This paper presents a user-friendly SSVEP-based Brain–Computer Interface (BCI) system for secure transaction authentication using a 6-digit ternary Transaction Authentication Number (TAN). The system eliminates training and minimizes cognitive load via a single-stimulus design and provides a novel kind of BCI application. A filter bank canonical correlation analysis (FBCCA) based algorithm enables accurate classification without prior calibration. Sixteen participants completed five authentication trials each using a web-based donation task. All participants performed at least one successful authentication, with nine participants (56%) achieving perfect accuracy across all trials. The system reached an average classification accuracy of 94.6% and a mean authentication time of 17.2 seconds. Compared to a recent cVEP-based TAN system, this reflects a 35.6 percentage point improvement in accuracy and a 61.9% reduction in authentication time. Participants reported low mental and physical strain, which indicates high usability. Future work includes transitioning to dry electrodes and embedded real-time processing to support deployment in real-world applications.

A Brain–computer interface (BCI) enables direct control of external devices through neural activity. Among BCI paradigms, visual evoked potentials (VEPs) are widely used because they harness the brain’s response to visual stimuli to deliver an intuitive and inclusive control interface. However, these systems typically rely on low-frequency flickering stimuli to evoke strong neural responses, which can cause discomfort and fatigue. While previous attempts have sought to mitigate this discomfort, eliminating it entirely would be ideal. This could be achieved with high-frequency flickering operating above the critical fusion frequency threshold, which is invisible to the human eye yet still evokes a distinct neural imprint. While this neural response is distinguishable from that of a non-flickering stimulus, reliably differentiating between two different high-frequency stimuli remains challenging.Our novel Imperceptible VEP (I-VEP) paradigm combines imperceptible high-frequency flicker segments with static (non-flickering) intervals to create patterns analogous to steady-state VEP (SSVEP) and code-modulated VEP (cVEP). From this paradigm, we define two variants: Imperceptible Steady-State VEP (I-SSVEP) and Imperceptible Code-Modulated VEP (I-cVEP). Our initial I-cVEP experiments demonstrate its feasibility, achieving a mean bit-wise accuracy of over 92% across 27 participants. These findings may represent a significant first step toward fully comfortable visual-stimulus BCIs, enabling broader adoption and improved usability.

This study investigated the potential of steady-state motion visual evoked potential (SSMVEP) using 3D stimuli as a preliminary step towards developing a brain-computer interface (BCI) for command selection in a virtual reality (VR) environment. Several movement patterns and stimulus shapes were examined to determine the optimal stimulus configurations for eliciting strong and robust SSMVEP responses. The aim was to identify the most effective stimulus and SSMVEP movement to improve user comfort, interaction and control accuracy for future immersive VR applications presented in a head-mounted display (HMD). The proposed shapes, cube and diamond, yielded comparable performance in the steady-state visual evoked potential (SSVEP) condition; however, for the zooming and rotating movements, the cube achieved higher average accuracies. In the frequency spectrum, the average signal-to-noise ratio (SNR) values of the diamonds were comparable to those of the cubes. Subjective results showed that participants had a preference for the rotating diamonds.

By utilizing brain signals, the Brain-Computer Interface (BCI) enables non-muscular communication. In recent decades, BCI systems —particularly speller interfaces— have offered a variety of graphical user interfaces (GUIs). Many attempts had been made to improve the system's user-friendliness and speller speed. In this paper we present a web-based BCI speller based on Code-Modulated Visual Evoked Potentials (cVEPs), which can be accessed at https://bci-Iab.hochschule-rhein-waal.de/en/cvepspeller/. As a result, this web speller is now available to BCI researchers worldwide free of charge, and can be used with variety of own classifier applications. This web speller was successfully tested with the majority of modern web browsers. In the three-step web speller, each character can be selected by navigating through three distinct web interface screens. In this study the web-basedcVEP speller was tested and compared to our “local speller” (incorporating the GUI and the signal processing in one application) by seven subjects, who were asked to spell the words “BCI_LAB” and “KLEVE”. All subjects were able to perform the spelling tasks, with a mean accuracy of 95.02% and an average Information Transfer Rate (ITR) of 42.55 bits/min, compared to our “local speller” with a mean accuracy of 93.81 % and an average ITR of 48.58 bits/min, respectively. The results showed similar values, confirming the suitability of the suggested web speller for the representation of thecVEP stimuli.

