[{"_1":2,"_353":-5,"_354":-5},"loaderData",{"_3":4,"_22":23,"_270":271},"root",{"_5":6,"_7":8},"toasts",[],"visitor",{"_9":10,"_11":12},"viewportWidth",1280,"visitorSession",{"_13":14,"_16":17,"_18":-5,"_19":20,"_21":-5},"attributes",[15],"bot","id",21005410,"membership","referenceId","019a17bf-377c-71fd-947e-fd17d1ca8e82","userAccount","routes/_public/mcp/servers/~namespace/~slug/_pages/_layout",{"_24":25,"_66":-5,"_67":26,"_27":68,"_186":187,"_269":170},"availableLocales",[26,38,45,52,59],{"_27":28,"_31":35,"_36":37},"mcpServer",{"_29":30,"_31":32,"_33":34},"description","A framework that integrates Brain-Computer Interface technology with the Model Context Protocol to enable real-time neural signal processing and AI-powered interactions for healthcare, accessibility, and research applications.","name","BCI-MCP Server","readme","# Brain-Computer Interface with Model Context Protocol (BCI-MCP)\n\nThis project integrates Brain-Computer Interface (BCI) technology with the Model Context Protocol (MCP) to create a powerful framework for neural signal acquisition, processing, and AI-enabled interactions.\n\n[![GitHub Pages](https://img.shields.io/badge/docs-GitHub%20Pages-blue)](https://enkhbold470.github.io/bci-mcp/)\n[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)\n\n## Overview\n\nBCI-MCP combines:\n\n- **Brain-Computer Interface (BCI)**: Real-time acquisition and processing of neural signals\n- **Model Context Protocol (MCP)**: Standardized AI communication interface \n\nThis integration enables advanced applications in healthcare, accessibility, research, and human-computer interaction.\n\n## Key Features\n\n### BCI Core Features\n\n- **Neural Signal Acquisition**: Capture electrical signals from brain activity in real-time\n- **Signal Processing**: Preprocess, extract features, and classify brain signals\n- **Command Generation**: Convert interpreted brain signals into commands\n- **Feedback Mechanisms**: Provide feedback to help users improve control\n- **Real-time Operation**: Process brain activity with minimal delay\n\n### MCP Integration Features\n\n- **Standardized Context Sharing**: Connect BCI data with AI models using MCP\n- **Tool Exposure**: Make BCI functions available to AI applications\n- **Composable Workflows**: Build complex operations combining BCI signals and AI processing\n- **Secure Data Exchange**: Enable privacy-preserving neural data transmission\n\n## System Architecture\n\nThe BCI-MCP system consists of several key components:\n\n```\n┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐\n│ │ │ │ │ │\n│ BCI Hardware │──────│ BCI Software │──────│ MCP Server │\n│ │ │ │ │ │\n└─────────────────┘ └─────────────────┘ └────────┬────────┘\n │\n │\n ┌────────▼────────┐\n │ │\n │ AI Applications │\n │ │\n └─────────────────┘\n```\n\n## Getting Started\n\n### Prerequisites\n\n- Python 3.10+\n- Compatible EEG hardware (or use simulated mode for testing)\n- Additional dependencies listed in requirements.txt\n\n### Installation\n\n```bash\n# Clone the repository\ngit clone https://github.com/enkhbold470/bci-mcp.git\ncd bci-mcp\n\n# Create a virtual environment\npython -m venv venv\nsource venv/bin/activate # On Windows: venv\\Scripts\\activate\n\n# Install dependencies\npip install -r requirements.txt\n```\n\n### Using Docker\n\nFor easier setup, you can use Docker:\n\n```bash\n# Build and start