Eeg MCP

Real-time EEG: BrainFlow streaming, wall-clock replay, online DSP, recording, and stimulation.

Quick Install

Automated & IDE Setup

Copy the AI prompt to install this server into Claude Code, Cursor, or another agent — or use 1-click editor setup below.

Add to Cursor Add to VS Code

Not yet automatically verified

We haven't yet run this listing's install command through our automated sandbox check. This isn't a red flag — we're steadily working through the catalog.

Manual Client & Custom JSON ConfigExpand JSON ▾

Client Config & Setup

Choose your client or environment

Target File:~/Library/Application Support/Claude/claude_desktop_config.json

claude_desktop_config.json

{
  "mcpServers": {
    "eeg-mcp": {
      "command": "uvx",
      "args": [
        "eeg-mcp"
      ]
    }
  }
}

💡 Paste the JSON block into your client's configuration file under mcpServers, then restart the application.

Documentation Overview

⚡🧠 eeg-mcp

Real-time EEG for AI agents — stream, replay, visualize, record, stimulate

Documentation · Tutorial · Hardware · Safety · Tool Reference

A Model Context Protocol server that gives an AI agent one interface over the live EEG workflow: acquisition from ~66 BrainFlow boards, wall-clock replay of existing recordings, stateful online DSP, a live browser monitor, crash-safe recording, and gated stimulation output.

The offline counterpart is neuro-mcp (MNE processing, source imaging, BIDS/EHR storage). This is the real-time half — everything that has to happen while the signal is still arriving.

Concept

mermaid

flowchart LR
    Researcher(["🔬 BCI Researcher"])
    Clinician(["🩺 Clinician"])
    Agent[["🤖 AI Agent"]]
    Server(("eeg-mcp<br/>FastMCP · 47 tools"))

    Clinician -- talks to --> Agent
    Researcher -- talks to --> Agent
    Agent -- MCP --> Server

    Server --> Acquire["Acquire<br/>66 boards · replay<br/>at true rate"]
    Server --> Process["Process<br/>stateful online DSP<br/>custom plugins"]
    Server --> Watch["Watch &amp; Record<br/>live monitor · HTML<br/>crash-safe .fif"]
    Server --> Stim["Stimulate<br/>LSL · TTL · TMS/tES<br/>3 safety gates"]

    classDef acq fill:#14b8a6,stroke:#0d9488,color:#fff
    classDef proc fill:#4f8cff,stroke:#2f5fbf,color:#fff
    classDef viz fill:#b06fe0,stroke:#7c3fae,color:#fff
    classDef stim fill:#eb5757,stroke:#b93b3b,color:#fff
    class Acquire acq
    class Process proc
    class Watch viz
    class Stim stim

Nobody calls a tool by hand — you talk to an agent in plain English and it drives the 47 tools underneath. The tutorial shows what that looks like end to end, with no hardware required.

What it does

📡 Stream

66 BrainFlow board identifiers — OpenBCI, Muse, ANT Neuro, g.tec, Mentalab and more — plus a synthetic board that needs no hardware. Samples land in a ring buffer filled by a background thread, so tool calls read a live view instead of blocking on a device.

⏪ Replay

Play an EDF/BDF/GDF/SET/FIF or BrainFlow CSV at the rate it was recorded, re-emitting annotations as events at their original timings. Adds speed, seek, pause and looping. A pipeline developed against a file runs unchanged against hardware.

👁 Visualize

A loopback-bound, token-gated live browser view: rolling traces, event markers, band power, per-electrode quality, and transport controls. Plus self-contained HTML reports — no CDN, no external assets, opens on an air-gapped machine.

💾 Record

Write continuously to MNE-native .fif with the event log attached as annotations, plus a metadata row in a store schema-compatible with neuro-mcp. Crash-safe: an interrupted session is recoverable.

