scholarly journals Asynchronous non-invasive high-speed BCI speller with robust non-control state detection

2018 ◽  
Author(s):  
Sebastian Nagel ◽  
Martin Spüler
Keyword(s):  
Information Transfer ◽  
High Speed ◽  
Brain Activity ◽  
Real Life ◽  
Fixed Time ◽  
Intentional Control ◽  
Non Invasive ◽  
Information Transfer Rate ◽  
State Detection ◽  
Control State

Brain-Computer Interfaces (BCIs) enable users to control a computer by using pure brain activity. Recent BCIs based on visual evoked potentials (VEPs) have shown to be suitable for high-speed communication. However, all recent high-speed BCIs are synchronous, which means that the system works with fixed time slots so that the user is not able to select a command at his own convenience, which poses a problem in real-world applications. In this paper, we present the first asynchronous high-speed BCI with robust distinction between intentional control (IC) and non-control (NC), with a nearly perfect NC state detection of only 0.075 erroneous classifications per minute. The resulting asynchronous speller achieved an average information transfer rate (ITR) of 122.7 bit/min using a 32 target matrix-keyboard. Since the method is based on random stimulation patterns it allows to use an arbitrary number of targets for any application purpose, which was shown by using an 55 target German QWERTZ-keyboard layout which allowed the participants to write an average of 16.1 (up to 30.7) correct case-sensitive letters per minute. As the presented system is the first asynchronous high-speed BCI speller with a robust non-control state detection, it is an important step for moving BCI applications out of the lab and into real-life.

scholarly journals A HIGH-SPEED BRAIN SPELLER USING STEADY-STATE VISUAL EVOKED POTENTIALS

2014 ◽  
Vol 24 (06) ◽  
pp. 1450019 ◽  
Author(s):  
MASAKI NAKANISHI ◽  
YIJUN WANG ◽  
YU-TE WANG ◽  
YASUE MITSUKURA ◽  
TZYY-PING JUNG
Keyword(s):  
Steady State ◽  
Information Transfer ◽  
High Speed ◽  
Target Identification ◽  
Real Life ◽  
Stimulus Presentation ◽  
Training Data ◽  
Information Transfer Rate ◽  
Electroencephalogram Eeg ◽  
Visual Evoked

Implementing a complex spelling program using a steady-state visual evoked potential (SSVEP)-based brain–computer interface (BCI) remains a challenge due to difficulties in stimulus presentation and target identification. This study aims to explore the feasibility of mixed frequency and phase coding in building a high-speed SSVEP speller with a computer monitor. A frequency and phase approximation approach was developed to eliminate the limitation of the number of targets caused by the monitor refresh rate, resulting in a speller comprising 32 flickers specified by eight frequencies (8–15 Hz with a 1 Hz interval) and four phases (0°, 90°, 180°, and 270°). A multi-channel approach incorporating Canonical Correlation Analysis (CCA) and SSVEP training data was proposed for target identification. In a simulated online experiment, at a spelling rate of 40 characters per minute, the system obtained an averaged information transfer rate (ITR) of 166.91 bits/min across 13 subjects with a maximum individual ITR of 192.26 bits/min, the highest ITR ever reported in electroencephalogram (EEG)-based BCIs. The results of this study demonstrate great potential of a high-speed SSVEP-based BCI in real-life applications.

scholarly journals A Hybrid Brain–Computer Interface for Real-Life Meal-Assist Robot Control

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4578
Author(s):  
Jihyeon Ha ◽  
Sangin Park ◽  
Chang-Hwan Im ◽  
Laehyun Kim
Keyword(s):  
User Interfaces ◽  
Information Transfer ◽  
Brain Computer Interface ◽  
False Positive Rate ◽  
Real Life ◽  
The Elderly ◽  
Computer Interface ◽  
Additional Time ◽  
Information Transfer Rate ◽  
Positive Rate

Assistant devices such as meal-assist robots aid individuals with disabilities and support the elderly in performing daily activities. However, existing meal-assist robots are inconvenient to operate due to non-intuitive user interfaces, requiring additional time and effort. Thus, we developed a hybrid brain–computer interface-based meal-assist robot system following three features that can be measured using scalp electrodes for electroencephalography. The following three procedures comprise a single meal cycle. (1) Triple eye-blinks (EBs) from the prefrontal channel were treated as activation for initiating the cycle. (2) Steady-state visual evoked potentials (SSVEPs) from occipital channels were used to select the food per the user’s intention. (3) Electromyograms (EMGs) were recorded from temporal channels as the users chewed the food to mark the end of a cycle and indicate readiness for starting the following meal. The accuracy, information transfer rate, and false positive rate during experiments on five subjects were as follows: accuracy (EBs/SSVEPs/EMGs) (%): (94.67/83.33/97.33); FPR (EBs/EMGs) (times/min): (0.11/0.08); ITR (SSVEPs) (bit/min): 20.41. These results revealed the feasibility of this assistive system. The proposed system allows users to eat on their own more naturally. Furthermore, it can increase the self-esteem of disabled and elderly peeople and enhance their quality of life.

