This project implements an EEG-based Brain-Computer Interface (BCI) using the ADS1299 analog front-end and a 3-electrode setup (Fp1, Fpz, Fp2). The system is engineered to reduce neuro-signal latency by 3 seconds and achieves a 93% brainwave classification accuracy through machine learning-powered mental-state mapping. The primary goal is to enable early disease prediction and provide a foundation for assistive communication technologies.

• EEG Acquisition: Utilizes ADS1299 for high-fidelity EEG signal capture.
• 3-Electrode Setup: Electrodes placed at Fp1, Fpz, and Fp2 positions for optimal frontal lobe signal acquisition.
• Low Latency: Neuro-signal processing latency reduced by 3 seconds.
• ML-Powered Classification: Achieves 93% accuracy in brainwave classification for mental-state mapping.
• Cloud Integration: Real-time data publishing to Ubidots IoT cloud via MQTT.
• ESP32-Based: Wireless, portable, and cost-effective hardware platform.
• Extensible Design: Future-ready for integration with assistive technologies (e.g., pyautogui, OpenCV).

The following diagram illustrates the high-level architecture of the system, from EEG signal acquisition to cloud integration and potential assistive applications.

Below is a mind map outlining the core components, data flow, and future expansion possibilities for the project.

• ESP32 DevKit v1: Main microcontroller for data processing and communication.
• ADS1299: Analog front-end for EEG signal acquisition.
• Electrodes: 3-electrode configuration (Fp1, Fpz, Fp2).
• LED Indicator: Visual feedback for system status.

• Arduino Framework: Rapid prototyping and hardware abstraction.
• AsyncTCP & ESPAsyncWebServer: Efficient, non-blocking communication.
• PubSubClient: MQTT client for cloud data publishing.
• Machine Learning Model: For brainwave classification (external training and deployment).
• Ubidots IoT Platform: Real-time data visualization and analytics.

1. Hardware Connections:

   • Connect ADS1299 to ESP32 as per the datasheet.
   • Place electrodes at Fp1, Fpz, and Fp2 positions.
   • Connect LED indicator to GPIO21.

2. Configuration:

   • Update WiFi SSID, password, and Ubidots token in src/main.cpp.
   • Ensure the correct board is selected in platformio.ini.

3. Build & Upload:

   • Use PlatformIO to build and upload the firmware:

     pio run --target upload

4. Monitor Output:

   • Open the serial monitor to view real-time EEG data and system logs.

5. Cloud Visualization:

   • Log in to Ubidots to visualize and analyze incoming EEG data.

• Assistive Communication: Integrate with pyautogui to enable non-verbal communication for specially-abled individuals.
• Computer Vision: Use OpenCV for gesture or facial expression-based triggers.
• Expanded ML Models: Enhance classification accuracy and add more mental-state categories.
• Web Dashboard: Real-time EEG visualization and control interface.

For any inquiries or support, please contact:

• Kalp Thakkar - [email protected]
• GitHub: kalpthakkar
• LinkedIn: kalpthakkar

• ADS1299 Datasheet
• ESP32 Documentation
• Ubidots IoT Platform
• PlatformIO Documentation