scholarly journals Improvement of Information Transfer Rates Using a Hybrid EEG-NIRS Brain-Computer Interface with a Short Trial Length: Offline and Pseudo-Online Analyses

Sensors ◽  
2018 ◽  
Vol 18 (6) ◽  
pp. 1827 ◽  
Author(s):  
Jaeyoung Shin ◽  
Do-Won Kim ◽  
Klaus-Robert Müller ◽  
Han-Jeong Hwang
Keyword(s):  
Information Transfer ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Transfer Rates ◽  
Trial Length

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 [...]

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.

A Dual Stimuli Approach Combined with Convolutional Neural Network to Improve Information Transfer Rate of Event-Related Potential-Based Brain-Computer Interface

2018 ◽  
Vol 28 (10) ◽  
pp. 1850034 ◽  
Author(s):  
Wei Li ◽  
Mengfan Li ◽  
Huihui Zhou ◽  
Genshe Chen ◽  
Jing Jin ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Transfer Rate ◽  
Information Transfer ◽  
Brain Computer Interface ◽  
Event Related Potential ◽  
Computer Interface ◽  
Generation Rate ◽  
Information Transfer Rate ◽  
Command Generation

Increasing command generation rate of an event-related potential-based brain-robot system is challenging, because of limited information transfer rate of a brain-computer interface system. To improve the rate, we propose a dual stimuli approach that is flashing a robot image and is scanning another robot image simultaneously. Two kinds of event-related potentials, N200 and P300 potentials, evoked in this dual stimuli condition are decoded by a convolutional neural network. Compared with the traditional approaches, this proposed approach significantly improves the online information transfer rate from 23.0 or 17.8 to 39.1 bits/min at an accuracy of 91.7%. These results suggest that combining multiple types of stimuli to evoke distinguishable ERPs might be a promising direction to improve the command generation rate in the brain-computer interface.

A new time coding approach for CTVEP-based brain-computer interface

2020 ◽  
Vol 20 (3) ◽  
pp. 743-757
Author(s):  
Teng Ma ◽  
Xuezhuan Zhao
Keyword(s):  
Information Transfer ◽  
High Performance ◽  
Visual Stimulation ◽  
Brain Computer Interface ◽  
Low Frequency ◽  
Computer Interface ◽  
Binary Codes ◽  
Frequency Change ◽  
Time Coding ◽  
New Time

The chromatic transient visual evoked potential (CTVEP)-based brain-computer interface (BCI) can provide safer and more comfortable stimuli than the traditional VEP-based BCIs due to its low frequency change and no luminance variation in the visual stimulation. However, it still generates relatively few codes that correspond to input commands to control the outside devices, which limits its application in the practical BCIs to some extent. Aiming to obtain more codes, we firstly proposes a new time coding technique to CTVEP-based BCI by utilizing a combination of two 4-bit binary codes to construct four 8-bit binary codes to increase the control commands to extend its application in practice. In the experiment, two time-encoded isoluminant chromatic stimuli are combined to serve as different commands for BCI control, and the results show that the high performance based on the new time coding approach with the average accuracy up to 90.28% and average information transfer rate up to 27.78 bits/min for BCI can be achieved. It turns out that the BCI system based on the proposed method is feasible, stable and efficient, which makes the method very suitable for the practical application of BCIs, such as military, entertainment and medical enterprise.

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 Design of Wearable Headset with Steady State Visually Evoked Potential-Based Brain Computer Interface

Micromachines ◽  
2019 ◽  
Vol 10 (10) ◽  
pp. 681
Author(s):  
Bor-Shyh Lin ◽  
Bor-Shing Lin ◽  
Tzu-Hsiang Yen ◽  
Chien-Chin Hsu ◽  
Yao-Chin Wang
Keyword(s):  
Steady State ◽  
Information Transfer ◽  
Evoked Potential ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Signal Quality ◽  
Eeg Signal ◽  
Visually Evoked Potential ◽  
Information Transfer Rate ◽  
Field Programmable

Brain–computer interface (BCI) is a system that allows people to communicate directly with external machines via recognizing brain activities without manual operation. However, for most current BCI systems, conventional electroencephalography (EEG) machines and computers are usually required to acquire EEG signal and translate them into control commands, respectively. The sizes of the above machines are usually large, and this increases the limitation for daily applications. Moreover, conventional EEG electrodes also require conductive gels to improve the EEG signal quality. This causes discomfort and inconvenience of use, while the conductive gels may also encounter the problem of drying out during prolonged measurements. In order to improve the above issues, a wearable headset with steady-state visually evoked potential (SSVEP)-based BCI is proposed in this study. Active dry electrodes were designed and implemented to acquire a good EEG signal quality without conductive gels from the hairy site. The SSVEP BCI algorithm was also implemented into the designed field-programmable gate array (FPGA)-based BCI module to translate SSVEP signals into control commands in real time. Moreover, a commercial tablet was used as the visual stimulus device to provide graphic control icons. The whole system was designed as a wearable device to improve convenience of use in daily life, and it could acquire and translate EEG signal directly in the front-end headset. Finally, the performance of the proposed system was validated, and the results showed that it had excellent performance (information transfer rate = 36.08 bits/min).

scholarly journals The Use of a Brain Computer Interface Remote Control to Navigate a Recreational Device

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Shih Chung Chen ◽  
Aaron Raymond See ◽  
Yeou Jiunn Chen ◽  
Chia Hong Yeng ◽  
Chih Kuo Liang
Keyword(s):  
Remote Control ◽  
Information Transfer ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Brain Response ◽  
Brain Signals ◽  
Information Transfer Rate ◽  
Cord Injury ◽  
Remote Controller ◽  
The Brain

People suffering from paralysis caused by serious neural disorder or spinal cord injury also need to be given a means of recreation other than general living aids. Although there have been a proliferation of brain computer interface (BCI) applications, developments for recreational activities are scarcely seen. The objective of this study is to develop a BCI-based remote control integrated with commercial devices such as the remote controlled Air Swimmer. The brain is visually stimulated using boxes flickering at preprogrammed frequencies to activate a brain response. After acquiring and processing these brain signals, the frequency of the resulting peak, which corresponds to the user’s selection, is determined by a decision model. Consequently, a command signal is sent from the computer to the wireless remote controller via a data acquisition (DAQ) module. A command selection training (CST) and simulated path test (SPT) were conducted by 12 subjects using the BCI control system and the experimental results showed a recognition accuracy rate of 89.51% and 92.31% for the CST and SPT, respectively. The fastest information transfer rate demonstrated a response of 105 bits/min and 41.79 bits/min for the CST and SPT, respectively. The BCI system was proven to be able to provide a fast and accurate response for a remote controller application.

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