🧩 Extend

Plug in your own real-time processor — feature extractor, classifier, artifact gate, or EEG tokenizer for sequence models — and it runs on the same footing as the built-ins, inside the acquisition loop. → Extending

⚡ Stimulate

One send_stim_event contract over pluggable backends: LSL for software, BrainFlow's marker channel for sample-aligned embedding, serial/TTL and templated ASCII for hardware including TMS and tES — behind three safety gates.

[!NOTE] One event log. Board markers, replayed annotations, dispatched stimulations and manual notes all land in the same table on the same clock, with absolute sample indices. A closed-loop run reconstructs afterwards with no clock join.

Install

bash

conda create -n eeg-mcp python=3.11 -y && conda activate eeg-mcp
pip install eeg-mcp

Optional extras and MCP client registration

Terminal

pip install "eeg-mcp[lsl]"      # LSL marker outlets (PsychoPy, OpenViBE, ...)
pip install "eeg-mcp[serial]"   # serial/TTL trigger delivery to hardware

config.json

{
  "mcpServers": {
    "eeg-realtime": {
      "command": "/path/to/envs/eeg-mcp/bin/python",
      "args": ["-m", "eeg_mcp"]
    }
  }
}

Or with the Claude Code CLI:

Terminal

claude mcp add eeg-realtime -- /path/to/envs/eeg-mcp/bin/python -m eeg_mcp

→ Full guide: Installation

Quick start

Ask your agent for the outcome; it picks the calls. No hardware required:

python

start_stream(session_id="s1", board="synthetic")
check_signal_quality(session_id="s1")          # before trusting anything
set_filters(session_id="s1", bandpass_low=1, bandpass_high=40, notch_freq=50)
get_band_power(session_id="s1", seconds=2)
start_monitor(session_id="s1")                 # → open the returned URL

Replay a real recording as if it were live, then keep the record:

python

inspect_recording(path="sub-04_rest.edf")
start_replay(session_id="r1", path="sub-04_rest.edf", speed=1.0)
get_events(session_id="r1", origin="annotation")
export_report(session_id="r1", notes="Routine review.")

mermaid

flowchart LR
    A["start_stream<br/><i>or</i> start_replay"] --> B[check_signal_quality]
    B --> C[set_filters]
    C --> D["get_band_power<br/>get_psd"]
    C --> E[start_monitor]
    C --> P[attach_processor]
    A --> R[start_recording]
    D --> S[send_stim_event]
    P --> S
    R --> X[stop_recording]
    S --> X
    E --> X
    X --> Z[stop_stream]

    classDef hot fill:#14b8a6,stroke:#0d9488,color:#fff
    class A,X hot

Documentation

Guide
🚀 Installation	Environment, client registration, troubleshooting
📘 Tutorial	End to end, no hardware needed
⏪ Replay-Driven Development	Build against a recording, deploy live
🔁 Closed-Loop Neurofeedback	Feature → trigger, with a measured latency budget
🩺 Live Clinical Review	Visual review, annotation, reporting
⚡ Stimulation Protocols	TMS and tES through the safety gates
🧩 Extending	Write a custom processor or EEG tokenizer
🔌 Supported Hardware	All 66 boards, formats, stimulation transports
⚠️ Safety	Read before connecting a stimulator
🛠 Tool Reference	All 47 tools

The one design decision worth knowing

[!IMPORTANT] Filtering happens in the producer thread, not at query time.

A stateful IIR filter must see every sample exactly once, in order. The common shortcut — filtering each query window independently — restarts the filter at every window boundary and injects a transient each time. It is invisible in a band-power plot and fatal for anything phase-sensitive.

So the producer filters each chunk once as it arrives, carrying sosfilt delay-line state forward, and writes to a second ring buffer. Queries just read.

mermaid

flowchart LR
    BF[Board / Recording] -->|poll| PR{{Producer thread}}
    PR -->|raw chunk| RB[(Raw ring buffer)]
    PR -->|stateful sosfilt| FB[(Filtered ring buffer)]
    PR -->|markers &amp; annotations| EL[(Event log)]
    PR -->|append| DISK[(.fif on disk)]
    PR -->|streaming| PL[Your processors]
    RB & FB & EL --> Q[MCP tools]

    classDef hot fill:#14b8a6,stroke:#0d9488,color:#fff
    class PR hot

The test suite asserts chunked filtering matches whole-signal filtering to 1e-9, and asserts as a control that the naive approach does not.

