Runtime Environment

• Windows 10 64bit
• SSVEP, ERP
  • 27-inch LCD monitor, 60Hz
  • Biosemi ActiveTwo
• Drowsiness detection
  • Empatica E4

Development Environment

• Biosemi Labview installation
  • https://www.biosemi.com/download.htm
  • Version : 8.06

• SSVEP
  • Python3 installation
    • https://www.python.org/downloads/
  • Spyder3 installation
    • pip install spyder
  • PsychoPy3.0.0 installation

• https://github.com/psychopy/psychopy/releases
• ERP

o    Eclipse Java EE IDE for Web Developers

• https://www.eclipse.org/downloads/packages/
• Version : Photon Release(4.8.0)

o    JDK

• https://www.oracle.com/technetwork/java/javase/downloads/index.html
• Version : 10.0.2
• Drowsiness detection

o    Python3 installation

• • • https://www.python.org/downloads/

o    JAVA – Android Studio installation

• https://developer.android.com/studio/?hl=ko

Get Started

• SSVEP / ERP
  • Open the Biosemi Labview and setting the option(sampling rate, channel, etc.)
  • Setting the parameter of python / Java code(trial, stimulus time, etc.)
  • Start the Boisemi Labview
  • Execute python / Java program
• Drowsiness detection
  • Wrist wear and power on the Empatica E4 device
  • Connection with a PPG measurement program
  • Button control of the PPG measurement program
  • Offline: For storing PPG data
  • Online: Real-time PPG measurement and drowsiness recognition

Overall description of source code

• SSVEP / ERP
  • Creating the visual stimulus of SSVEP / ERP
  • Acquisition of EEG data of SSVEP / ERP
  • Preprocessing the EEG data of SSVEP / ERP acquired.
  • Feature Extraction (Common Spatial Pattern, Canonical Correlation Analysis, etc.)
  • Classification (Linear Discriminant Analysis, Support Vector Machine)

• Drowsiness detection
  • Acquisition of user’s PPG data using PPG measuring device (‘Empatica E4’)
  • Real-time communication of acquired PPG data
  • Classifier model training using acquired PPG data (offline)
  • User state classification using PPG data acquired in real time (online)

The development of Intention Recognition Technology based on Brain Signals

Introduction

• The goal of this project is to set up a technology for controlling external application using human implicit intention.
• Data from the EEG extraction device is transmitted by TCP/IP protocol.
• We use the EEG-based BCI paradigms to control IoT device.
• Among the BCI paradigms, we use Steady-State Visual Evoked Potential (SSVEP) and Event-related potential (ERP) to recognize human intentions.
• We developed the on/off control technology of Philips Hue lamp through human drowsiness state recognition based on PPG.
• The IoT device used in this technology are Philips Hue lamp, Belkin Wemo Plug, etc.