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Table of Contents

• Quick Start
• Connect to AI Assistants
• Example Conversations
• Tool Reference
• Architecture
• Reference Scripts
• Development
• About Neuroelectrics
• License

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Quick Start

pip install ne-mcp-eeg

Or, to install from source:

git clone https://github.com/giulioruffini/ne-mcp-eeg.git
cd ne-mcp-eeg
python3 -m venv .venv
source .venv/bin/activate   # On Windows: .venv\Scripts\activate
pip install -e .

Run the server:

ne-eeg-server
# or: python -m ne_eeg_server.server

The server communicates over stdio by default (the standard transport for Claude Desktop). For web-based agents, SSE transport is also available — see Connect to AI Assistants.

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Connect to AI Assistants

Claude Desktop

Add to your Claude Desktop configuration file:

Platform	Config path
macOS	~/Library/Application Support/Claude/claude_desktop_config.json
Windows	%APPDATA%\Claude\claude_desktop_config.json

Point to the Python binary inside your virtual environment so Claude Desktop can launch it without activating the venv:

{
  "mcpServers": {
    "ne-mcp-eeg": {
      "command": "/absolute/path/to/ne-mcp-eeg/.venv/bin/python",
      "args": ["-m", "ne_eeg_server.server"]
    }
  }
}

Tip: Run which python with the venv activated to get the exact path.

Restart Claude Desktop after editing the config. The six EEG analysis tools will appear in the tool picker.

Claude Code

claude mcp add ne-mcp-eeg -- /path/to/ne-mcp-eeg/.venv/bin/python -m ne_eeg_server.server

ChatGPT

ChatGPT supports MCP servers via the Actions feature with SSE transport. Start the server in SSE mode:

ne-eeg-server --transport sse --port 8080

Then configure a Custom GPT action pointing to http://localhost:8080/sse. See the OpenAI MCP documentation for setup details.

Google Gemini

Gemini supports MCP servers in Google AI Studio and via the Gemini API. Start the server in SSE mode:

ne-eeg-server --transport sse --port 8080

In Google AI Studio, add an MCP tool server with the URL http://localhost:8080/sse. The six EEG analysis tools will appear automatically. For the Gemini API, connect via the MCP client SDK — see Gemini MCP documentation.

Other MCP Clients

Any MCP-compatible client can connect to this server using one of two transports:

Transport	Use case	Command
stdio	Local desktop apps (Claude Desktop, Claude Code, Cursor)	python -m ne_eeg_server.server
SSE	Web-based clients, remote agents (ChatGPT, Gemini, custom)	ne-eeg-server --transport sse --port 8080

For stdio, the client launches the server process directly. For SSE, you run the server first and point the client to the HTTP endpoint.

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Example Conversations

Once connected, try these prompts with any MCP-compatible AI assistant:

1. "What's in /path/to/recording.easy?" Returns file metadata: format, device, channels (with 10-20 labels), sampling rate, duration, file size.

2. "Show me the events in /path/to/recording.nedf" Extracts all markers/triggers with timestamps and event codes — useful for identifying EO/EC paradigms, stimulus onsets, or protocol segments.

3. "Check the signal quality of /path/to/recording.easy" Runs per-channel QC: RMS (raw, notch-filtered, fully filtered), kurtosis, over-threshold event rate, 50/60 Hz line power, suspicious PSD peaks. Each channel gets a PASS/FAIL flag with specific reasons.

4. "Analyze the EEG in /path/to/recording.easy — focus on channels O1, O2, Pz" Full spectral analysis on selected channels: PSD via Welch's method, absolute and relative band powers (delta through gamma), peak alpha frequency, alpha/theta ratio, per-channel QC, and an inline PSD plot.

5. "Generate a QC report for /path/to/recording.nedf" Produces a branded PDF with per-channel metrics table (color-coded PASS/FAIL), PSD plots (8 channels per page), raw and filtered time-series.

6. "Create a full analysis report for /path/to/recording.easy" Produces a branded PDF with raw EEG traces overlaid with event markers, PSD overlay plot, band power heatmap, alpha reactivity (Eyes Open vs Eyes Closed), alpha/theta ratio bars, spectral summary table, frontal alpha asymmetry, and per-channel statistics.

Supported formats: .easy (NE native ASCII), .easy.gz (compressed), .nedf (NE binary with XML header), .edf (European Data Format). All formats are transparently normalized for processing — no manual conversion needed.

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Tool Reference

All six tools operate on local EEG files on disk. There is no cloud access, no patient database, no stimulation functionality.

