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 Effects of Skin Friction on Tactile P300 Brain-Computer Interface Performance

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Ying Mao ◽  
Jing Jin ◽  
Shurui Li ◽  
Yangyang Miao ◽  
Andrzej Cichocki
Keyword(s):  
Skin Friction ◽  
Information Transfer ◽  
High Performance ◽  
Brain Computer Interface ◽  
Event Related Potentials ◽  
Research Direction ◽  
Vital Role ◽  
Tactile Perception ◽  
Computer Interface ◽  
Related Potentials

Tactile perception, the primary sensing channel of the tactile brain-computer interface (BCI), is a complicated process. Skin friction plays a vital role in tactile perception. This study aimed to examine the effects of skin friction on tactile P300 BCI performance. Two kinds of oddball paradigms were designed, silk-stim paradigm (SSP) and linen-stim paradigm (LSP), in which silk and linen were wrapped on target vibration motors, respectively. In both paradigms, the disturbance vibrators were wrapped in cotton. The experimental results showed that LSP could induce stronger event-related potentials (ERPs) and achieved a higher classification accuracy and information transfer rate (ITR) compared with SSP. The findings indicate that high skin friction can achieve high performance in tactile BCI. This work provides a novel research direction and constitutes a viable basis for the future tactile P300 BCI, which may benefit patients with visual impairments.

scholarly journals Optimising non-invasive brain-computer interface systems for free communication between naïve human participants

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Angela I. Renton ◽  
Jason B. Mattingley ◽  
David R. Painter
Keyword(s):  
Information Transfer ◽  
High Performance ◽  
Word Association ◽  
Brain Computer Interface ◽  
Activity Patterns ◽  
Computer Interface ◽  
Free Communication ◽  
Factors Affecting ◽  
Non Invasive ◽  
Communication Task

AbstractFree communication is one of the cornerstones of modern civilisation. While manual keyboards currently allow us to interface with computers and manifest our thoughts, a next frontier is communication without manual input. Brain-computer interface (BCI) spellers often achieve this by decoding patterns of neural activity as users attend to flickering keyboard displays. To date, the highest performing spellers report typing rates of ~10.00 words/minute. While impressive, these rates are typically calculated for experienced users repetitively typing single phrases. It is therefore not clear whether naïve users are able to achieve such high rates with the added cognitive load of genuine free communication, which involves continuously generating and spelling novel words and phrases. In two experiments, we developed an open-source, high-performance, non-invasive BCI speller and examined its feasibility for free communication. The BCI speller required users to focus their visual attention on a flickering keyboard display, thereby producing unique cortical activity patterns for each key, which were decoded using filter-bank canonical correlation analysis. In Experiment 1, we tested whether seventeen naïve users could maintain rapid typing during prompted free word association. We found that information transfer rates were indeed slower during this free communication task than during typing of a cued character sequence. In Experiment 2, we further evaluated the speller’s efficacy for free communication by developing a messaging interface, allowing users to engage in free conversation. The results showed that free communication was possible, but that information transfer was reduced by voluntary textual corrections and turn-taking during conversation. We evaluated a number of factors affecting the suitability of BCI spellers for free communication, and make specific recommendations for improving classification accuracy and usability. Overall, we found that developing a BCI speller for free communication requires a focus on usability over reduced character selection time, and as such, future performance appraisals should be based on genuine free communication scenarios.

scholarly journals The Study of Influence of Sound on Visual ERP-Based Brain Computer Interface

Sensors ◽  
2020 ◽  
Vol 20 (4) ◽  
pp. 1203 ◽  
Author(s):  
Guizhi Xu ◽  
Yuwei Wu ◽  
Mengfan Li
Keyword(s):  
Information Transfer ◽  
High Performance ◽  
Brain Computer Interface ◽  
Event Related Potential ◽  
Training Data ◽  
Computer Interface ◽  
Information Transfer Rate ◽  
High Workload ◽  
The One ◽  
Study Designs

The performance of the event-related potential (ERP)-based brain–computer interface (BCI) declines when applying it into the real environment, which limits the generality of the BCI. The sound is a common noise in daily life, and whether it has influence on this decline is unknown. This study designs a visual-auditory BCI task that requires the subject to focus on the visual interface to output commands and simultaneously count number according to an auditory story. The story is played at three speeds to cause different workloads. Data collected under the same or different workloads are used to train and test classifiers. The results show that when the speed of playing the story increases, the amplitudes of P300 and N200 potentials decrease by 0.86 μV (p = 0.0239) and 0.69 μV (p = 0.0158) in occipital-parietal area, leading to a 5.95% decline (p = 0.0101) of accuracy and 9.53 bits/min decline (p = 0.0416) of information transfer rate. The classifier that is trained by the high workload data achieves higher accuracy than the one trained by the low workload if using the high workload data to test the performance. The result indicates that the sound could affect the visual ERP-BCI by increasing the workload. The large similarity of the training data and testing data is as important as the amplitudes of the ERP on obtaining high performance, which gives us an insight on how make to the ERP-BCI generalized.

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.

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 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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