Future of Human-Computer Interaction

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Research

Ongoing works (closed-loop BCIs):

An investigation of closed-loop EEG-based Brain Computer Interface (BCI) helps bridge the gap in communicaon 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. Advanced signal processing techniques and pattern recogni on methods such as machine learning, ar ficial neural network, and deep learning can improve the efficiency of BCI by increasing information transfer rate and accuracy. Ultimately, research based on BCI could lead to the development of novel tools used in both clinical and healthy populations. 

In collaboration with Prof. Dr. Poramate Manoonpong who is an expert on bio-inspired robotics and embodied artficial intelligence, we combine the neural engineering technology with a bio-inspired robotic technology for advanced human-machine interaction (under BRAIN lab at VISTEC).

(For more in details: Click)

Previous works (open-loop BCIs) have been selected below:

1. Hybrid Brain/Blink Computer Interface toward a Personal Identification Number Application [ใช้สัญญาณสมองขณะหลับตา และสัญญาณการกระพริบตา ในการควบคุมใส่รหัส PIN เข้าสู่คอมพิวเตอร์]
Author: Theerawit Wilaiprasitporn, Alexandru Popovici and Tohru Yagi

Hybrid Brain-Computer Interface
PIN Application Using Hybrid Brain/Blink-Computer Interface
Feature Extraction on Brain Wave and Eye Movement Signals
Feature Extraction on Brain Wave and Eye Movement Signals

Abstract: In this study, we propose a hybrid brain/blink computer interface based on a single-channel electroencephalography (EEG) amplifier. Eyelid closing and hard blink were selected as two possible inputs for control of the interface. A 2-min calibration was required before starting to use the interface. An algorithm for feature extraction and classification was developed for EEG signals from eyelid closing, hard blink, and resting. To evaluate the performance of the interface, we incorporated it into a personal identification number (PIN) application, in both visual and auditory modes. Experiments with 5 healthy participants revealed that the PIN application based on the interface achieved a mean accuracy of 97.40%. In conclusion, we expect that our investigation will contribute to hybrid brain-computer interface research and technologies in the near future. [Full Text]

2. Single-channel Electrooculography Application Using Unsupervised Calibration [ใช้การเคลื่อนไหวของดวงตา(ซ้าย หรือขวา)ในการเลื่อน Cursor ไปซ้าย-ขวา และบน-ล่าง ประกอบกับการใช้สัญญาณกระพริบตาเพื่อการ Click หรือเลือก]
Author: Alexandru Popovici, Theerawit Wilaiprasitporn and Tohru Yagi

EOG signals: signals from eye movement
EOG signals: signals from eye movement
PIN application using EOG
PIN application using EOG

Abstract: Electrooculography (EOG) enables users to use specific eye movements as inputs for various applications, without using their fingers. However, online classification of such signals often requires long or sophisticated calibration procedures and multiple electrodes, which makes the resulting systems not practical for everyday use. To this end, a single-channel bio-potential recording system was used to develop a Personal Identification Number (PIN) input application, based on a simple and short unsupervised calibration method. The average accuracy achieved from five healthy subjects was 98.24%. The high accuracy, combined with the use of single-channel recording with convenient electrode placement resulted in a system that could be embedded in a reliable wearable human-computer interface (HCI) device. [Full Text]

3. Personal Identification Number Application Using Adaptive P300 Brain–Computer Interface [ใช้สัญญาณสมองชนิด P300 เพียงแค่จ้องมองหน้าจอก็สามารถใส่รหัส PIN เข้าสู่คอมพิวเตอร์ได้]
Author: Theerawit Wilaiprasitporn and Tohru Yagi

Novel Visual Stimulation for P300-BCI
Novel Visual Stimulation for P300-BCI
Event-Related Potential (ERP): P300 wave
Event-Related Potential (ERP): P300 wave
PIN application using P300-BCI
PIN application using P300-BCI

Abstract: Here we report the development of a personal identification number (PIN) application using a P300-based brain-computer interface (BCI). We focused on visual stimulation design for increasing the evoked potential in the brain. Single-channel electroencephalography and a computationally inexpensive algorithm were used for P300 detection. Experimental results showed that our proposed stimulus induced higher P300 amplitude than did a conventional stimulus. For a performance evaluation, we compared two versions of the proposed application, which were based on our ‘original P300 BCI’ and ‘adaptive P300 BCI’. In the adaptive P300 BCI, we introduced a novel algorithm for P300 detection to improve the information transfer rate while maintaining acceptable accuracy. Experiments with 10 healthy participants revealed that the original P300 BCI achieved mean accuracy of 83.50% at 11.40 bits/min and the adaptive version achieved mean accuracy of 86.00% at 18.63 bits/min. [Full Text]

4. A Study on SSVEP-based Brain Synchronization: Road to Brain-to-Brain Communication [เสนอแนวทางความเป็นไปได้ในการสื่อสารระหว่างคนสองคนผ่านสัญญาณสมอง และวงจรทางไฟฟ้า]
Author: Christopher Micek, Theerawit Wilaiprasitporn and Tohru Yagi

brain-to-brain-communication

Abstract: By applying a basic knowledge of brain-computer interfaces and brain stimulation, we introduce a novel architecture for brain-to-brain communication (B2B). Two main issues presented herein are brain synchronization and message modulation. According to our proposed B2B architecture, we assume that the higher the root mean square (RMS) of the voltage across two brains, the easier it is to recognize variations in brain potential states that can be used for communication. By using phase-synchronized alpha waves of multiple subjects via steady-state visually evoked potentials (SSVEP), we demonstrate the feasibility of our proposed B2B architecture as well as a method for maximizing the RMS of brain potentials. [Full Text (in press)]

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