Brain-computer interfaces (BCIs) like e.g. different types of EEG-based BCI spellers (up to date the most common BCI applications) allow new methods of control and interaction with computers and machines. The impact of background music and noise on user's performance (information transfer rate - ITR and accuracy) has already been investigated for different types of spellers, and usually led to a distractive, decreasing effect on the BCI performance, though there is always room for improving the subjective user experience. In this study, 11 participants used a cVEP-based BCI to perform two spelling tasks “BCI_AND_MUSIC” and “CONTRARY”, while listening via headphones to a standard instrumental song, a self-chosen instrumental song (“own music”), or to noise conditions: “no music” and “white noise”. Objective factors such as the blood pressure and heart rate were measured after each spelling task, and questionnaires were answered. The BCI accuracy was close to 100% in most cases, while the lowest accuracy was reached while listening to the individually selected, self-chosen music (“own music”). Similarly, the ITR was the highest in case of “no music” and lowest in case of “own music” (which, on the other hand, resulted in a better mood and higher excitement of the participants). Thus, while the general condition of “no music” could be recommended for further BCI experiments, additional research regarding individual factors like musicality or listening habits of BCI participants should be considered.

Brain-computer interfaces (BCIs) based on code-modulated visual evoked potentials (c-VEPs) hold promise to serve as a fast and reliable hands-free communication tool for people with severe disabilities. A c-VEP BCI application presents flickering target objects (e. g. letters of a keyboard) coded with different time-lags of a code pattern. Template matching methods are used to identify the target of interest. Unfortunately, this approach requires a training session, in which several trials of EEG data are recorded and analysed. Long training sessions are necessary to ensure good signal-to-noise ratios. For the user, these training sessions may be tedious. Especially for patients, who may use the system on a daily basis e. g. for communication, alternative approaches are desirable. This paper investigates the feasibility of session-to-session transfer of EEG templates for c-VEP BCIs, where templates recorded in a previous session are used, so the application could be used instantly. Ten healthy participants went through training and copy-spelling tasks in two experimental sessions (they were scheduled two weeks apart). In the second session, the templates recorded in the first session were used. Eight participants yielded good results with the session-to-session transfer approach with accuracies of 97.1% and information transfer rates of 85.7 bit/min on average. For these participants, the results were not significantly different from the values achieved using the standard approach (training in the same session). For two participants, however, the system was not controllable with the priorly recorded templates. The results demonstrate that for most users daily recalibration is not required.

The brain-computer interface (BCI) and the tracking of eye gaze provide modalities for human-machine communication and control. In this article, we provide the evaluation of a collaborative BCI and eye gaze approach, known as a hybrid BCI. The combined inputs interact with a virtual environment to provide actuation according to a four-way menu system. The following two approaches are evaluated: first, steady-state visual evoked potential (SSVEP) BCI with on-screen stimulation; second, hybrid BCI, which combined eye gaze and SSVEP for navigation and selection. A study comprises participants without known brain injury (non-BI, N = 30) and participants with known brain injury (BI, N = 14). A total of 29 out of 30 non-BI participants can successfully control the hybrid BCI, while nine out of the 14 BI participants are able to achieve control, as evidenced by task completion. The hybrid BCI provides a mean accuracy of 99.84% in the cohort of non-BI participants and 99.14% in the cohort of BI participants. Information transfer rates are 24.41 bpm in non-BI participants and 15.87 bpm in BI participants. The research goal is to quantify usage of SSVEP and ET approaches in cohorts of non-BI and BI participants. The hybrid is the preferred interaction modality for most participants for both cohorts. When compared to non-BI participants, it is encouraging that nine out of 14 participants with known BI can use the hBCI technology with equivalent accuracy and efficiency, albeit with slower transfer rates.

