Synthesize brainwave
data, on demand.
Physics-based multi-channel EEG synthesis with artifact simulation, real-time topomap, and PSD validation.
Brain State8 states
Clinical Presetspatient sim
Parameters
Duration 10 s
Noise level 0.15
Amplitude (μV) 80
Channels6 active
Artifact SimulationNEW
Ocular Blink
Fp1/Fp2 spikes
Myogenic (Jaw)
T3/T4 HF burst
Line Noise
Power grid hum
Cardio (ECG)
~1 Hz QRS bleed
Waveform Output
0.00 s
0 ch
Topomapreal-time
SNR
—
dB ratio
Samples
—
@ 256 Hz
PSD Match
—
Wasserstein
Quality
—
TSTR score
Active Artifacts
None
Power Spectral DensityWelch · 512-pt FFT
Generation Log
00:00:00Axiom Neural v2.0 — engine initialized
00:00:00FFT, Welch, pink noise, artifact engine ready
Validate + Filter Sandbox
PSD matching, band-power analysis, and interactive filtering — apply notch and bandpass filters and watch the spectrum respond.
Band Power Radar
Band Power Distribution
Filter Sandbox
Notch freq
50 Hz
High-pass (Hz)
1 Hz
Low-pass (Hz)
40 Hz
PSD Overlay — Raw vs Filtered256 Hz · Welch
Wasserstein Dist.
—
lower is better
TSTR Score
—
train-synth / test-real
KL Divergence
—
distribution match
Corr. Coeff.
—
cross-channel
EEG Corpus Registry
Open-source EEG datasets compatible with the Axiom Neural signal engine.
| Dataset | Subjects | Channels | Duration | Paradigm | License | Status |
|---|
MNE-Python — Load Dataset
import mne
from mne.datasets import eegbci
raw_fnames = eegbci.load_data(1, runs=[6, 10, 14])
raw = mne.io.concatenate_raws([mne.io.read_raw_edf(f, preload=True) for f in raw_fnames])
raw.filter(1., 40.)
ica = mne.preprocessing.ICA(n_components=20, random_state=42)
ica.fit(raw)
ica.apply(raw)
df = pd.DataFrame(raw.get_data().T * 1e6, columns=raw.ch_names)
df['time_s'] = raw.times
df.to_csv('axiom_seed.csv', index=False)
Export Dataset
Download the current synthetic EEG as a portable file format.
Format
.CSV
Comma-separated. Compatible with Python, MATLAB, Excel.
.EDF (via MNE)
European Data Format. Native EEG standard.
.JSON
Structured JSON with metadata, channels, and signal arrays.
EDF Export via MNE-Python
import mne, pandas as pd
df = pd.read_csv('axiom_export.csv')
info = mne.create_info(ch_names=[c for c in df.columns if c != 'time_s'], sfreq=256, ch_types='eeg')
raw = mne.io.RawArray(df.drop('time_s', axis=1).values.T * 1e-6, info)
raw.export('axiom_export.edf', fmt='edf')
# pip install mne pyEDFlib pandas numpy
REST API Reference
FastAPI backend endpoints for programmatic EEG synthesis.
POST /api/generate200 OK
POST /api/generate
{
"state": "alpha",
"duration_s": 10,
"sfreq": 256,
"channels": ["Fp1", "Fp2", "C3", "C4"],
"noise": 0.15,
"amplitude_uv": 80,
"artifacts": ["blink", "linenoise_50hz"]
}
GET /api/presets
GET /api/presets
[
{ "id": "eyes_closed_male", "label": "24yo Male · Resting Eyes Closed", "state": "alpha" },
{ "id": "absence_seizure", "label": "45yo Female · Absence Seizure", "state": "seizure" },
...
]