Tool	Description	Required	Optional
file_info	Extract file metadata (format, channels, sample rate, duration, device)	file_path	—
list_events	Extract markers/events with timestamps and codes	file_path	start_time_s, duration_s
signal_quality	Per-channel QC metrics with PASS/FAIL flags	file_path	channels, start_time_s, duration_s
analyze_eeg	Spectral analysis: PSD, band powers, ratios, peak alpha + inline plot	file_path	channels, start_time_s, duration_s
generate_qc_report	Generate branded PDF signal quality report	file_path	output_path, start_time_s, duration_s
generate_analysis_report	Generate branded PDF spectral/functional analysis report	file_path	output_path, start_time_s, duration_s

QC Metrics (signal_quality & analyze_eeg)

Each channel is assessed against configurable thresholds:

Metric	Description	Default Threshold
RMS raw	Root mean square of demeaned signal (µV)	0.01 – 1.5 µV
RMS notch	RMS after 50/60 Hz notch filter (µV)	< 1.0 µV
RMS filtered	RMS after 1–40 Hz bandpass + notch (µV)	< 0.5 µV
Kurtosis	Distribution peakedness on notch-filtered data	< 5.0
Event rate	Over-threshold events per second	< 10.0 /s
Line power	PSD at 50/60 Hz in dB	< 1.2 dB
PSD peak SNR	Suspicious narrowband peak SNR (dB above baseline)	< 12.0 dB

A channel fails if any metric exceeds its threshold.

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Architecture

+-----------------------------+
|   AI Agent                  |
|   (Claude, ChatGPT, Gemini) |
+--------------+--------------+
               |
               | MCP Protocol (stdio or SSE)
               |
+--------------+--------------+
|   ne-mcp-eeg Server         |
|   ne_eeg_server.server       |
+---------+----------+---------+
          |          |
          v          v
   +----------+  +--------+
   | Readers  |  |Analysis|
   | .easy    |  | Filter |
   | .nedf    |  | PSD    |
   | .edf     |  | QC     |
   +----------+  +--------+
          |          |
          v          v
   +-----------+  +----------+
   | Local EEG |  | PDF      |
   | Files     |  | Reports  |
   +-----------+  +----------+

The server operates entirely locally. EEG files never leave the machine — all signal processing (1–40 Hz bandpass, 50/60 Hz notch, Welch PSD, QC metrics) runs in-process using NumPy and SciPy.

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Reference Scripts

The reference_scripts/ directory contains standalone Python scripts demonstrating the traditional Neuroelectrics EEG workflow — the manual approach that the MCP server modernizes:

Script	What it does
load_easy_file.py	Load .easy + .info, parse metadata, access channels by 10-20 label
compute_psd.py	Power spectral density via Welch's method with frequency band shading
quality_check.py	Per-channel quality assessment (flatline, noise, line noise, clipping)
band_power.py	Absolute/relative band powers and clinical ratios (alpha/theta, delta/alpha, beta/alpha)

cd reference_scripts
python load_easy_file.py
python compute_psd.py
python quality_check.py
python band_power.py

Includes a synthetic 8-channel, 10-second demo recording (sample_data/demo_recording.easy) with alpha rhythm in occipital channels and frontal theta.

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Development

Setup

python3 -m venv .venv
source .venv/bin/activate
pip install -e ".[dev]"

Testing

pytest -v

All 16 tests cover readers, tool implementations, server configuration, and security (no stimulation/patient/protocol content).

SSE Transport

pip install starlette uvicorn
ne-eeg-server --transport sse --port 8080

Dependencies

Package	Purpose
mcp	Model Context Protocol SDK
pydantic	Data validation and serialization
numpy	Numerical computation
scipy	Signal processing (filtering, PSD, peak detection)
matplotlib	PSD and time-series plots
pandas	Tabular data handling for .easy files
reportlab	PDF report generation
pyedflib	EDF file reading

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About Neuroelectrics

Neuroelectrics (Barcelona) develops medical-grade brain monitoring and stimulation devices used in clinical trials and neuroscience research worldwide.

• Enobio — Wireless EEG system, up to 32 channels, 24-bit resolution, 500 S/s
• Starstim — Hybrid EEG + transcranial electrical stimulation (tDCS/tACS/tRNS)

This server focuses exclusively on the EEG recording and analysis capabilities of these devices.

Related tools:

• NEPy — Python toolbox for offline .easy and .nedf file processing
• ne-mcp — Full Neuroelectrics MCP server (internal, includes stimulation and platform tools)

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License

Apache 2.0