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

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.

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Comparison of Modern Highly Interactive Flicker-Free Steady State Motion Visual Evoked Potentials for Practical Brain–Computer Interfaces

Brain Sciences ◽

10.3390/brainsci10100686 ◽

2020 ◽

Vol 10 (10) ◽

pp. 686

Author(s):

Piotr Stawicki ◽

Ivan Volosyak

Keyword(s):

Steady State ◽

Evoked Potentials ◽

Visual Evoked Potentials ◽

Information Transfer ◽

Minimum Energy ◽

Brain Computer Interfaces ◽

Steady State Motion ◽

Computer Interfaces ◽

Information Transfer Rate ◽

Visual Evoked

Motion-based visual evoked potentials (mVEP) is a new emerging trend in the field of steady-state visual evoked potentials (SSVEP)-based brain–computer interfaces (BCI). In this paper, we introduce different movement-based stimulus patterns (steady-state motion visual evoked potentials—SSMVEP), without employing the typical flickering. The tested movement patterns for the visual stimuli included a pendulum-like movement, a flipping illusion, a checkerboard pulsation, checkerboard inverse arc pulsations, and reverse arc rotations, all with a spelling task consisting of 18 trials. In an online experiment with nine participants, the movement-based BCI systems were evaluated with an online four-target BCI-speller, in which each letter may be selected in three steps (three trials). For classification, the minimum energy combination and a filter bank approach were used. The following frequencies were utilized: 7.06 Hz, 7.50 Hz, 8.00 Hz, and 8.57 Hz, reaching an average accuracy between 97.22% and 100% and an average information transfer rate (ITR) between 15.42 bits/min and 33.92 bits/min. All participants successfully used the SSMVEP-based speller with all types of stimulation pattern. The most successful SSMVEP stimulus was the SSMVEP1 (pendulum-like movement), with the average results reaching 100% accuracy and 33.92 bits/min for the ITR.

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A Novel Dictionary-Driven Mental Spelling Application Based on Code-Modulated Visual Evoked Potentials

Computers ◽

10.3390/computers8020033 ◽

2019 ◽

Vol 8 (2) ◽

pp. 33 ◽

Cited By ~ 2

Author(s):

Felix Gembler ◽

Ivan Volosyak

Keyword(s):

Evoked Potentials ◽

Visual Evoked Potentials ◽

Information Transfer ◽

Target Identification ◽

Computer Interfaces ◽

Information Transfer Rate ◽

Preset Time ◽

N Gram ◽

Dynamic Sliding Window ◽

Visual Evoked

Brain–computer interfaces (BCIs) based on code-modulated visual evoked potentials (c-VEPs) typically utilize a synchronous approach to identify targets (i.e., after preset time periods the system produces command outputs). Hence, users have only a limited amount of time to fixate a desired target. This hinders the usage of more complex interfaces, as these require the BCI to distinguish between intentional and unintentional fixations. In this article, we investigate a dynamic sliding window mechanism as well as the implementation of software-based stimulus synchronization to enable the threshold-based target identification for the c-VEP paradigm. To further improve the usability of the system, an ensemble-based classification strategy was investigated. In addition, a software-based approach for stimulus on-set determination is proposed, which allows for an easier setup of the system, as it reduces additional hardware dependencies. The methods were tested with an eight-target spelling application utilizing an n-gram word prediction model. The performance of eighteen participants without disabilities was tested; all participants completed word- and sentence spelling tasks using the c-VEP BCI with a mean information transfer rate (ITR) of 75.7 and 57.8 bpm, respectively.

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Analyzing Steady-State Visual Evoked Potentials for Effective User Response Detection for Brain-Computer Interfaces

Volume 3A: Biomedical and Biotechnology Engineering ◽

10.1115/imece2013-65592 ◽

2013 ◽

Author(s):

Pouria Riyahi ◽

Azim Eskandarian

Keyword(s):

Steady State ◽

Evoked Potentials ◽

Visual Evoked Potentials ◽

Information Transfer ◽

Signal To Noise Ratio ◽

Time Windows ◽

Time Step ◽

Response Detection ◽

Source Signals ◽

Visual Evoked

This article evaluates an M-order Adaptive Kalman filter analysis on Steady-State Visual Evoked Potentials (SSVEPs). This model is based on finding the original brain source signals from their combined observed EEG signals. At each time step, observed brain signals are filtered according to their ideal reference signals measured from 10, 11, 12 and 13 Hz LED stimuli. SSVEP response detection is based on maximum Signal to Noise Ratio (SNR) of the brain source signals. In each test, the average system accuracy is calculated with and without overlapped time-windows along with system Information Transfer Rate (ITR). The overall system accuracy and ITR are showing promising level of SSVEP detection for future online BCI systems.

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

International Journal of Neural Systems ◽

10.1142/s0129065714500191 ◽

2014 ◽

Vol 24 (06) ◽

pp. 1450019 ◽

Cited By ~ 149

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.

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Brain–computer interfaces based on code-modulated visual evoked potentials (c-VEP): a literature review

Journal of Neural Engineering ◽

10.1088/1741-2552/ac38cf ◽

2021 ◽

Author(s):

Víctor Martínez-Cagigal ◽

Jordy Thielen ◽

Eduardo Santamaría-Vázquez ◽

Sergio Pérez-Velasco ◽

Peter Desain ◽

...

Keyword(s):

Literature Review ◽

Evoked Potentials ◽

Visual Evoked Potentials ◽

High Speed ◽

Meta Analysis ◽

Future Research ◽

Brain Computer Interfaces ◽

Comprehensive Overview ◽

Computer Interfaces ◽

Visual Evoked

Abstract Objective. Code-modulated visual evoked potentials (c-VEP) have been consolidated in recent years as robust control signals capable of providing non-invasive brain–computer interfaces (BCIs) for reliable, high-speed communication. Their usefulness for communication and control purposes has been reflected in an exponential increase of related articles in the last decade. The aim of this review is to provide a comprehensive overview of the literature to gain understanding of the existing research on c-VEP-based BCIs, since its inception (1984) until today (2021), as well as to identify promising future research lines. Approach. The literature review was conducted according to the Preferred Reporting Items for Systematic reviews and Meta-Analysis (PRISMA) guidelines. After assessing the eligibility of journal manuscripts, conferences, book chapters and non-indexed documents, a total of 70 studies were included. A comprehensive analysis of the main characteristics and design choices of c-VEP-based BCIs was discussed, including stimulation paradigms, signal processing, modeling responses, applications, etc. Main results. The literature review showed that state-of-the-art c-VEP-based BCIs are able to provide an accurate control of the system with a large number of commands, high selection speeds and even without calibration. In general, a lack of validation in real setups was observed, especially regarding the validation with disabled populations. Future work should be focused toward developing self-paced c-VEP-based portable BCIs applied in real-world environments that could exploit the unique benefits of c-VEP paradigms. Some aspects such as asynchrony, unsupervised training, or code optimization still require further research and development. Significance. Despite the growing popularity of c-VEP-based BCIs, to the best of our knowledge, this is the first literature review on the topic. In addition to providing a joint discussion of the advances in the field, some future lines of research are suggested to contribute to the development of reliable plug-and-play c-VEP-based BCIs.

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