Consequence
Filters are causal	No zero-phase option — that needs future samples. `stream_status` reports `group_delay_sec`
Both buffers are kept	`read_window(filtered=false)` always gets raw signal, to check whether a feature is real or an artifact
Indices are shared	An event's `sample_index` means the same thing in either buffer

[!TIP] Budget a closed loop as group delay + poll interval + dispatch latency — measured at ~71 ms in the reference configuration. Good for neurofeedback; not adequate for phase-locked stimulation.

Stimulation safety

Read the full README →View source on GitHub →

Related MCP Servers

View all in Developer Tools View all alternatives

Adoption & maintenance

Factual signals from GitHub, npm, and our automated checks — not a rating.

GitHub stars

Stargazers on the source repository.

Last commit

2mo ago

Most recent push to the default branch.

Directory activity

2 views

Config copies, upvotes, and views on AllMCPs.

Reviews

No reviews yet — be the first to share how this listing worked for you.

Frequently Asked Questions about Eeg MCP

Add the following block to your claude_desktop_config.json under mcpServers: "mcpServers": { "eeg-mcp": { "command": "uvx", "args": ["eeg-mcp"] } }

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Technical Specs & Signals

CategoryDeveloper Tools

11/12 checks healthy over the last 45d

39Quality signal: Fair · 39/100How this signal is calculated ▾

Server availabilityNot measured

Not scored for repo-hosted servers — we can't reach the running server, only its GitHub page. Hosted MCP endpoints are health-checked live.

Verified ownership10/20

Documentation & tools16/30

Adoption & activity3/15

Community engagement0/10

A guidance signal from public completeness & health data — not a user rating. New listings start lower and rise as they add docs, get verified, and grow adoption. Signals we can't observe for a listing are skipped, not counted against it.

Supply-chain signal

No high-severity advisories surfaced by our automated scan.

Critical 0High 0Medium 0Low 0

Scanned 4d ago via OSV.dev · eeg-mcp (PyPI)

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More in 💻 Developer Tools →Best MCP servers for Developers →Alternatives to Eeg MCP →Install in Claude Desktop Install in Cursor Install in VS Code Setup guides for all 13 MCP clients

Eeg MCP

Real-time EEG: BrainFlow streaming, wall-clock replay, online DSP, recording, and stimulation.

Quick Install

Automated & IDE Setup

Copy the AI prompt to install this server into Claude Code, Cursor, or another agent — or use 1-click editor setup below.

Add to Cursor Add to VS Code

Not yet automatically verified

We haven't yet run this listing's install command through our automated sandbox check. This isn't a red flag — we're steadily working through the catalog.

Manual Client & Custom JSON ConfigExpand JSON ▾

Client Config & Setup

Choose your client or environment

Target File:~/Library/Application Support/Claude/claude_desktop_config.json

claude_desktop_config.json

{
  "mcpServers": {
    "eeg-mcp": {
      "command": "uvx",
      "args": [
        "eeg-mcp"
      ]
    }
  }
}

💡 Paste the JSON block into your client's configuration file under mcpServers, then restart the application.

Documentation Overview

⚡🧠 eeg-mcp

Real-time EEG for AI agents — stream, replay, visualize, record, stimulate

Documentation · Tutorial · Hardware · Safety · Tool Reference

The offline counterpart is neuro-mcp (MNE processing, source imaging, BIDS/EHR storage). This is the real-time half — everything that has to happen while the signal is still arriving.

