cVEP Training Data Validation-Towards Optimal Training Set Composition from Multi-Day Data.

This paper investigates the effects of the repetitive block-wise training process on the classification accuracy for a code-modulated visual evoked potentials (cVEP)-based brain-computer interface (BCI). The cVEP-based BCIs are popular thanks to their autocorrelation feature. The cVEP-based stimuli are generated by a specific code pattern, usually the m-sequence, which is phase-shifted between the individual targets. Typically, the cVEP classification requires a subject-specific template (individually created from the user's own pre-recorded EEG responses to the same stimulus target), which is compared to the incoming electroencephalography (EEG) data, using the correlation algorithms. The amount of the collected user training data determines the accuracy of the system. In this offline study, previously recorded EEG data collected during an online experiment with 10 participants from multiple sessions were used. A template matching target identification, with similar models as the task-related component analysis (TRCA), was used for target classification. The spatial filter was generated by the canonical correlation analysis (CCA). When comparing the training models from one session with the same session's data (intra-session) and the model from one session with the data from the other session (inter-session), the accuracies were (94.84%, 94.53%) and (76.67%, 77.34%) for intra-sessions and inter-sessions, respectively. In order to investigate the most reliable configuration for accurate classification, the training data blocks from different sessions (days) were compared interchangeably. In the best training set composition, the participants achieved an average accuracy of 82.66% for models based only on two training blocks from two different sessions. Similarly, at least five blocks were necessary for the average accuracy to exceed 90%. The presented method can further improve cVEP-based BCI performance by reusing previously recorded training data.

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

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.

Asynchronous c-VEP communication tools-efficiency comparison of low-target, multi-target and dictionary-assisted BCI spellers.

Keyboards and smartphones allow users to express their thoughts freely via manual control. Hands-free communication can be realized with brain-computer interfaces (BCIs) based on code-modulated visual evoked potentials (c-VEPs). Various variations of such spellers have been developed: Low-target systems, multi-target systems and systems with dictionary support. In general, it is not clear which kinds of systems are optimal in terms of reliability, speed, cognitive load, and visual load. The presented study investigates the feasibility of different speller variations. 58 users tested a 4-target speller and a 32-target speller with and without dictionary functionality. For classification, multiple individualized spatial filters were generated via canonical correlation analysis (CCA). We used an asynchronous implementation allowing non-control state, thus aiming for high accuracy rather than speed. All users were able to control the tested spellers. Interestingly, no significant differences in accuracy were found: 94.4%, 95.5% and 94.0% for 4-target spelling, 32-target spelling, and dictionary-assisted 32-target spelling. The mean ITRs were highest for the 32-target interface: 45.2, 96.9 and 88.9 bit/min. The output speed in characters per minute, was highest in dictionary-assisted spelling: 8.2, 19.5 and 31.6 characters/min. According to questionnaire results, 86% of the participants preferred the 32-target speller over the 4-target speller.

Comparison of Different Visual Feedback Methods for SSVEP-Based BCIs.

In this paper we compared different visual feedback methods, informing users about classification progress in a steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) speller application. According to results from our previous studies, changes in stimulus size and contrast as online feedback of classification progress have great impact on BCI performance in SSVEP-based spellers. In this experiment we further investigated these effects, and tested a 4-target SSVEP speller interface with a much higher number of subjects. Five different scenarios were used with variations in stimulus size and contrast, "no feedback", "size increasing", "size decreasing", "contrast increasing", and "contrast decreasing". With each of the five scenarios, 24 participants had to spell six letter words (at least 18 selections with this three-steps speller). The fastest feedback modalities were different for the users, there was no visual feedback which was generally better than the others. With the used interface, six users achieved significantly better Information Transfer Rates (ITRs) compared to the "no feedback" condition. Their average improvement by using the individually fastest feedback method was 46.52%. This finding is very important for BCI experiments, as by determining the optimal feedback for the user, the speed of the BCI can be improved without impairing the accuracy.

Five Shades of Grey: Exploring Quintary m-Sequences for More User-Friendly c-VEP-Based BCIs.

