Devices & Data Formats

Supported Devices

Neurosity Crown

The Neurosity Crown is an 8-channel consumer EEG headset.

Property	Value
Channels	8
Channel Names	CP3, C3, F5, PO3, PO4, F6, C4, CP4
Sample Rate	256 Hz
Montage	International 10-20 subset
Connection	WiFi (SDK) or Bluetooth

Channel locations map to the standard 10-05 montage:

Frontal: F5, F6 (lateral frontal)
Central: C3, C4 (motor cortex, left/right)
Parietal: CP3, CP4 (centro-parietal)
Occipital: PO3, PO4 (parieto-occipital)

Muse (Muse 2, Muse S)

The Muse headbands are 4-channel consumer EEG devices.

Property	Value
Channels	4
Channel Names	TP9, AF7, AF8, TP10
Sample Rate	256 Hz
Montage	International 10-20 subset
Connection	Bluetooth (Web Bluetooth API)

Channel locations:

Temporal: TP9, TP10 (behind ears)
Frontal: AF7, AF8 (forehead, above eyebrows)

Data Formats

Input: Device CSV

The API accepts raw CSV data as a string. The CSV should contain:

A header row with channel names (and optionally a timestamp column)
Data rows with values in microvolts (µV)

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

The timestamp column is optional. If present, it’s ignored during processing — ZUNA uses the sample rate to reconstruct timing. But it’s useful for aligning with other data sources.

Output: JSON Channel Data

When output_format: "json", the response includes a channel_data object with per-channel arrays:

{
  "channel_data": {
    "TP9": [1.23, -0.45, 2.67, ...],
    "AF7": [0.89, 1.34, -0.78, ...],
    "AF8": [2.01, 0.56, 1.89, ...],
    "TP10": [-0.34, 1.12, -0.23, ...]
  }
}

Each array contains output.n_samples values in microvolts (µV).

Output: CSV

When output_format: "csv", the response includes output.csv_content:

timestamp_s,TP9,AF7,AF8,TP10
0.000000,1.230000,-0.450000,2.670000,...
0.003906,0.890000,1.340000,-0.780000,...

The timestamp_s column contains time in seconds from the start of the recording.

ZUNA Processing Details

What ZUNA Does

ZUNA (by Zyphra) is a foundation model for EEG signal processing. It was trained on large-scale EEG datasets to understand the structure of brain signals. Denoising removes:

Eye blink artifacts (frontal channels)
Muscle artifacts (jaw clenching, head movement)
Environmental noise (50/60 Hz line noise)
Electrode contact noise

While preserving:

Neural oscillations (alpha, beta, theta, gamma, delta)
Event-related potentials
Frequency-specific power patterns

Processing Pipeline

CSV → FIF: Convert CSV to MNE RawArray with standard 10-05 montage
Preprocessing: Resample to 256 Hz, bandpass filter, epoch into 5-second segments, normalize (std = 0.1)
Inference: Run ZUNA model on GPU (diffusion-based denoising)
Post-processing: Convert output tensors back to FIF, then to CSV/JSON

Key Constraints

Constraint	Value	Reason
Minimum duration	5 seconds	ZUNA epochs are 5s long
Recommended buffer	1 second	The API trims partial epochs
Sample rate	Any → 256 Hz	ZUNA resamples internally
Channel names	Standard 10-20	Required for montage positioning
Output length	≤ Input	Partial final epochs are dropped

Example: Duration Math

Input Duration	Epochs	Output Duration
5.0s	1	5.0s
6.0s	1	5.0s
10.0s	2	10.0s
12.5s	2	10.0s
30.0s	6	30.0s

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Devices & Data Formats

Devices & Data Formats

Supported Devices

Neurosity Crown

Muse (Muse 2, Muse S)

Data Formats

Input: Device CSV

Output: JSON Channel Data

Output: CSV

ZUNA Processing Details

What ZUNA Does

Processing Pipeline

Key Constraints

Example: Duration Math

​Devices & Data Formats

​Supported Devices

​Neurosity Crown

​Muse (Muse 2, Muse S)

​Data Formats

​Input: Device CSV

​Output: JSON Channel Data

​Output: CSV

​ZUNA Processing Details

​What ZUNA Does

​Processing Pipeline

​Key Constraints

​Example: Duration Math

Devices & Data Formats

Supported Devices

Neurosity Crown

Muse (Muse 2, Muse S)

Data Formats

Input: Device CSV

Output: JSON Channel Data

Output: CSV

ZUNA Processing Details

What ZUNA Does

Processing Pipeline

Key Constraints

Example: Duration Math