EEG-Based Brain-Computer Interface and Its Applications:A Study on Practical Use of Electrophysiological Responses

Hello BCI Colleges!

I am Theerawit who is a PhD student at Yagi Laboratory, Tokyo Institute of Technology, Japan. I brought OpenBCI from Kickstarter :). Here, I would like to share what I and my friends have done so far with OpenBCI. Selected works have been listed below:

1. Hybrid Brain/Blink Computer Interface toward a Personal Identification Number Application

Author: Theerawit Wilaiprasitporn, Alexandru Popovici and Tohru Yagi

Hybrid BCI, BCI, openBCI, EEG

Using Brain & Blink to input PIN

Feature Extraction in Frequency Domain

Feature Extraction in Frequency Domain

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.

Read full-text:
https://www.researchgate.net/publication/307533795_Hybrid_BrainBlink_Computer_Interface_toward_a_Personal_Identification_Number_Application

2. Single-channel Electrooculography Application Using Unsupervised Calibration

Author:  Alexandru Popovici, Theerawit Wilaiprasitporn and Tohru Yagi

EOG signals, Eye movement

EOG signals, Eye movement

Use EOG/eye movement to input PIN

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.

Read full-text:
https://www.researchgate.net/publication/309739385_Single-channel_Electrooculography_Application_Using_Unsupervised_Calibration?ev=prf_pub

3. Personal Identification Number Application Using Adaptive P300 Brain–Computer Interface

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

P300-BCI,adaptive 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.

Read full-text:
https://www.researchgate.net/publication/303993797_Personal_Identification_Number_Application_Using_Adaptive_P300_Brain-Computer_Interface

4. A Study on SSVEP-based Brain Synchronization: Road to Brain-to-Brain Communication

Author: Christopher Micek, Theerawit Wilaiprasitporn and Tohru Yagi

Proposed concept of brain-to-brain communication

Proposed concept of 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.

Read full-text:
https://www.researchgate.net/publication/309739508_A_Study_on_SSVEP-based_Brain_Synchronization_Road_to_Brain-to-Brain_Communication

About Authors:
Theerawit is during Doctoral degree completion process at Tokyo Institute of Technology. Alex is working as researcher at NASA Ames Research Center. Christ is in last grade of undergraduate school at the Johns Hopkins University. Dr. Tohru Yagi is Associate Professor at Tokyo Institute of Technology.

And we are always very welcome research collaboration !

Enjoy!

Leave a Reply