Responsive EEG-based communication systems have been implemented with brain-computer interfaces (BCIs) based on code-modulated visual evoked potentials (c-VEPs). The BCI targets are typically encoded with binary m-sequences because of their autocorrelation property; the digits one and zero correspond to different target colours (usually black and white), which are updated every frame according to the code. While binary flickering patterns enable high communication speeds, they are perceived as annoying by many users. Quintary (base 5) m-sequences, where the five digits correspond to different shades of grey, may yield a more subtle visual stimulation. This study explores two approaches to reduce the flickering sensation: (1) adjusting the flickering speed via refresh rates and (2) applying quintary codes. In this respect, six flickering modalities are tested using an eight-target spelling application: binary patterns and quintary patterns generated with 60, 120, and 240 Hz refresh rates. This study was conducted with 18 nondisabled participants. For all six flickering modalities, a copy-spelling task was conducted. According to questionnaire results, most users favoured the proposed quintary over the binary pattern while achieving similar performance to it (no statistical differences between the patterns were found). Mean accuracies across participants were above 95%, and information transfer rates were above 55 bits/min for all patterns and flickering speeds.

Towards solving of the Illiteracy phenomenon for VEP-based brain-computer interfaces.

Brain-Computer Interface (BCI) systems use brain activity as an input signal and enable communication without requiring bodily movement. This novel technology may help impaired patients and users with disabilities to communicate with their environment. Over the years, researchers investigated the performance of subjects in different BCI paradigms, stating that 15%-30% of BCI users are unable to reach proficiency in using a BCI system and therefore were labelled as BCI illiterates. Recent progress in the BCIs based on the visually evoked potentials (VEPs) necessitates re-considering of this term, as very often all subjects are able to use VEP-based BCI systems. This study examines correlations among BCI performance, personal preferences, and further demographic factors for three different modern visually evoked BCI paradigms: (1) the conventional Steady-State Visual Evoked Potentials (SSVEPs) based on visual stimuli flickering at specific constant frequencies (fVEP), (2) Steady-State motion Visual Evoked Potentials (SSmVEP), and (3) code-modulated Visual Evoked Potentials (cVEP). Demographic parameters, as well as handedness, vision correction, BCI experience, etc., have no significant effect on the performance of VEP-based BCI. Most subjects did not consider the flickering stimuli annoying, only 20 out of a total of 86 participants indicated a change in fatigue during the experiment. 83 subjects were able to successfully finish all spelling tasks with the fVEP speller, with a mean (SD) information transfer rate of 31.87 bit/min (9.83) and an accuracy of 95.28% (5.18), respectively. Compared to that, 80 subjects were able to successfully finish all spelling tasks using SSmVEP, with a mean information transfer rate of 26.44 bit/min (8.04) and an accuracy of 91.10% (6.01), respectively. Finally, all 86 subjects were able to successfully finish all spelling tasks with the cVEP speller, with a mean information transfer rate of 40.23 bit/min (7.63) and an accuracy of 97.83% (3.37).

Peak Detection with Online Electroencephalography (EEG) Artifact Removal for Brain-Computer Interface (BCI) Purposes.

Brain-computer interfaces (BCIs) measure brain activity and translate it to control computer programs or external devices. However, the activity generated by the BCI makes measurements for objective fatigue evaluation very difficult, and the situation is further complicated due to different movement artefacts. The BCI performance could be increased if an online method existed to measure the fatigue objectively and accurately. While BCI-users are moving, a novel automatic online artefact removal technique is used to filter out these movement artefacts. The effects of this filter on BCI performance and mainly on peak frequency detection during BCI use were investigated in this paper. A successful peak alpha frequency measurement can lead to more accurately determining objective user fatigue. Fifteen subjects performed various imaginary and actual movements in separate tasks, while fourteen electroencephalography (EEG) electrodes were used. Afterwards, a steady-state visual evoked potential (SSVEP)-based BCI speller was used, and the users were instructed to perform various movements. An offline curve fitting method was used for alpha peak detection to assess the effect of the artefact filtering. Peak detection was improved by the filter, by finding 10.91% and 9.68% more alpha peaks during simple EEG recordings and BCI use, respectively. As expected, BCI performance deteriorated from movements, and also from artefact removal. Average information transfer rates (ITRs) were 20.27 bit/min, 16.96 bit/min, and 14.14 bit/min for the (1) movement-free, (2) the moving and unfiltered, and (3) the moving and filtered scenarios, respectively.