all services\ndocker-compose up -d\n\n# Access the documentation at http://localhost:8000\n# The MCP server will be available at ws://localhost:8765\n```\n\n### Basic Usage\n\n```bash\n# Start the MCP server\npython src/main.py --server\n\n# Or use the interactive console\npython src/main.py --interactive\n\n# List available EEG devices\npython src/main.py --list-ports\n\n# Record a 60-second BCI session\npython src/main.py --port /dev/tty.usbmodem1101 --record 60\n```\n\n## Advanced Applications\n\nThe BCI-MCP integration enables a range of cutting-edge applications:\n\n### Healthcare and Accessibility\n\n- **Assistive Technology**: Enable individuals with mobility impairments to control devices\n- **Rehabilitation**: Support neurological rehabilitation with real-time feedback\n- **Diagnostic Tools**: Aid in diagnosing neurological conditions\n\n### Research and Development\n\n- **Neuroscience Research**: Facilitate studies of brain function and cognition\n- **BCI Training**: Accelerate learning and adaptation to BCI control\n- **Protocol Development**: Establish standards for neural data exchange\n\n### AI-Enhanced Interfaces\n\n- **Adaptive Interfaces**: Interfaces that adjust based on neural signals and AI assistance\n- **Intent Recognition**: Better understanding of user intent through neural signals\n- **Augmentative Communication**: Enhanced communication for individuals with speech disabilities\n\n## Documentation\n\nThe project documentation is hosted on GitHub Pages at: [https://enkhbold470.github.io/bci-mcp/](https://enkhbold470.github.io/bci-mcp/)\n\n### Maintaining the Documentation\n\nThe documentation is built using MkDocs with the Material theme. To update the documentation:\n\n1. Make changes to the Markdown files in the `docs/` directory on the `main` branch\n2. Commit and push your changes to the `main` branch\n3. The GitHub Actions workflow will automatically build and deploy the updated documentation to GitHub Pages\n\n### Local Documentation Development\n\nTo work with the documentation locally:\n\n1. Install the required dependencies:\n\n ```bash\n pip install mkdocs-material mkdocstrings mkdocstrings-python\n ```\n\n2. Run the local server:\n\n ```bash\n mkdocs serve\n ```\n\n3. View the documentation at: http://localhost:8000\n\n## Project Structure\n\n```\n.\n├── docs/ # Documentation files\n│ ├── api/ # API Documentation\n│ ├── features/ # Feature Documentation\n│ ├── getting-started/ # Getting Started Guides\n│ └── index.md # Documentation Home Page\n├── mkdocs.yml # MkDocs Configuration\n└── .github/workflows/ # GitHub Actions Workflows\n```\n\n## Contributing\n\nContributions are welcome! Please feel free to submit a Pull Request.\n\n1. Fork the repository\n2. Create a feature branch (`git checkout -b feature/amazing-feature`)\n3. Commit your changes (`git commit -m 'Add some amazing feature'`)\n4. Push to the branch (`git push origin feature/amazing-feature`)\n5. Open a Pull Request\n\n## License\n\nThis project is licensed under the MIT License - see the LICENSE file for details.