scholarly journals A Hybrid Speller Design Using Eye Tracking and SSVEP Brain–Computer Interface

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 891 ◽  
Author(s):  
Malik M. Naeem Mannan ◽  
M. Ahmad Kamran ◽  
Shinil Kang ◽  
Hak Soo Choi ◽  
Myung Yung Jeong
Keyword(s):  
Eye Tracking ◽  
Classification Accuracy ◽  
Information Transfer ◽  
High Speed ◽  
Communication Channel ◽  
Computer Interface ◽  
Stimuli Responsive ◽  
Transfer Rates ◽  
Computer Interfaces ◽  
Large Numbers

Steady-state visual evoked potentials (SSVEPs) have been extensively utilized to develop brain–computer interfaces (BCIs) due to the advantages of robustness, large number of commands, high classification accuracies, and information transfer rates (ITRs). However, the use of several simultaneous flickering stimuli often causes high levels of user discomfort, tiredness, annoyingness, and fatigue. Here we propose to design a stimuli-responsive hybrid speller by using electroencephalography (EEG) and video-based eye-tracking to increase user comfortability levels when presented with large numbers of simultaneously flickering stimuli. Interestingly, a canonical correlation analysis (CCA)-based framework was useful to identify target frequency with a 1 s duration of flickering signal. Our proposed BCI-speller uses only six frequencies to classify forty-eight targets, thus achieve greatly increased ITR, whereas basic SSVEP BCI-spellers use an equal number of frequencies to the number of targets. Using this speller, we obtained an average classification accuracy of 90.35 ± 3.597% with an average ITR of 184.06 ± 12.761 bits per minute in a cued-spelling task and an ITR of 190.73 ± 17.849 bits per minute in a free-spelling task. Consequently, our proposed speller is superior to the other spellers in terms of targets classified, classification accuracy, and ITR, while producing less fatigue, annoyingness, tiredness and discomfort. Together, our proposed hybrid eye tracking and SSVEP BCI-based system will ultimately enable a truly high-speed communication channel.

scholarly journals Brain–Computer Interface Spellers for Communication: Why We Need to Address Their Security and Authenticity

2020 ◽  
Vol 10 (3) ◽  
pp. 139
Author(s):  
Anirban Dutta
Keyword(s):  
Research And Development ◽  
Information Transfer ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Brain Computer Interfaces ◽  
Transfer Rates ◽  
Computer Interfaces ◽  
Significant Research

Brain–Computer Interfaces (BCI) have witnessed significant research and development in the last 20 years where the main aim was to improve their accuracy and increase their information transfer rates (ITRs), while still making them portable and easy to use by a broad range of users [...]

scholarly journals An Open Source-Based BCI Application for Virtual World Tour and Its Usability Evaluation

2021 ◽  
Vol 15 ◽  
Author(s):  
Sanghum Woo ◽  
Jongmin Lee ◽  
Hyunji Kim ◽  
Sungwoo Chun ◽  
Daehyung Lee ◽  
...  
Keyword(s):  
Open Source ◽  
Virtual World ◽  
Communication Channel ◽  
Brain Computer Interface ◽  
Open Science ◽  
Computer Interface ◽  
The Public ◽  
Control Functions ◽  
Computer Interfaces ◽  
And Control

Brain–computer interfaces can provide a new communication channel and control functions to people with restricted movements. Recent studies have indicated the effectiveness of brain–computer interface (BCI) applications. Various types of applications have been introduced so far in this field, but the number of those available to the public is still insufficient. Thus, there is a need to expand the usability and accessibility of BCI applications. In this study, we introduce a BCI application for users to experience a virtual world tour. This software was built on three open-source environments and is publicly available through the GitHub repository. For a usability test, 10 healthy subjects participated in an electroencephalography (EEG) experiment and evaluated the system through a questionnaire. As a result, all the participants successfully played the BCI application with 96.6% accuracy with 20 blinks from two sessions and gave opinions on its usability (e.g., controllability, completeness, comfort, and enjoyment) through the questionnaire. We believe that this open-source BCI world tour system can be used in both research and entertainment settings and hopefully contribute to open science in the BCI field.

scholarly journals High-speed spelling with a noninvasive brain–computer interface

2015 ◽  
Vol 112 (44) ◽  
pp. E6058-E6067 ◽  
Author(s):  
Xiaogang Chen ◽  
Yijun Wang ◽  
Masaki Nakanishi ◽  
Xiaorong Gao ◽  
Tzyy-Ping Jung ◽  
...  
Keyword(s):  
Information Transfer ◽  
High Speed ◽  
Target Identification ◽  
Modulation Method ◽  
Identification Algorithm ◽  
Calibration Data ◽  
Transfer Rates ◽  
Modulation And Demodulation ◽  
Individual Calibration ◽  
High Consistency