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.

scholarly journals High-speed brain-computer communication interface based on code-modulated visual evoked potentials

2019 ◽  
pp. 26-31
Author(s):  
RK Grigoryan ◽  
DB Filatov ◽  
AYa Kaplan
Keyword(s):  
Evoked Potentials ◽  
Visual Evoked Potentials ◽  
Information Transfer ◽  
High Speed ◽  
Communication Interface ◽  
Computer Communication ◽  
Information Transfer Rate ◽  
Individual Traits ◽  
Command Input ◽  
Visual Evoked

Brain-computer interface (BCI) technologies are actively used in clinical practice to communicate with patients unable to speak and move. Such interfaces imply identifying potentials evoked by stimuli meaningful to the patient in his/her EEG and interpreting these potentials into inputs for the communication software. The stimuli can take form of highlighted letters on a screen, etc. This study aimed to investigate EEG indicators and assess the command input performance of a promising type of BCI utilizing the so-called code-modulated visual evoked potentials (CVEP) appearing in response to a certain sequence of highlights of the desired letter. The operation of the interface was studied on 15 healthy volunteers. During the experiments, we changed the speed of stimuli demonstration and inverted the order of flashing. It was established that the optimal speed of stimulation significantly depends on individual traits of the person receiving the stimuli, and inversion of their sequence does not affect operation of the interface. The median accuracy of selection of commands was as follows: 1 s stimulation cycle mode — 0.96 and 0.95 (information transfer rate 142 and 141 bit per minute); 2 s stimulation cycle mode — 1; 0.5 s cycle — 0.33. The evoked potentials were most expressed at the Oz site. It was assumed that CVEP-based brain-computer interfaces can be optimized through individualization of the set of stimulation parameters.

scholarly journals Clinical characterization of respiratory droplet production during common airway procedures using high-speed imaging

2020 ◽  
Author(s):  
SK Mueller ◽  
R Veltrup ◽  
B Jakubaß ◽  
JS Kempfle ◽  
S Kniesburges ◽  
...  
Keyword(s):  
Airway Management ◽  
High Speed ◽  
Clinical Care ◽  
Real Life ◽  
Invasive Ventilation ◽  
High Speed Imaging ◽  
Non Invasive ◽  
Non Invasive Ventilation ◽  
Management Procedures

AbstractBackgroundDuring the COVID-19 pandemic, a significant number of healthcare workers have been infected with SARS-CoV-2. However, there remains little knowledge regarding droplet dissemination during airway management procedures in real life settings.Methods12 different airway management procedures were investigated during routine clinical care. A high-speed video camera (1000 frames/second) was for imaging. Quantitative droplet characteristics as size, distance traveled, and velocity were computed.ResultsDroplets were detected in 8/12 procedures. The droplet trajectories could be divided into two distinctive patterns (type 1/2). Type 1 represented a ballistic trajectory with higher speed droplets whereas type 2 represented a random trajectory of slower particles that persisted longer in air. Speaking and coughing lead to a larger amount of droplets than non-invasive ventilation therapy. The use of tracheal cannula filters reduced the amount of droplets.ConclusionsRespiratory droplet patterns generated during airway management procedures follow two distinctive trajectories based on the influence of aerodynamic forces. Speaking and coughing produce more droplets than non-invasive ventilation therapy confirming these behaviors as exposure risks. Even large droplets may exhibit patterns resembling the fluid dynamics smaller airborne aerosols that follow the airflow convectively and may place the healthcare provider at risk.

scholarly journals The Self-Paced Graz Brain-Computer Interface: Methods and Applications

2007 ◽  
Vol 2007 ◽  
pp. 1-9 ◽  
Author(s):  
Reinhold Scherer ◽  
Alois Schloegl ◽  
Felix Lee ◽  
Horst Bischof ◽  
Janez Janša ◽  
...  
Keyword(s):  
Brain Activity ◽  
Brain Computer Interface ◽  
Computer Game ◽  
Google Earth ◽  
The Self ◽  
Computer Interface ◽  
Emg Activity ◽  
Intentional Control ◽  
Electroencephalogram Eeg ◽  
Control State

We present the self-paced 3-class Graz brain-computer interface (BCI) which is based on the detection of sensorimotor electroencephalogram (EEG) rhythms induced by motor imagery. Self-paced operation means that the BCI is able to determine whether the ongoing brain activity is intended as control signal (intentional control) or not (non-control state). The presented system is able to automatically reduce electrooculogram (EOG) artifacts, to detect electromyographic (EMG) activity, and uses only three bipolar EEG channels. Two applications are presented: the freeSpace virtual environment (VE) and the Brainloop interface. The freeSpace is a computer-game-like application where subjects have to navigate through the environment and collect coins by autonomously selecting navigation commands. Three subjects participated in these feedback experiments and each learned to navigate through the VE and collect coins. Two out of the three succeeded in collecting all three coins. The Brainloop interface provides an interface between the Graz-BCI and Google Earth.

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.

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