\n\n## Acknowledgments\n\n- Inspired by the [OpenBCI](https://openbci.com/) project\n- Built on the [Model Context Protocol](https://modelcontextprotocol.io/) framework\n- Thanks to the neuroscience and AI research communities\n\n## Contact\n\nEnkhbold Ganbold - [GitHub Profile](https://github.com/enkhbold470)\n\nProject Link: [https://github.com/enkhbold470/bci-mcp](https://github.com/enkhbold470/bci-mcp)","English","tag","en-US",{"_27":39,"_31":43,"_36":44},{"_29":40,"_31":41,"_33":42},"Un marco que integra la tecnología de interfaz cerebro-computadora con el Protocolo de Contexto Modelo para permitir el procesamiento de señales neuronales en tiempo real e interacciones impulsadas por IA para aplicaciones de atención médica, accesibilidad e investigación.","Servidor BCI-MCP","# Interfaz cerebro-computadora con protocolo de contexto de modelo (BCI-MCP)\n\nEste proyecto integra la tecnología de interfaz cerebro-computadora (BCI) con el protocolo de contexto de modelo (MCP) para crear un marco poderoso para la adquisición, el procesamiento y las interacciones habilitadas por IA de señales neuronales.\n\n[![Páginas de GitHub](https://img.shields.io/badge/docs-GitHub%20Pages-blue)](https://enkhbold470.github.io/bci-mcp/) [![Licencia: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)\n\n## Descripción general\n\nBCI-MCP combina:\n\n* **Interfaz cerebro-computadora (BCI)** : adquisición y procesamiento de señales neuronales en tiempo real\n* **Protocolo de contexto de modelo (MCP)** : interfaz de comunicación de IA estandarizada\n\nEsta integración permite aplicaciones avanzadas en atención médica, accesibilidad, investigación e interacción persona-computadora.\n\n## Características principales\n\n### Características principales de BCI\n\n* **Adquisición de señales neuronales** : captura señales eléctricas de la actividad cerebral en tiempo real\n* **Procesamiento de señales** : preprocesar, extraer características y clasificar señales cerebrales\n* **Generación de comandos** : convertir las señales cerebrales interpretadas en comandos\n* **Mecanismos de retroalimentación** : proporcionar retroalimentación para ayudar a los usuarios a mejorar el control\n* **Operación en tiempo real** : procesa la actividad cerebral con un retraso mínimo\n\n### Funciones de integración de MCP\n\n* **Intercambio de contexto estandarizado** : conecte datos de BCI con modelos de IA mediante MCP\n* **Exposición de herramientas** : hacer que las funciones de BCI estén disponibles para las aplicaciones de IA\n* **Flujos de trabajo componibles** : cree operaciones complejas combinando señales BCI y procesamiento de IA\n* **Intercambio seguro de datos** : permite la transmisión de datos neuronales preservando la privacidad\n\n## Arquitectura del sistema\n\nEl sistema BCI-MCP consta de varios componentes clave:\n\n```\n┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐\n│ │ │ │ │ │\n│ BCI Hardware │──────│ BCI Software │──────│ MCP Server │\n│ │ │ │ │ │\n└─────────────────┘ └─────────────────┘ └────────┬────────┘\n │\n │\n ┌────────▼────────┐\n │ │\n │ AI Applications │\n │ │\n └─────────────────┘\n```\n\n## Empezando\n\n### Prerrequisitos\n\n* Python 3.10+\n* Hardware EEG compatible (o utilice el modo simulado para realizar pruebas)\n* Dependencias adicionales enumeradas en requirements.txt\n\n### Instalación\n\n```bash\n# Clone the repository\ngit clone https://github.com/enkhbold470/bci-mcp.git\ncd bci-mcp\n\n# Create a virtual environment\npython -m venv venv\nsource venv/bin/activate # On Windows: venv\\Scripts\\activate\n\n# Install dependencies\npip install -r requirements.txt\n```\n\n### Usando Docker\n\nPara una configuración más sencilla, puedes