Dynamic time window mechanism for time synchronous VEP-based BCIs-Performance evaluation with a dictionary-supported BCI speller employing SSVEP and c-VEP.

Brain-Computer Interfaces (BCIs) based on visual evoked potentials (VEPs) allow high communication speeds and accuracies. The fastest speeds can be achieved if targets are identified in a synchronous way (i.e., after a pre-set time period the system will produce a command output). The duration a target needs to be fixated on until the system classifies an output command affects the overall system performance. Hence, extracting a data window dedicated for the classification is of critical importance for VEP-based BCIs. Secondly, unintentional fixation on a target could easily lead to its selection. For the practical usability of BCI applications it is desirable to distinguish between intentional and unintentional fixations. This can be achieved by using threshold-based target identification methods. The study explores personalized dynamic classification time windows for threshold-based time synchronous VEP BCIs. The proposed techniques were tested employing the SSVEP and the c-VEP paradigm. Spelling performance was evaluated using an 8-target dictionary-supported BCI utilizing an n-gram word prediction model. The performance of twelve healthy participants was assessed with the information transfer rate (ITR) and accuracy. All participants completed sentence spelling tasks, reaching average accuracies of 94% and 96.3% for the c-VEP and the SSVEP paradigm, respectively. Average ITRs around 57 bpm were achieved for both paradigms.

Different Feedback Methods For An SSVEP-Based BCI.

In this paper we examined different ways to inform the user of the classification progress in our online SSVEPbased BCI speller. Different user feedback was given based on the distance from the classification threshold, separately calculated for each stimulus. We focused on the comparison of the accuracies and spelling times associated with each different feedback type. We tested eight different methods, one without feedback for comparison, and the two paradigms each (an increase and a decrease), for three varying parameters, during an online spelling task. The eighth method was a combination of the best performing feedback modalities. A 28 target speller was used for spelling the same word with different feedback methods. The level of comfort was assessed by the seven healthy participants, using a questionnaire. We found substantial decreases in spelling times; they were reduced to 12-77% of the no-feedback condition spelling times, for each of our subjects, with at least one of the parameters. However, this parameter, as expected, was different for each user. According to the personal fastest feedback methods, a combination of them was also used for spelling. These combined feedback methods usually resulted in a slower spelling than the individual best feedback, but still faster than without any feedback. Overall, the average spelling times with the different feedback methods were: no feedback, 95.09 s, increasing size, 62.94 s, decreasing size, 87.73 s, increasing contrast, 77.80 s, decreasing contrast, 124.37 s, increasing duty-cycle, 134.70 s, and decreasing dutycycle, 103.77 s.

Brain-Computer Interface Spellers: A Review.

A Brain-Computer Interface (BCI) provides a novel non-muscular communication method via brain signals. A BCI-speller can be considered as one of the first published BCI applications and has opened the gate for many advances in the field. Although many BCI-spellers have been developed during the last few decades, to our knowledge, no reviews have described the different spellers proposed and studied in this vital field. The presented speller systems are categorized according to major BCI paradigms: P300, steady-state visual evoked potential (SSVEP), and motor imagery (MI). Different BCI paradigms require specific electroencephalogram (EEG) signal features and lead to the development of appropriate Graphical User Interfaces (GUIs). The purpose of this review is to consolidate the most successful BCI-spellers published since 2010, while mentioning some other older systems which were built explicitly for spelling purposes. We aim to assist researchers and concerned individuals in the field by illustrating the highlights of different spellers and presenting them in one review. It is almost impossible to carry out an objective comparison between different spellers, as each has its variables, parameters, and conditions. However, the gathered information and the provided taxonomy about different BCI-spellers can be helpful, as it could identify suitable systems for first-hand users, as well as opportunities of development and learning from previous studies for BCI researchers.

A Novel Hybrid Mental Spelling Application Based on Eye Tracking and SSVEP-Based BCI.

