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/.

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"
}

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
}

VM Status

GET /api/eeg-models/vm-status

Response:

{
  "status": "running",
  "vmStatus": "running"
}

Possible vmStatus values: running, deallocated, starting, stopping, deallocating

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

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"
}

Field	Type	Required	Description
`task`	string	✅	`"denoise"`, `"reconstruct"`, or `"upsample"`
`eeg.csv_content`	string	✅	Raw CSV data with channel columns (values in µV)
`eeg.device_type`	string	✅	`"neurosity"`, `"muse"`, or `"auto"`
`eeg.sfreq`	number		Sampling frequency in Hz (default: 256)
`eeg.channel_names`	string[]		Override auto-detected channel names
`output_format`	string		`"json"` (default) or `"csv"`
`target_channels`	string[]		For `upsample` task: target channel names
`bad_channels`	string[]		Channels to exclude from processing
`gpu_device`	number		GPU device index (default: 0)

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, ...],
    ...
  }
}

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..."
  }
}

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
}

CSV Input Format

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

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
...

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.

Error Responses

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

Code Examples

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`);

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']}")

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"
  }'

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API Reference

API Reference

Health Check

Wake GPU VM

VM Status

Run ZUNA Inference

Request Body

Response (JSON format)

Response (CSV format)

Response (HTTP 202 — GPU warming up)

CSV Input Format

Neurosity Crown

Muse

Error Responses

Code Examples

JavaScript/TypeScript

Python

cURL

​API Reference

​Health Check

​Wake GPU VM

​VM Status

​Run ZUNA Inference

​Request Body

​Response (JSON format)

​Response (CSV format)

​Response (HTTP 202 — GPU warming up)

​CSV Input Format

​Neurosity Crown

​Muse

​Error Responses

​Code Examples

​JavaScript/TypeScript

​Python

​cURL

API Reference

Health Check

Wake GPU VM

VM Status

Run ZUNA Inference

Request Body

Response (JSON format)

Response (CSV format)

Response (HTTP 202 — GPU warming up)

CSV Input Format

Neurosity Crown

Muse

Error Responses

Code Examples

JavaScript/TypeScript

Python

cURL