usar Docker:\n\n```bash\n# Build and start all services\ndocker-compose up -d\n\n# Access the documentation at http://localhost:8000\n# The MCP server will be available at ws://localhost:8765\n```\n\n### Uso básico\n\n```bash\n# Start the MCP server\npython src/main.py --server\n\n# Or use the interactive console\npython src/main.py --interactive\n\n# List available EEG devices\npython src/main.py --list-ports\n\n# Record a 60-second BCI session\npython src/main.py --port /dev/tty.usbmodem1101 --record 60\n```\n\n## Aplicaciones avanzadas\n\nLa integración BCI-MCP permite una gama de aplicaciones de vanguardia:\n\n### Atención sanitaria y accesibilidad\n\n* **Tecnología de asistencia** : permitir que las personas con problemas de movilidad controlen dispositivos\n* **Rehabilitación** : Apoye la rehabilitación neurológica con retroalimentación en tiempo real\n* **Herramientas de diagnóstico** : Ayudan a diagnosticar afecciones neurológicas.\n\n### Investigación y desarrollo\n\n* **Investigación en neurociencia** : facilitar los estudios sobre la función cerebral y la cognición\n* **Capacitación BCI** : Acelere el aprendizaje y la adaptación al control BCI\n* **Desarrollo de protocolos** : establecer estándares para el intercambio de datos neuronales\n\n### Interfaces mejoradas con IA\n\n* **Interfaces adaptativas** : interfaces que se ajustan en función de señales neuronales y asistencia de IA.\n* **Reconocimiento de intenciones** : mejor comprensión de la intención del usuario a través de señales neuronales\n* **Comunicación aumentativa** : comunicación mejorada para personas con discapacidades del habla\n\n## Documentación\n\nLa documentación del proyecto está alojada en GitHub Pages en: \n\n### Mantenimiento de la documentación\n\nLa documentación se creó con MkDocs y el tema Material. Para actualizar la documentación:\n\n1. Realice cambios en los archivos Markdown en el directorio `docs/` en la rama `main`\n2. Confirme y envíe sus cambios a la rama `main`\n3. El flujo de trabajo de Acciones de GitHub creará e implementará automáticamente la documentación actualizada en las páginas de GitHub.\n\n### Desarrollo de documentación local\n\nPara trabajar con la documentación localmente:\n\n1. Instale las dependencias necesarias:\n\n ```bash\n pip install mkdocs-material mkdocstrings mkdocstrings-python\n ```\n\n2. Ejecute el servidor local:\n\n ```bash\n mkdocs serve\n ```\n\n3. Ver la documentación en: \n\n## Estructura del proyecto\n\n```\n.\n├── docs/ # Documentation files\n│ ├── api/ # API Documentation\n│ ├── features/ # Feature Documentation\n│ ├── getting-started/ # Getting Started Guides\n│ └── index.md # Documentation Home Page\n├── mkdocs.yml # MkDocs Configuration\n└── .github/workflows/ # GitHub Actions Workflows\n```\n\n## Contribuyendo\n\n¡Agradecemos sus contribuciones! No dude en enviar una solicitud de incorporación de cambios.\n\n1. Bifurcar el repositorio\n2. Crear una rama de características ( `git checkout -b feature/amazing-feature` )\n3. Confirme sus cambios ( `git commit -m 'Add some amazing feature'` )\n4. Empujar a la rama ( `git push origin feature/amazing-feature` )\n5. Abrir una solicitud de extracción\n\n## Licencia\n\nEste proyecto está licenciado bajo la licencia MIT: consulte el archivo de LICENCIA para obtener más detalles.