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Bio-Inspired Robotics & Neural Engineering (BRAIN) Lab

Founding Members

Prof. Dr. Poramate Manoonpong (poramate.m@vistec.ac.th): Bio-inspired Robotics
Dr. Theerawit Wilaiprasitporn (theerawit.w@vistec.ac.th): Neural Engineering

The Bio-inspired Robotics and Neural Engineering (BRAIN) laboratory is part of School of Information Science & Technology (IST) at the VIDYASIRIMEDHI institute. The BRAIN Lab focuses on two main research areas (Bio-inspired Robotics and Neural Engineering) to develop fundamental methods to achieve advanced robotic technology for advanced human-machine interaction. According to this, our research concentrates on the following themes:

  • Soft and flexible biosensors for robust signal acquisition
  • Multiple (bio) sensory modality feedback for human machine interaction
  • Machine learning for complex sensory signal processing and integration
  • Adaptive neural motor control and autonomous learning of many degrees of freedom bio-inspired robotic systems
  • Embodied artificial intelligence
  • Neuroinformatics
  • Cognitive neurorobtics
  • Rehabilitation robotics
  • Cloud robotics / The Internet of Robotic Things
  • Wearable devices for practical applications

Research Topics
Bio-Inspired Adaptive Motor Control and Autonomous Learning for Embodied Multi-Sensorimotor Robotic Systems

How can brain-like mechanisms be developed and realized on artificial systems so they can perform multiple complex functions as biological living systems?

To address this, we employ a bio-inspired approach to develop brain-like mechanisms for adaptive motor control and autonomous learning of embodied multi-sensorimotor robotic systems. The developed mechanisms (BRAIN technology) are adaptive and flexible, which can be transferred to application areas like human-machine interaction, brain-machine interface, and rehabilitation.

Closed-loop EEG-based Brain Computer Interface (BCI) for Adaptive Continuous Robot Control

An investigation of closed-loop EEG-based Brain Computer Interface (BCI) helps bridging the gap in communication between man and machine. Understanding patterns of brain activity from basic neuroscience research will help engineering to optimize the BCI system that could be applied in the real-world environment. To achieve this, we employ advanced signal processing techniques and pattern recognition methods such as machine learning, artificial neural network, and deep learning to improve the efficiency of BCI by increasing information transfer rate and accuracy. We also work on developing generic methods for closed-loop EEG-based continuous robot control and smooth human-machine/machine-human interactions. This advanced BCI technology will be transferred to industrial as well as medical applications.

Cloud Service Robotics / Internet of Robotic Things (IoRT) for Smart Living & Business intelligence

Due to our world-class infrastructure with high performance and cloud computing at IST, we also work on advanced solutions to unwrap a limitation of the digital (internet) and physical (robot) worlds for smart living and business intelligence. Specifically, together with the Scalable Data Systems Lab, we will develop a generic and flexible framework for cloud computing and the Internet of Robotic Things (IoRT) as well as methods for data management of IoRT devices. The development will be used to collect data online from the devices to identify and predict their states in order to later proactively ontrol them for advanced human-machine interaction.

Advanced Human-Machine Interaction

There is an increase in number of using machines (or robots) in everyday life for a variety of applications, like rehabilitation, healthcare, welfare, service, inspection, household, industry, companion, etc. These machines have to robustly and smoothly interact with the (unpredictable) real world and humans. They have to also adapt to deal with unknown situations. According to this, we aim to develop advanced human-machine interaction technology that generates natural and proactive human-machine interactions for improving quality of life and health and even productivity. To do so, we will employ key knowledge from various areas (including 1. (bio-inspired) robotics, 2. machine learning, 3. neural engineering, 4. cloud computing, and 5. data science) to build up the cutting-edge technology.