Concept

mermaid

flowchart LR
    Researcher(["🔬 BCI Researcher"])
    Clinician(["🩺 Clinician"])
    Agent[["🤖 AI Agent"]]
    Server(("eeg-mcp<br/>FastMCP · 47 tools"))

    Clinician -- talks to --> Agent
    Researcher -- talks to --> Agent
    Agent -- MCP --> Server

    Server --> Acquire["Acquire<br/>66 boards · replay<br/>at true rate"]
    Server --> Process["Process<br/>stateful online DSP<br/>custom plugins"]
    Server --> Watch["Watch &amp; Record<br/>live monitor · HTML<br/>crash-safe .fif"]
    Server --> Stim["Stimulate<br/>LSL · TTL · TMS/tES<br/>3 safety gates"]

    classDef acq fill:#14b8a6,stroke:#0d9488,color:#fff
    classDef proc fill:#4f8cff,stroke:#2f5fbf,color:#fff
    classDef viz fill:#b06fe0,stroke:#7c3fae,color:#fff
    classDef stim fill:#eb5757,stroke:#b93b3b,color:#fff
    class Acquire acq
    class Process proc
    class Watch viz
    class Stim stim

Nobody calls a tool by hand — you talk to an agent in plain English and it drives the 47 tools underneath. The tutorial shows what that looks like end to end, with no hardware required.

What it does

📡 Stream

⏪ Replay

👁 Visualize

💾 Record

🧩 Extend

⚡ Stimulate

[!NOTE] One event log. Board markers, replayed annotations, dispatched stimulations and manual notes all land in the same table on the same clock, with absolute sample indices. A closed-loop run reconstructs afterwards with no clock join.

Install

bash

conda create -n eeg-mcp python=3.11 -y && conda activate eeg-mcp
pip install eeg-mcp

Optional extras and MCP client registration

Terminal

pip install "eeg-mcp[lsl]"      # LSL marker outlets (PsychoPy, OpenViBE, ...)
pip install "eeg-mcp[serial]"   # serial/TTL trigger delivery to hardware

config.json

{
  "mcpServers": {
    "eeg-realtime": {
      "command": "/path/to/envs/eeg-mcp/bin/python",
      "args": ["-m", "eeg_mcp"]
    }
  }
}

Or with the Claude Code CLI:

Terminal

claude mcp add eeg-realtime -- /path/to/envs/eeg-mcp/bin/python -m eeg_mcp

→ Full guide: Installation

Quick start

Ask your agent for the outcome; it picks the calls. No hardware required:

python

start_stream(session_id="s1", board="synthetic")
check_signal_quality(session_id="s1")          # before trusting anything
set_filters(session_id="s1", bandpass_low=1, bandpass_high=40, notch_freq=50)
get_band_power(session_id="s1", seconds=2)
start_monitor(session_id="s1")                 # → open the returned URL

Replay a real recording as if it were live, then keep the record:

python

inspect_recording(path="sub-04_rest.edf")
start_replay(session_id="r1", path="sub-04_rest.edf", speed=1.0)
get_events(session_id="r1", origin="annotation")
export_report(session_id="r1", notes="Routine review.")

mermaid

flowchart LR
    A["start_stream<br/><i>or</i> start_replay"] --> B[check_signal_quality]
    B --> C[set_filters]
    C --> D["get_band_power<br/>get_psd"]
    C --> E[start_monitor]
    C --> P[attach_processor]
    A --> R[start_recording]
    D --> S[send_stim_event]
    P --> S
    R --> X[stop_recording]
    S --> X
    E --> X
    X --> Z[stop_stream]

    classDef hot fill:#14b8a6,stroke:#0d9488,color:#fff
    class A,X hot

Documentation

Guide
🚀 Installation	Environment, client registration, troubleshooting
📘 Tutorial	End to end, no hardware needed
⏪ Replay-Driven Development	Build against a recording, deploy live
🔁 Closed-Loop Neurofeedback	Feature → trigger, with a measured latency budget
🩺 Live Clinical Review	Visual review, annotation, reporting
⚡ Stimulation Protocols	TMS and tES through the safety gates
🧩 Extending	Write a custom processor or EEG tokenizer
🔌 Supported Hardware	All 66 boards, formats, stimulation transports
⚠️ Safety	Read before connecting a stimulator
🛠 Tool Reference	All 47 tools

The one design decision worth knowing

[!IMPORTANT] Filtering happens in the producer thread, not at query time.