\n\n## Expresiones de gratitud\n\n* Inspirado en el proyecto [OpenBCI](https://openbci.com/)\n* Construido sobre el marco [del Protocolo de Contexto Modelo](https://modelcontextprotocol.io/)\n* Gracias a las comunidades de investigación en neurociencia e inteligencia artificial\n\n## Contacto\n\nEnkhbold Ganbold - [Perfil de GitHub](https://github.com/enkhbold470)\n\nEnlace del proyecto: ","Spanish","es-ES",{"_27":46,"_31":50,"_36":51},{"_29":47,"_31":48,"_33":49},"ブレイン・コンピュータ・インターフェース技術とモデル・コンテキスト・プロトコルを統合し、ヘルスケア、アクセシビリティ、研究アプリケーションにおけるリアルタイムの神経信号処理と AI を活用したインタラクションを可能にするフレームワークです。","BCI-MCP サーバー","# モデルコンテキストプロトコルを備えた脳コンピュータインターフェース（BCI-MCP）\n\nこのプロジェクトでは、脳コンピュータインターフェース (BCI) テクノロジーとモデルコンテキストプロトコル (MCP) を統合して、神経信号の取得、処理、AI 対応のインタラクションのための強力なフレームワークを作成します。\n\n[![GitHubページ](https://img.shields.io/badge/docs-GitHub%20Pages-blue)](https://enkhbold470.github.io/bci-mcp/) [![ライセンス: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)\n\n## 概要\n\nBCI-MCP は次のものを組み合わせます:\n\n* **脳コンピュータインターフェース（BCI）** ：神経信号のリアルタイム取得と処理\n* **モデルコンテキストプロトコル（MCP）** ：標準化されたAI通信インターフェース\n\nこの統合により、ヘルスケア、アクセシビリティ、研究、人間とコンピュータの相互作用における高度なアプリケーションが可能になります。\n\n## 主な特徴\n\n### BCIコア機能\n\n* **神経信号取得**：脳活動からの電気信号をリアルタイムで捕捉する\n* **信号処理**：脳信号の前処理、特徴抽出、分類\n* **コマンド生成**: 解釈された脳信号をコマンドに変換する\n* **フィードバックメカニズム**: ユーザーのコントロールを向上させるためのフィードバックを提供する\n* **リアルタイム操作**：最小限の遅延で脳活動を処理\n\n### MCP統合機能\n\n* **標準化されたコンテキスト共有**：MCPを使用してBCIデータをAIモデルに接続\n* **ツール公開**：BCI機能をAIアプリケーションで利用できるようにする\n* **構成可能なワークフロー**: BCI信号とAI処理を組み合わせた複雑な操作を構築\n* **安全なデータ交換**：プライバシーを保護した神経データ伝送を可能にする\n\n## システムアーキテクチャ\n\nBCI-MCP システムは、いくつかの主要コンポーネントで構成されています。\n\n```\n┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐\n│ │ │ │ │ │\n│ BCI Hardware │──────│ BCI Software │──────│ MCP Server │\n│ │ │ │ │ │\n└─────────────────┘ └─────────────────┘ └────────┬────────┘\n │\n │\n ┌────────▼────────┐\n │ │\n │ AI Applications │\n │ │\n └─────────────────┘\n```\n\n## はじめる\n\n### 前提条件\n\n* Python 3.10以上\n* 互換性のある EEG ハードウェア (またはテストにはシミュレーションモードを使用)\n* requirements.txt にリストされている追加の依存関係\n\n### インストール\n\n```bash\n# Clone the repository\ngit clone https://github.com/enkhbold470/bci-mcp.git\ncd bci-mcp\n\n# Create a virtual environment\npython -m venv venv\nsource venv/bin/activate # On Windows: venv\\Scripts\\activate\n\n# Install dependencies\npip install -r requirements.txt\n```\n\n### Dockerの使用\n\nセットアップを簡単にするには、Docker を使用できます。\n\n```bash\n# Build and start all services\ndocker-compose up -d\n\n# Access the documentation at http://localhost:8000\n# The MCP server will be available at ws://localhost:8765\n```\n\n### 基本的な使い方\n\n```bash\n# Start the MCP server\npython src/main.py --server\n\n# Or use the interactive console\npython src/main.py --interactive\n\n# List available EEG devices\npython src/main.py --list-ports\n\n# Record a 60-second BCI session\npython src/main.py --port /dev/tty.usbmodem1101 --record 60\n```\n\n## 高度なアプリケーション\n\nBCI-MCP 統合により、さまざまな最先端アプリケーションが可能になります。\n\n### ヘルスケアとアクセシビリティ\n\n* **支援技術**：運動障害のある人がデバイスを制御できるようにする\n* **リハビリテーション**：リアルタイムフィードバックによる神経学的リハビリテーションのサポート\n* **診断ツール**：神経疾患の診断を支援する\n\n### 研究開発\n\n* **神経科学研究**：脳機能と認知の研究を促進する\n* **BCIトレーニング**：BCI制御の学習と適応を加速\n* **プロトコル開発**：神経データ交換の標準を確立する\n\n### AI強化インターフェース\n\n* **アダプティブインターフェース**：神経信号とAI支援に基づいて調整するインターフェース\n* **意図認識**：神経信号を通じてユーザーの意図をより深く理解する\n* **補助的コミュニケーション**：言語障害のある人のための強化されたコミュニケーション\n\n## ドキュメント\n\nプロジェクトのドキュメントは、GitHub Pages のでホストされています。\n\n### ドキュメントの維持\n\nドキュメントはMkDocsとMaterialテーマを使用して作成されています。