Brain-Computer Interfaces (BCIs) based on Steady-State Visually Evoked Potentials (SSVEPs) can be used as hand-free control device. To utilize this control method in a real life scenario, we created a system in which a smart home is controlled by BCI. Six devices in the smart home environment could be controlled with the BCI system: The entrance door, the wardrobe, the kitchens' worktop and drawers, the light system of all the rooms and a guide light. In the presented paper, the visual stimuli for the BCI were placed at multiple screens in the smart home (placed at different locations such as the kitchen and the living room). The processing was done on one computer, located in the living room. The placement of the visual stimuli corresponded to the actuators that were controlled, e.g. the kitchen drawers were linked to the stimuli displayed in the kitchen. An online experiment was conducted where participants went through a scenario consisting of thirteen SSVEP-BCI selections in total. Eight healthy participants took part in the experiments. For BCI signal acquisition, a mobile EEG amplifier was used. Participants walked freely around the rooms during the experiment. An average accuracy of 81 % was achieved, which suggests that the SSVEP-system is suitable to control the external devices in the smart home, and that the system can be expanded to involve more actuators.

Brain-Computer Interfaces (BCIs) based on code-modulated visual evoked potentials can be used as hand-free communication tool for severely disabled people. In this paper we propose a filter bank design for c-VEP BCIs based on alpha, beta and gamma sub-bands. The approach was tested using a dictionary driven spelling application utilizing flexible time-windows. The graphical user interface offers word suggestions that are updated after each selection. The system was tested with 18 healthy participants. Performance of a word and a sentence spelling task was analyzed. Remarkably, in the word spelling task, all participants reached 100 % accuracy. In the sentence spelling task the mean accuracy was still extremely high (97 %). Furthermore, to assess the speed of the system, information transfer rate (ITR) and output characters per minute (OCM) were calculated. Mean ITRs of 149.3 bpm and 93.1 bpm were reached in word and sentence spelling; the mean OCM was 29.9 chars/min and 32.1 chars/min.

In this paper, the performance of a steady-state visually evoked potential (SSVEP)-based brain-computer interface (BCI) was evaluated after reducing the number of electroencephalography (EEG) electrodes used for recording. Our aim was the comparison of the performance using a different number of electrodes, as well as determining the best electrode locations. The main benefit of determining the most efficient electrode sites can be the shortening of the preparation time needed for the setup of the BCI system. The performance of the BCI was measured with a different number of EEG electrodes reduced from initially sixteen to six electrodes as the intermediate step, and finally to four electrodes. Seventeen subjects participated in this study, seven were male and ten female. The mean ITRs were 27.50 bit/min, 24.09 bit/min, and 23.23 bit/min for sixteen, six and four EEG electrodes, respectively. However, not all users managed to use the system with less electrodes, as one user did not manage to control the system with six electrodes, and four of the seventeen users were not able to finish the spelling tasks with four electrodes. With a reduced number of electrodes, a high accuracy can be achieved while retaining a fair spelling speed.

Brain-computer interfaces (BCIs) measure brain activity and can be used by impaired patients to close the gap to the real world. In BCI applications based on steady-state visual evoked potentials (SSVEPs), the user focuses on stimuli with different frequencies. During the last decades, the effect of background music on the cognitive performance has been discussed controversially in several studies. This study investigated the influence of background music on the performance of an SSVEP-based BCI speller based on objective (ITR, accuracy, and vital parameters) and subjective measures (familiarity, excitement, mood, valence, and support). The experiments were performed with 18 healthy participants (age 25.17± 4.4 years, eleven females) and comprised a control condition without music, one with white noise, and four conditions with “exciting” and “relaxing” pre-selected and self-chosen music. While the ITR decreased for the noise and music conditions compared to the one without music, an accuracy increase was shown in the white noise and “exciting” self-chosen music condition. The physiological parameters and subjective opinions did not necessarily match the achieved performances. Excitement, mood, and other factors including familiarity of the music and the underlying musical structure were identified as having an influence on the performance values. In sum, this study shows and discusses both positive and negative effects of background music on the BCI performance and prepares further investigations concerning the possible application of BCI systems in noisy environments.

Event-Related Potential (ERP) is one of the brain signal features which are used in Electroencephalography (EEG) based research and application development, such as Brain-Computer Interface (BCI) applications. Recently, single-trial ERP has been one of the main interests of researchers in the field of BCI and neuroscience. In this paper, an offline study which evaluated the feasibility of developing an online BCI guessing game, based on single-trial ERP, was presented. The objective was to determine the optimal methods and parameters needed to achieve high online classification accuracy and performance. Eight subjects participated in our experiments to collect the data for the offline study. Each subject had to choose one out of six cards displayed on a computer monitor. Three different algorithms of Linear Discriminant Analysis (LDA) were used for classifying the cards into targets and non-targets. Canonical Correlation Analysis (CCA) was applied as a spatial filter for the 16-channel data. Additionally, the data were analysed and classified per channel to deduce which channel reached the higher performance. The results proved the feasibility of the online application. The best performance was achieved with the personalised data and by taking the majority vote of the three LDA algorithms.

