
      "device_type": "muse",
      "sfreq": 256
    },
    "output_format": "json"
  }'