ドキュメントを更新するには、以下の手順に従ってください。\n\n1. `main`ブランチの`docs/`ディレクトリにある Markdown ファイルに変更を加える\n2. 変更をコミットして`main`ブランチにプッシュします\n3. GitHub Actionsワークフローは、更新されたドキュメントを自動的にビルドしてGitHub Pagesにデプロイします。\n\n### ローカルドキュメントの開発\n\nドキュメントをローカルで操作するには:\n\n1. 必要な依存関係をインストールします。\n\n ```bash\n pip install mkdocs-material mkdocstrings mkdocstrings-python\n ```\n\n2. ローカルサーバーを実行します。\n\n ```bash\n mkdocs serve\n ```\n\n3. ドキュメントはで参照できます。\n\n## プロジェクト構造\n\n```\n.\n├── docs/ # Documentation files\n│ ├── api/ # API Documentation\n│ ├── features/ # Feature Documentation\n│ ├── getting-started/ # Getting Started Guides\n│ └── index.md # Documentation Home Page\n├── mkdocs.yml # MkDocs Configuration\n└── .github/workflows/ # GitHub Actions Workflows\n```\n\n## 貢献\n\n貢献を歓迎します！お気軽にプルリクエストを送信してください。\n\n1. リポジトリをフォークする\n2. 機能ブランチを作成する ( `git checkout -b feature/amazing-feature` )\n3. 変更をコミットします ( `git commit -m 'Add some amazing feature'` )\n4. ブランチにプッシュする ( `git push origin feature/amazing-feature` )\n5. プルリクエストを開く\n\n## ライセンス\n\nこのプロジェクトは MIT ライセンスに基づいてライセンスされています - 詳細については LICENSE ファイルを参照してください。\n\n## 謝辞\n\n* [OpenBCI](https://openbci.com/)プロジェクトに触発されて\n* [モデルコンテキストプロトコル](https://modelcontextprotocol.io/)フレームワーク上に構築\n* 神経科学とAI研究コミュニティに感謝\n\n## 接触\n\nEnkhbold Ganbold - [GitHub プロフィール](https://github.com/enkhbold470)\n\nプロジェクトリンク: ","Japanese","ja-JP",{"_27":53,"_31":57,"_36":58},{"_29":54,"_31":55,"_33":56},"실시간 신경 신호 처리와 의료, 접근성 및 연구 애플리케이션을 위한 AI 기반 상호 작용을 가능하게 하는 모델 컨텍스트 프로토콜과 뇌-컴퓨터 인터페이스 기술을 통합하는 프레임워크입니다.","BCI-MCP 서버","# 모델 컨텍스트 프로토콜(BCI-MCP)을 통한 뇌-컴퓨터 인터페이스\n\n이 프로젝트는 BCI(뇌-컴퓨터 인터페이스) 기술과 MCP(모델 컨텍스트 프로토콜)를 통합하여 신경 신호 수집, 처리 및 AI 기반 상호작용을 위한 강력한 프레임워크를 구축합니다.\n\n[![GitHub 페이지](https://img.shields.io/badge/docs-GitHub%20Pages-blue)](https://enkhbold470.github.io/bci-mcp/) [![라이센스: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)\n\n## 개요\n\nBCI-MCP는 다음을 결합합니다.\n\n* **뇌-컴퓨터 인터페이스(BCI)** : 신경 신호의 실시간 수집 및 처리\n* **모델 컨텍스트 프로토콜(MCP)** : 표준화된 AI 커뮤니케이션 인터페이스\n\n이러한 통합을 통해 의료, 접근성, 연구, 인간-컴퓨터 상호 작용 분야에서 고급 응용 프로그램이 가능해집니다.\n\n## 주요 특징\n\n### BCI 핵심 기능\n\n* **신경 신호 수집** : 뇌 활동으로부터 실시간으로 전기 신호를 수집합니다.\n* **신호 처리** : 뇌 신호 전처리, 특징 추출 및 분류\n* **명령 생성** : 해석된 뇌 신호를 명령으로 변환\n* **피드백 메커니즘** : 사용자가 제어를 개선할 수 있도록 피드백을 제공합니다.\n* **실시간 작업** : 지연을 최소화하여 뇌 활동을 처리합니다.\n\n### MCP 통합 기능\n\n* **표준화된 컨텍스트 공유** : MCP를 사용하여 BCI 데이터를 AI 모델과 연결\n* **도구 노출** : BCI 기능을 AI 애플리케이션에 사용 가능하게 만들기\n* **구성 가능한 워크플로** : BCI 신호와 AI 처리를 결합한 복잡한 작업 구축\n* **안전한 데이터 교환** : 개인 정보를 보호하는 신경 데이터 전송을 활성화합니다.\n\n## 시스템 아키텍처\n\nBCI-MCP 시스템은 다음과 같은 몇 가지 핵심 구성 요소로 구성됩니다.\n\n지엑스피1\n\n## 시작하기\n\n### 필수 조건\n\n* 파이썬 3.10+\n* 호환 EEG 하드웨어(또는 테스트를 위해 시뮬레이션 모드 사용)\n* requirements.txt에 나열된 추가 종속성\n\n### 설치\n\n```bash\n# Clone the repository\ngit clone https://github.com/enkhbold470/bci-mcp.git\ncd bci-mcp\n\n# Create a virtual environment\npython -m venv venv\nsource venv/bin/activate # On Windows: venv\\Scripts\\activate\n\n# Install dependencies\npip install -r requirements.txt\n```\n\n### Docker 사용\n\n더 쉽게 설정하려면 Docker를 사용할 수 있습니다.\n\n```bash\n# Build and start all services\ndocker-compose up -d\n\n# Access the documentation at http://localhost:8000\n# The MCP server will be available at ws://localhost:8765\n```\n\n### 기본 사용법\n\n```bash\n# Start the MCP server\npython src/main.py --server\n\n# Or use the interactive console\npython src/main.py --interactive\n\n# List available EEG devices\npython src/main.py --list-ports\n\n# Record a 60-second BCI session\npython src/main.py --port /dev/tty.usbmodem1101 --record 60\n```\n\n## 고급 응용 프로그램\n\nBCI-MCP 통합을 통해 다양한 최첨단 애플리케이션을 구현할 수 있습니다.