In this paper the influence of different backgrounds was investigated on SSVEP-based BCI performance, with a focus on measuring objective user fatigue. We tested three different backgrounds: a black screen, white noise, and a video, with a BCI spelling application (in which four distinct frequencies were used). We measured information transfer rate (ITR), accuracy, and several parameters which could indicate user fatigue levels. The data were recorded with EEG. The level of comfort before and after the experiment, as well as the subjective level of fatigue were assessed using questionnaires, while the objective evaluation was done using the measured indexes. Eight healthy participants were tested. With the used objective fatigue evaluation method, no significant differences were found between the different background scenarios. Evaluating the study as a whole showed significantly decreased alpha-band activity (p = 0.03) at the end and a significantly increased subjective user fatigue. This experiment was conducted to investigate the level of fatigue caused by SSVEP-based BCIs in noisy environments.

Many individuals require Aided Augmentative and Alternative Communication (AAAC) to communicate, especially when traditional AAC, like sign language, is not an option due to some movement impairments. This paper presents a high tech AAAC using Surface Electromyography (sEMG) signals as an input modality for the direct control of a speller presented on a Graphical User Interface (GUI). The GUI displays alphabetical letters, special characters, and a delete option. The aim of this study is to introduce and examine the performance of a three-steps sEMG-based speller which does not depend on the movement of a system cursor. Seven subjects participated in the experiment to test the spelling system. Each participant had to undergo a familiarization task first, after which the main spelling task took place. Experimental data were recorded and the resulted mean accuracy and information transfer rate (ITR) for the main spelling task were 89.10% and 55.10 bits/min, respectively, at an average spelling speed of 10.29 char/min. Results and users' feedback also indicated that the spelling performance could be further enhanced through training and the regular use of the speller.

Brain-Computer Interface (BCI) interprets brain signals, which are measured by an electroencephalogram (EEG), allowing people to communicate without the need for any muscular movement. One of the most commonly discussed applications in literature is BCI speller as a communication modality for people with disabilities. In Brain-Neural Computer Interface (BNCI) other biosignals are merged with the brain signals to combine the advantages of both. Consequently, the here-studied hybrid BCI system discusses the parallel usage of an Steady-State Visual Evoked Potential (SSVEP) BCI and surface-electromyographic (sEMG) activity to accomplish a faster performance for a more accurate spelling application. sEMG signal was used for the activation of target selection for an SSVEP-based speller. Eight participants carried out three copy-spelling tasks for each system (six in total) to compare between both paradigms: SSVEP alone and the hybrid speller. Results showed that the hybrid speller can achieve faster performances without affecting the accuracy (mean accuracy 92.37%, 93.75%, and 100%, mean ITR 37.37, 33.41, and 31.05 bits/min), verifying our hypothesis. In addition, participants who experienced difficulties controlling the SSVEP speller were able to control the hybrid system.

Brain-Computer Interface (BCI) provides a non-muscular communication by using the brain signals. During last decades, the BCI-systems provided various graphical user interfaces, especially the speller interfaces. Efforts had been made to increase the speller speed and user friendliness of the system. In this paper, we are introducing a browser-driven SSVEP-based BCI web speller (accessible at: https://bci-lab.hochschule-rhein-waal.de/en/speller/). This web speller can be used with the help of major existing web browsers. Thus, all the international researchers working in the field of BCI can access this web speller online free of charge, and they can run this speller using their own classifier applications. In the presented three-step web speller, the user can select a desired character by going through three different steps of the web interface. The browser-driven BCI speller was tested in this study by ten subjects, who were asked to spell the words "BCI_LAB" and "KLEVE". All of the subjects were able to perform the spelling tasks, with the mean accuracy of 94.5% and an average information transfer rate (ITR) of 12.7 bits/min.