The past 20 years have witnessed unprecedented progress in brain–computer interfaces (BCIs). However, low communication rates remain key obstacles to BCI-based communication in humans. This study presents an electroencephalogram-based BCI speller that can achieve information transfer rates (ITRs) up to 5.32 bits per second, the highest ITRs reported in BCI spellers using either noninvasive or invasive methods. Based on extremely high consistency of frequency and phase observed between visual flickering signals and the elicited single-trial steady-state visual evoked potentials, this study developed a synchronous modulation and demodulation paradigm to implement the speller. Specifically, this study proposed a new joint frequency-phase modulation method to tag 40 characters with 0.5-s-long flickering signals and developed a user-specific target identification algorithm using individual calibration data. The speller achieved high ITRs in online spelling tasks. This study demonstrates that BCIs can provide a truly naturalistic high-speed communication channel using noninvasively recorded brain activities.

scholarly journals Fast DCT algorithms for EEG data compression in embedded systems

2015 ◽  
Vol 12 (1) ◽  
pp. 49-62 ◽  
Author(s):  
Darius Birvinskas ◽  
Vacius Jusas ◽  
Ignas Martisius ◽  
Robertas Damasevicius
Keyword(s):  
Embedded Systems ◽  
Data Compression ◽  
High Speed ◽  
Communication Channel ◽  
Brain Computer Interface ◽  
Low Cost ◽  
Computer Interface ◽  
Eeg Signals ◽  
Eeg Data ◽  
Transform Coefficients

Electroencephalography (EEG) is widely used in clinical diagnosis, monitoring and Brain - Computer Interface systems. Usually EEG signals are recorded with several electrodes and transmitted through a communication channel for further processing. In order to decrease communication bandwidth and transmission time in portable or low cost devices, data compression is required. In this paper we consider the use of fast Discrete Cosine Transform (DCT) algorithms for lossy EEG data compression. Using this approach, the signal is partitioned into a set of 8 samples and each set is DCT-transformed. The least-significant transform coefficients are removed before transmission and are filled with zeros before an inverse transform. We conclude that this method can be used in real-time embedded systems, where low computational complexity and high speed is required.

EEG-Based Brain–Computer Interfaces

2017 ◽  
Author(s):  
Gert Pfurtscheller ◽  
Clemens Brunner ◽  
Christa Neuper
Keyword(s):  
Communication Channel ◽  
Brain Activity ◽  
Clinical Applications ◽  
Current User ◽  
Physiological Signals ◽  
Computer Interface ◽  
Computer Interfaces ◽  
Control Signals ◽  
And Control ◽  
Eeg Features

A brain–computer interface (BCI) offers an alternative to natural communication and control by recording brain activity, processing it online, and producing control signals that reflect the user’s intent or the current user state. Therefore, a BCI provides a non-muscular communication channel that can be used to convey messages and commands without any muscle activity. This chapter presents information on the use of different electroencephalographic (EEG) features such as steady-state visual evoked potentials, P300 components, event-related desynchronization, or a combination of different EEG features and other physiological signals for EEG-based BCIs. This chapter also reviews motor imagery as a control strategy, discusses various training paradigms, and highlights the importance of feedback. It also discusses important clinical applications such as spelling systems, neuroprostheses, and rehabilitation after stroke. The chapter concludes with a discussion on different perspectives for the future of BCIs.

Brain–computer interface (BCI) operation: optimizing information transfer rates

2003 ◽  
Vol 63 (3) ◽  
pp. 237-251 ◽  
Author(s):  
Dennis J McFarland ◽  
William A Sarnacki ◽  
Jonathan R Wolpaw
Keyword(s):  
Information Transfer ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Transfer Rates

An Application Framework for Controlling an Avatar in a Desktop-Based Virtual Environment via a Software SSVEP Brain–Computer Interface

2010 ◽  
Vol 19 (1) ◽  
pp. 25-34 ◽  
Author(s):  
Josef Faller ◽  
Gernot Müller-Putz ◽  
Dieter Schmalstieg ◽  
Gert Pfurtscheller
Keyword(s):  
Virtual Environment ◽  
Healthy Subjects ◽  
Information Transfer ◽  
Brain Computer Interface ◽  
Research Direction ◽  
Computer Interface ◽  
Application Framework ◽  
Transfer Rates ◽  
Brain Signals ◽  
Real World Applications

This paper presents a reusable, highly configurable application framework that seamlessly integrates SSVEP stimuli within a desktop-based virtual environment (VE) on standard PC equipment. Steady-state visual evoked potentials (SSVEPs) are brain signals that offer excellent information transfer rates (ITR) within brain–computer interface (BCI) systems while requiring only minimal training. Generating SSVEP stimuli in a VE allows for an easier implementation of motivating training paradigms and more realistic simulations of real-world applications. EEG measurements on seven healthy subjects within three scenarios (Button, Slalom, and Apartment) showed that moving and static software generated SSVEP stimuli flickering at frequencies of up to 29 Hz proved suitable to elicit SSVEPs. This research direction could lead to vastly improved immersive VEs that allow both disabled and healthy users to seamlessly communicate or interact through an intuitive, natural, and friendly interface.

Sign in / Sign up

Export Citation Format

Share Document