So the producer filters each chunk once as it arrives, carrying sosfilt delay-line state forward, and writes to a second ring buffer. Queries just read.

mermaid

flowchart LR
    BF[Board / Recording] -->|poll| PR{{Producer thread}}
    PR -->|raw chunk| RB[(Raw ring buffer)]
    PR -->|stateful sosfilt| FB[(Filtered ring buffer)]
    PR -->|markers &amp; annotations| EL[(Event log)]
    PR -->|append| DISK[(.fif on disk)]
    PR -->|streaming| PL[Your processors]
    RB & FB & EL --> Q[MCP tools]

    classDef hot fill:#14b8a6,stroke:#0d9488,color:#fff
    class PR hot

The test suite asserts chunked filtering matches whole-signal filtering to 1e-9, and asserts as a control that the naive approach does not.

Consequence
Filters are causal	No zero-phase option — that needs future samples. `stream_status` reports `group_delay_sec`
Both buffers are kept	`read_window(filtered=false)` always gets raw signal, to check whether a feature is real or an artifact
Indices are shared	An event's `sample_index` means the same thing in either buffer

[!TIP] Budget a closed loop as group delay + poll interval + dispatch latency — measured at ~71 ms in the reference configuration. Good for neurofeedback; not adequate for phase-locked stimulation.

Stimulation safety

Read the full README →View source on GitHub →

Related MCP Servers

View all in Developer Tools View all alternatives

Adoption & maintenance

Factual signals from GitHub, npm, and our automated checks — not a rating.

GitHub stars

Stargazers on the source repository.

Last commit

2mo ago

Most recent push to the default branch.

Directory activity

2 views

Config copies, upvotes, and views on AllMCPs.

Reviews

No reviews yet — be the first to share how this listing worked for you.

Frequently Asked Questions about Eeg MCP

Add the following block to your claude_desktop_config.json under mcpServers: "mcpServers": { "eeg-mcp": { "command": "uvx", "args": ["eeg-mcp"] } }

AllMCPs Directory Badge

Full Badge Customizer

Showcase your server listing on GitHub or your project documentation. Embed this dynamic SVG badge to highlight official listing status and live engagement.

Badge Style:

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Markdown (GitHub README)

[![AllMCPs](https://allmcps.com/api/badge/eeg-mcp?style=directory)](https://allmcps.com/mcp/eeg-mcp)

HTML Embed

<a href="https://allmcps.com/mcp/eeg-mcp"><img src="https://allmcps.com/api/badge/eeg-mcp?style=directory" alt="Eeg MCP on AllMCPs" /></a>

Technical Specs & Signals

CategoryDeveloper Tools

11/12 checks healthy over the last 45d

39Quality signal: Fair · 39/100How this signal is calculated ▾

Server availabilityNot measured

Not scored for repo-hosted servers — we can't reach the running server, only its GitHub page. Hosted MCP endpoints are health-checked live.

Verified ownership10/20

Documentation & tools16/30

Adoption & activity3/15

Community engagement0/10

Supply-chain signal

No high-severity advisories surfaced by our automated scan.

Critical 0High 0Medium 0Low 0

Scanned 4d ago via OSV.dev · eeg-mcp (PyPI)

★ Spotlight Slot

Feature Your MCP Server

Get maximum visibility for your server across our directory, search results, and detail pages.

Spotlight Your Server

Own this project?

Free dofollow backlink: add your website and place the AllMCPs badge on it — no claim needed. We detect it automatically and keep it verified as long as the badge stays live.

Claim & get free dofollow

Share & Embed

Add our SVG badge (dark/light directory styles) or embeddable widget to your site.

Explore more

More in 💻 Developer Tools →Best MCP servers for Developers →Alternatives to Eeg MCP →Install in Claude Desktop Install in Cursor Install in VS Code Setup guides for all 13 MCP clients