\n\n### 의료 및 접근성\n\n* **보조 기술** : 이동 장애가 있는 개인이 장치를 제어할 수 있도록 지원\n* **재활** : 실시간 피드백으로 신경학적 재활 지원\n* **진단 도구** : 신경계 질환 진단에 도움\n\n### 연구개발\n\n* **신경과학 연구** : 뇌 기능 및 인지 연구 촉진\n* **BCI 교육** : BCI 제어에 대한 학습 및 적응 가속화\n* **프로토콜 개발** : 신경 데이터 교환을 위한 표준 수립\n\n### AI 강화 인터페이스\n\n* **적응형 인터페이스** : 신경 신호와 AI 지원에 따라 조정되는 인터페이스\n* **의도 인식** : 신경 신호를 통해 사용자 의도를 더 잘 이해\n* **증강 의사소통** : 언어 장애가 있는 개인을 위한 향상된 의사소통\n\n## 선적 서류 비치\n\n프로젝트 문서는 GitHub Pages [(https://enkhbold470.github.io/bci-mcp/)](https://enkhbold470.github.io/bci-mcp/) 에 호스팅됩니다.\n\n### 문서 유지 관리\n\n이 문서는 Material 테마를 적용한 MkDocs를 사용하여 작성되었습니다. 문서를 업데이트하려면 다음을 수행하세요.\n\n1. `main` 브랜치의 `docs/` 디렉토리에 있는 Markdown 파일을 변경합니다.\n2. 변경 사항을 `main` 브랜치에 커밋하고 푸시합니다.\n3. GitHub Actions 워크플로는 업데이트된 문서를 자동으로 빌드하고 GitHub Pages에 배포합니다.\n\n### 로컬 문서 개발\n\n로컬에서 문서를 사용하려면 다음을 수행하세요.\n\n1. 필요한 종속성을 설치하세요:\n\n ```bash\n pip install mkdocs-material mkdocstrings mkdocstrings-python\n ```\n\n2. 로컬 서버를 실행합니다.\n\n ```bash\n mkdocs serve\n ```\n\n3. 설명서는 에서 확인하세요.\n\n## 프로젝트 구조\n\n```\n.\n├── docs/ # Documentation files\n│ ├── api/ # API Documentation\n│ ├── features/ # Feature Documentation\n│ ├── getting-started/ # Getting Started Guides\n│ └── index.md # Documentation Home Page\n├── mkdocs.yml # MkDocs Configuration\n└── .github/workflows/ # GitHub Actions Workflows\n```\n\n## 기여하다\n\n기여를 환영합니다! 풀 리퀘스트를 제출해 주세요.\n\n1. 저장소를 포크하세요\n2. 기능 브랜치를 생성합니다( `git checkout -b feature/amazing-feature` )\n3. 변경 사항을 커밋하세요( `git commit -m 'Add some amazing feature'` )\n4. 브랜치에 푸시( `git push origin feature/amazing-feature` )\n5. 풀 리퀘스트 열기\n\n## 특허\n\n이 프로젝트는 MIT 라이선스에 따라 라이선스가 부여되었습니다. 자세한 내용은 라이선스 파일을 참조하세요.\n\n## 감사의 말\n\n* [OpenBCI](https://openbci.com/) 프로젝트에서 영감을 받음\n* [모델 컨텍스트 프로토콜](https://modelcontextprotocol.io/) 프레임워크 기반으로 구축됨\n* 신경과학 및 AI 연구 커뮤니티에 감사드립니다.\n\n## 연락하다\n\nEnkhbold Ganbold - [GitHub 프로필](https://github.com/enkhbold470)\n\n프로젝트 링크: ","Korean","ko-KR",{"_27":60,"_31":64,"_36":65},{"_29":61,"_31":62,"_33":63},"该框架将脑机接口技术与模型上下文协议相结合，实现实时神经信号处理和人工智能交互，用于医疗保健、无障碍和研究应用。","BCI-MCP服务器","# 带有模型上下文协议的脑机接口（BCI-MCP）\n\n该项目将脑机接口 (BCI) 技术与模型上下文协议 (MCP) 相结合，为神经信号采集、处理和人工智能交互创建一个强大的框架。\n\n[![GitHub 页面](https://img.shields.io/badge/docs-GitHub%20Pages-blue)](https://enkhbold470.github.io/bci-mcp/) [![许可证：MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)\n\n## 概述\n\nBCI-MCP 结合了：\n\n* **脑机接口（BCI）** ：实时采集和处理神经信号\n* **模型上下文协议（MCP）** ：标准化AI通信接口\n\n这种集成使得医疗保健、无障碍、研究和人机交互等领域的高级应用成为可能。\n\n## 主要特点\n\n### BCI 核心功能\n\n* **神经信号采集**：实时捕捉大脑活动的电信号\n* **信号处理**：预处理、提取特征和分类脑信号\n* **命令生成**：将解释的脑信号转换为命令\n* **反馈机制**：提供反馈以帮助用户提高控制\n* **实时操作**：以最小延迟处理大脑活动\n\n### MCP 集成功能\n\n* **标准化上下文共享**：使用 MCP 将 BCI 数据与 AI 模型连接起来\n* **工具曝光**：让 BCI 功能可用于 AI 应用程序\n* **可组合工作流程**：构建结合 BCI 信号和 AI 处理的复杂操作\n* **安全数据交换**：实现隐私保护的神经数据传输\n\n## 系统架构\n\nBCI-MCP系统由几个关键组件组成：\n\n```\n┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐\n│ │ │ │ │ │\n│ BCI Hardware │──────│ BCI Software │──────│ MCP Server │\n│ │ │ │ │ │\n└─────────────────┘ └─────────────────┘ └────────┬────────┘\n │\n │\n ┌────────▼────────┐\n │ │\n │ AI Applications │\n │ │\n └─────────────────┘\n```\n\n## 入门\n\n### 先决条件\n\n* Python 3.10+\n* 兼容的EEG硬件（或使用模拟模式进行测试）\n* requirements.txt 