Brain-Computer Interfaces (BCIs) allow communication and control of the environment without the use of peripheral muscles. One of the standard BCI paradigms is based on steady state visual evoked potentials (SSVEPs), brain signals induced by gazing at a constantly flickering target. In this article, a VR SSVEP-based steering simulation is presented and evaluated in comparison to a standard desktop version. Three control classes were used to control the application. The experimental task was to steer a virtual vacuum robot and collect 10 dust piles (for this, at least 31 command classifications were required). Participants were instructed to complete the task twice, using a head mounted display (HMD) and the laptop screen for visual stimulation. All participants were able to complete the task in both scenarios. Mean accuracies of 98.91% and 97.48% and mean ITRs of 23.96 and 20.71 bits/min were achieved for the HMD and desktop control, respectively. On average, the number of commands needed to complete the task in the online experiment was 32.00 and 32.75, for the HMD and desktop scenario, respectively.

Brain-Computer Interfaces (BCIs) allow users to communicate and to control their environment without the use of peripheral muscles. One of the commonly used BCI paradigms is called steady state visual evoked potentials (SSVEPs). It is controlled by brain signals induced through gazing at a constantly flickering light source. In this article, a VR-based SSVEP-controlled simulation is presented and evaluated, in comparison to the standard desktopbased version. Three control classes were used to control the application. The experimental task was to navigate (in first person view) through a virtual maze and reach the desired destination (the optimal path required at least 26 commands). Participants performed the task twice, using either a head mounted display (HMD), or the laptop screen, to compare the paths and investigate the spatial awareness with the HMD. During the first run, the participants needed to explore the maze (different corridors and doors on 3 levels) in order to reach the destination (a teleporter room). There were three commands available for the user: "Turn left", "Forward", and "Turn right". All participants were able to complete both versions (HMD and laptop screen). On average, the number of commands needed to complete the task in the online experiment was 51 and 62 for the HMD and laptop screen, respectively. The average time of the task for the HMD scenario was 287.16 seconds and for the laptop 435.79 seconds, almost 51.75% higher. We found, that the participants achieved a better spatial awareness with the HMD setup.

Brain-computer interfaces (BCIs) based on code-modulated potentials (cVEPs) identify a target usually in the synchronous way (i.e. after a preset time period the system will produce a command output). Hence, users have only a limited amount of time to fixate a desired target. For the practical usability of BCI spellers it is important to distinguish between intentional and unintentional fixations. In this paper we propose the use of threshold-based target identification methods for the cVEP paradigm. These methods were tested with a dictionary driven spelling application utilizing eight flashing targets. In this respect, an n-gram word prediction model was implemented. The performance of ten healthy participants was evaluated in an online experiment. All participants completed different German sentences using the cVEP BCI with a mean information transfer rate (ITR) of 31.08 bpm.

Brain–Computer Interface (BCI) systems analyze brain signals to generate control commands for computer applications or external devices. Utilized as alternative communication channel, BCIs have the potential to assist people with severe motor disabilities to interact with their environment and to participate in daily life activities. Handicapped people from all age groups could benefit from such BCI technologies. Although some papers have previously reported slightly worse BCI performance by older subjects, in many studies BCI systems were tested with young subjects only. In the presented paper age-associated differences in BCI performance were investigated. We compared accuracy and speed of a steady-state visual evoked potential (SSVEP)-based BCI spelling application controlled by participants of two different equally sized age groups. Twenty subjects (eleven female and nine male) participated in this study; each age group consisted of ten subjects, ranging from 19 to 27 years and from 64 to 76 years. Our results confirm that elderly people may have a deteriorated information transfer rate (ITR). The mean (SD) ITR of the young age group was 27.36 (6.50) bit/min while the elderly people achieved a significantly lower ITR of 16.10 (5.90) bit/min. The average time window length associated with the signal classification was usually larger for the participants of advanced age. These findings show that the subject age must be taken into account during the development of SSVEP-based applications.

The main aim behind the design of rehabilitation robotic systems is to support disabled people in daily life situations as well as in the working environment. This requires the autonomous execution of different tasks. In order to achieve the ability of the robotic system to operate autonomously, the sensor system for the scene observation providing the necessary inputs for the manipulator control is essential. In this paper, the main emphasis is on the development and integration of the closed-loop controls at the visual sensory input level in order to increase the robustness of the vision-based system control. Moreover, the integration of additional sensors in order to support the vision system and to increase the reliability of the whole robotic system is discussed.