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Python Real-Time EEG & BCI Signal WebSocket Streaming Service

Low-latency Python WebSocket server designed for Brain-Computer Interface (BCI) applications and neural signal processing. Receives high-frequency EEG channel packets, applies digital SciPy Butterworth FIR bandpass filters (8-30Hz Alpha/Beta waves), and streams real-time feature vectors.

Zero-copy NumPy ring-buffer for continuous 250Hz multichannel arrays

SciPy signal.butter and sosfilt for zero-phase digital filtering

Interactive Prompt & Options

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GENERATION PROMPT (PRE-FILLED):Fully Editable

Configurable Blueprint Options:

EEG Sampling Frequency (Hz)

Custom Parameters / Additional Stack Requirements:

Appended directly to the LLM system prompt for tailored code output.

✨ OpenRouter LLM Engine (Llama-3.3-70B) Active

Generated Code

⚡ Instant Boilerplate

python-websocket-eeg-parser.py

1	import asyncio
2	import json
3	import numpy as np
4	from scipy.signal import butter, sosfilt
5	import websockets
6
7	SAMPLING_RATE = 250
8	CHANNELS = 8
9	sos_filter = butter(4, [8.0/125.0, 30.0/125.0], btype='band', output='sos')
10
11	async def handler(websocket):
12	async for message in websocket:
13	data = json.loads(message)
14	raw = np.array(data.get("channels", [0.0]*8))
15	filtered = sosfilt(sos_filter, raw)
16	await websocket.send(json.dumps({"filtered": filtered.tolist()}))
17
18	async def main():
19	async with websockets.serve(handler, "0.0.0.0", 8765):
20	await asyncio.Future()
21
22	if __name__ == "__main__":
23	asyncio.run(main())
24

24 lines • 650 bytespython

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