中列出的其他依赖项\n\n### 安装\n\n```bash\n# Clone the repository\ngit clone https://github.com/enkhbold470/bci-mcp.git\ncd bci-mcp\n\n# Create a virtual environment\npython -m venv venv\nsource venv/bin/activate # On Windows: venv\\Scripts\\activate\n\n# Install dependencies\npip install -r requirements.txt\n```\n\n### 使用 Docker\n\n为了更容易设置，您可以使用 Docker：\n\n```bash\n# Build and start all services\ndocker-compose up -d\n\n# Access the documentation at http://localhost:8000\n# The MCP server will be available at ws://localhost:8765\n```\n\n### 基本用法\n\n```bash\n# Start the MCP server\npython src/main.py --server\n\n# Or use the interactive console\npython src/main.py --interactive\n\n# List available EEG devices\npython src/main.py --list-ports\n\n# Record a 60-second BCI session\npython src/main.py --port /dev/tty.usbmodem1101 --record 60\n```\n\n## 高级应用程序\n\nBCI-MCP 集成支持一系列尖端应用：\n\n### 医疗保健和无障碍\n\n* **辅助技术**：使行动不便人士能够控制设备\n* **康复**：通过实时反馈支持神经康复\n* **诊断工具**：辅助诊断神经系统疾病\n\n### 研究与开发\n\n* **神经科学研究**：促进大脑功能和认知的研究\n* **BCI 训练**：加速学习和适应 BCI 控制\n* **协议开发**：建立神经数据交换标准\n\n### 人工智能增强界面\n\n* **自适应界面**：根据神经信号和人工智能辅助进行调整的界面\n* **意图识别**：通过神经信号更好地理解用户意图\n* **增强沟通**：增强言语障碍人士的沟通能力\n\n## 文档\n\n项目文档托管在 GitHub Pages 上： \n\n### 维护文档\n\n本文档使用 MkDocs 和 Material 主题构建。更新文档：\n\n1. 对`main`分支上`docs/`目录中的 Markdown 文件进行更改\n2. 提交并将更改推送到`main`分支\n3. GitHub Actions 工作流程将自动构建更新的文档并将其部署到 GitHub Pages\n\n### 本地文档开发\n\n要在本地使用文档：\n\n1. 安装所需的依赖项：\n\n ```bash\n pip install mkdocs-material mkdocstrings mkdocstrings-python\n ```\n\n2. 运行本地服务器：\n\n ```bash\n mkdocs serve\n ```\n\n3. 查看文档： \n\n## 项目结构\n\n```\n.\n├── docs/ # Documentation files\n│ ├── api/ # API Documentation\n│ ├── features/ # Feature Documentation\n│ ├── getting-started/ # Getting Started Guides\n│ └── index.md # Documentation Home Page\n├── mkdocs.yml # MkDocs Configuration\n└── .github/workflows/ # GitHub Actions Workflows\n```\n\n## 贡献\n\n欢迎贡献代码！欢迎提交 Pull 请求。\n\n1. 分叉存储库\n2. 创建功能分支（ `git checkout -b feature/amazing-feature` ）\n3. 提交您的更改（ `git commit -m 'Add some amazing feature'` ）\n4. 推送到分支（ `git push origin feature/amazing-feature` ）\n5. 打开拉取请求\n\n## 执照\n\n该项目根据 MIT 许可证获得许可 - 有关详细信息，请参阅 LICENSE 文件。\n\n## 致谢\n\n* 受到[OpenBCI](https://openbci.com/)项目的启发\n* 基于[模型上下文协议](https://modelcontextprotocol.io/)框架构建\n* 感谢神经科学和人工智能研究社区\n\n## 接触\n\nEnkhbold Ganbold - [GitHub 个人资料](https://github.com/enkhbold470)\n\n项目链接： ","Chinese","zh-CN","githubUser","locale",{"_69":70,"_13":71,"_79":30,"_80":30,"_16":81,"_82":83,"_31":32,"_123":124,"_131":-5,"_132":-5,"_133":134,"_176":177,"_181":-5,"_90":161,"_182":165,"_93":183,"_184":185},"addedAt","2025-03-23T19:02:54.503229Z",[72,73,74,75,76,77,78],"category:bioinformatics","category:health-and-wellness","category:autonomous-agents","environment:local","hosting:hybrid","language:python","license_grade:f","descriptionMarkdown","descriptionPlainText",3279,"integrations",[84,95,102,109,116],{"_85":86,"_29":92,"_93":94},"brand",{"_87":88,"_31":89,"_90":91},"logoSvgPath","M13.983 11.078h2.119a.186.186 0 00.186-.185V9.006a.186.186 0 00-.186-.186h-2.119a.185.185 0 00-.185.185v1.888c0 .102.083.185.185.185m-2.954-5.43h2.118a.186.186 0 00.186-.186V3.574a.186.186 0 00-.186-.185h-2.118a.185.185 0 00-.185.185v1.888c0 .102.082.185.185.185m0 2.716h2.118a.187.187 0 00.186-.186V6.29a.186.186 0 00-.186-.185h-2.118a.185.185 0 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MCP directory API