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 An open interface system for non-invasive brain-to-brain free communication between naive human participants

2018 ◽  
Author(s):  
Angela Ingrid Renton ◽  
Jason B Mattingley ◽  
David R Painter
Keyword(s):  
Communication Systems ◽  
Information Transfer ◽  
High Performance ◽  
Word Association ◽  
Brain Activity ◽  
Free Association ◽  
Open Communication ◽  
Free Communication ◽  
Non Invasive ◽  
Human Participants

Free and open communication is fundamental to modern life. Brain-computer interfaces (BCIs), which translate measurements of the user's brain activity into computer commands, present emerging forms of hands-free communication. BCI communication systems have long been used in clinical settings for patients with paralysis and other motor disorders, and yet have not been implemented for free communication between healthy, BCI-naive users. Here, in two studies, we developed and validated a high-performance non-invasive BCI communication system, and examined its feasibility for communication during free word association and unprompted free conversation. Our system, focusing on usability for free communication, produced information transfer rates sufficient and practical for free association and brain-to-brain conversation (~5.7 words/minute). Our findings suggest that performance appraisals for BCI systems should incorporate the free communication scenarios for which they are ultimately intended. To facilitate free and open communication in healthy users and patients, we have made our source code and data open access.

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 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.

scholarly journals A Review of Hybrid Brain-Computer Interface Systems

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Setare Amiri ◽  
Reza Fazel-Rezai ◽  
Vahid Asadpour
Keyword(s):  
Transfer Rate ◽  
Information Transfer ◽  
Brain Activity ◽  
Brain Computer Interface ◽  
Activity Patterns ◽  
Computer Interface ◽  
Advantages And Disadvantages ◽  
Research Activities ◽  
Information Transfer Rate ◽  
Bci System

Increasing number of research activities and different types of studies in brain-computer interface (BCI) systems show potential in this young research area. Research teams have studied features of different data acquisition techniques, brain activity patterns, feature extraction techniques, methods of classifications, and many other aspects of a BCI system. However, conventional BCIs have not become totally applicable, due to the lack of high accuracy, reliability, low information transfer rate, and user acceptability. A new approach to create a more reliable BCI that takes advantage of each system is to combine two or more BCI systems with different brain activity patterns or different input signal sources. This type of BCI, called hybrid BCI, may reduce disadvantages of each conventional BCI system. In addition, hybrid BCIs may create more applications and possibly increase the accuracy and the information transfer rate. However, the type of BCIs and their combinations should be considered carefully. In this paper, after introducing several types of BCIs and their combinations, we review and discuss hybrid BCIs, different possibilities to combine them, and their advantages and disadvantages.

scholarly journals World’s Fastest Brain-Computer Interface: Combining EEG2Code with Deep Learning

2019 ◽  
Author(s):  
Sebastian Nagel ◽  
Martin Spüler
Keyword(s):  
Deep Learning ◽  
Visual Information ◽  
Information Transfer ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Non Invasive ◽  
Information Transfer Rate ◽  
Powerful Approach ◽  
Utility Rate ◽  
Average Utility

AbstractIn this paper, we present a Brain-Computer Interface (BCI) that is able to reach an information transfer rate (ITR) of more than 1200 bit/min using non-invasively recorded EEG signals. By combining the EEG2Code method with deep learning, we present an extremely powerful approach for decoding visual information from EEG. This approach can either be used in a passive BCI setting to predict properties of a visual stimulus the person is viewing, or it can be used to actively control a BCI spelling application. The presented approach was tested in both scenarios and achieved an average ITR of 701 bit/min in the passive BCI approach with the best subject achieving an online ITR of 1237 bit/min. The presented BCI is more than three times faster than the previously fastest BCI and allows to discriminate 500,000 different visual stimuli based on 2 seconds of EEG data with an accuracy of up to 100 %. When using the approach in an asynchronous BCI for spelling, we achieved an average utility rate of 175 bit/min, which corresponds to an average of 35 error-free letters per minute. As we observe a ceiling effect where more powerful approaches for brain signal decoding do not translate into better BCI control anymore, we discuss if BCI research has reached a point where the performance of non-invasive BCI control cannot be substantially improved anymore.

NON-INVASIVE BCI METHOD: EEG - ELECTROENCEPHALOGRAPHY

2019 ◽  
pp. 139-145
Author(s):  
Selma Büyükgöze
Keyword(s):  
Brain Computer Interface ◽  
Computer Interface ◽  
Electrode Placement ◽  
Eeg Signals ◽  
Non Invasive ◽  
Brain Signals ◽  
Brain States ◽  
The Brain

Brain Computer Interface consists of hardware and software that convert brain signals into action. It changes the nerves, muscles, and movements they produce with electro-physiological signs. The BCI cannot read the brain and decipher the thought in general. The BCI can only identify and classify specific patterns of activity in ongoing brain signals associated with specific tasks or events. EEG is the most commonly used non-invasive BCI method as it can be obtained easily compared to other methods. In this study; It will be given how EEG signals are obtained from the scalp, with which waves these frequencies are named and in which brain states these waves occur. 10-20 electrode placement plan for EEG to be placed on the scalp will be shown.

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.

Pilot Study on Using Innovative Counting Peaks Method for Assessment Purposes of the EEG Data Recorded from a Single-Channel Non-Invasive Brain-Computer Interface

2021 ◽  
Author(s):  
Natalia Browarska ◽  
Jaroslaw Zygarlicki ◽  
Mariusz Pelc ◽  
Michal Niemczynowicz ◽  
Malgorzata Zygarlicka ◽  
...  
Keyword(s):  
Pilot Study ◽  
Single Channel ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Non Invasive ◽  
Eeg Data

scholarly journals EEG Waveform Analysis of P300 ERP with Applications to Brain Computer Interfaces

2018 ◽  
Vol 8 (11) ◽  
pp. 199 ◽  
Author(s):  
Rodrigo Ramele ◽  
Ana Villar ◽  
Juan Santos
Keyword(s):  
Brain Computer Interface ◽  
Waveform Analysis ◽  
Computer Interface ◽  
Clinical Tool ◽  
Non Invasive ◽  
Computer Interfaces ◽  
Brain Signals ◽  
Public Dataset ◽  
Imaging Sensor ◽  
Clinical Eeg

The Electroencephalography (EEG) is not just a mere clinical tool anymore. It has become the de-facto mobile, portable, non-invasive brain imaging sensor to harness brain information in real time. It is now being used to translate or decode brain signals, to diagnose diseases or to implement Brain Computer Interface (BCI) devices. The automatic decoding is mainly implemented by using quantitative algorithms to detect the cloaked information buried in the signal. However, clinical EEG is based intensively on waveforms and the structure of signal plots. Hence, the purpose of this work is to establish a bridge to fill this gap by reviewing and describing the procedures that have been used to detect patterns in the electroencephalographic waveforms, benchmarking them on a controlled pseudo-real dataset of a P300-Based BCI Speller and verifying their performance on a public dataset of a BCI Competition.

Sign in / Sign up

Export Citation Format

Share Document