Steady state visual evoked potentials (SSVEPs)-based Brain-Computer interfaces (BCIs), as well as eyetracking devices, provide a pathway for re-establishing communication for people with severe disabilities. We fused these control techniques into a novel eyetracking/SSVEP hybrid system, which utilizes eye tracking for initial rough selection and the SSVEP technology for fine target activation. Based on our previous studies, only four stimuli were used for the SSVEP aspect, granting sufficient control for most BCI users. As Eye tracking data is not used for activation of letters, false positives due to inappropriate dwell times are avoided. This novel approach combines the high speed of eye tracking systems and the high classification accuracies of low target SSVEP-based BCIs, leading to an optimal combination of both methods. We evaluated accuracy and speed of the proposed hybrid system with a 30-target spelling application implementing all three control approaches (pure eye tracking, SSVEP and the hybrid system) with 32 participants. Although the highest information transfer rates (ITRs) were achieved with pure eye tracking, a considerable amount of subjects was not able to gain sufficient control over the stand-alone eye-tracking device or the pure SSVEP system (78.13% and 75% of the participants reached reliable control, respectively). In this respect, the proposed hybrid was most universal (over 90% of users achieved reliable control), and outperformed the pure SSVEP system in terms of speed and user friendliness. The presented hybrid system might offer communication to a wider range of users in comparison to the standard techniques.

Driving a Semiautonomous Mobile Robotic Car Controlled by an SSVEP-Based BCI.

Brain-computer interfaces represent a range of acknowledged technologies that translate brain activity into computer commands. The aim of our research is to develop and evaluate a BCI control application for certain assistive technologies that can be used for remote telepresence or remote driving. The communication channel to the target device is based on the steady-state visual evoked potentials. In order to test the control application, a mobile robotic car (MRC) was introduced and a four-class BCI graphical user interface (with live video feedback and stimulation boxes on the same screen) for piloting the MRC was designed. For the purpose of evaluating a potential real-life scenario for such assistive technology, we present a study where 61 subjects steered the MRC through a predetermined route. All 61 subjects were able to control the MRC and finish the experiment (mean time 207.08 s, SD 50.25) with a mean (SD) accuracy and ITR of 93.03% (5.73) and 14.07 bits/min (4.44), respectively. The results show that our proposed SSVEP-based BCI control application is suitable for mobile robots with a shared-control approach. We also did not observe any negative influence of the simultaneous live video feedback and SSVEP stimulation on the performance of the BCI system.

Exploring the possibilities and limitations of multitarget SSVEP-based BCI applications.

Steady state visual evoked potentials (SSVEPs) are the brain signals induced by gazing at a constantly flickering target. Frame-based frequency approximation methods can be implemented in order to realize a high number of visual stimuli for SSVEP-based Brain-Computer Interfaces (BCIs) on ordinary computer screens. In this paper, we investigate the possibilities and limitations regarding the number of targets in SSVEP-based BCIs. The BCI-performance of seven healthy subjects was evaluated in an online experiment with six differently sized target matrices. Our results confirm previous observations, according to which BCI accuracy and speed are dependent on the number of simultaneously displayed targets. The peak ITR achieved in the experiment was 130.15 bpm. Interestingly, it was achieved with the 15 target matrix. Generally speaking, the BCI performance dropped with an increasing number of simultaneously displayed targets. Surprisingly, however, one subject even gained control over a system with 84 flickering targets, achieving an accuracy of 91.30%, which verifies that stimulation frequencies separated by less than 0.1 Hz can still be distinguished from each other.

Autonomous Parameter Adjustment for SSVEP-Based BCIs with a Novel BCI Wizard.

Brain-Computer Interfaces (BCIs) transfer human brain activities into computer commands and enable a communication channel without requiring movement. Among other BCI approaches, steady-state visual evoked potential (SSVEP)-based BCIs have the potential to become accurate, assistive technologies for persons with severe disabilities. Those systems require customization of different kinds of parameters (e.g., stimulation frequencies). Calibration usually requires selecting predefined parameters by experienced/trained personnel, though in real-life scenarios an interface allowing people with no experience in programming to set up the BCI would be desirable. Another occurring problem regarding BCI performance is BCI illiteracy (also called BCI deficiency). Many articles reported that BCI control could not be achieved by a non-negligible number of users. In order to bypass those problems we developed a SSVEP-BCI wizard, a system that automatically determines user-dependent key-parameters to customize SSVEP-based BCI systems. This wizard was tested and evaluated with 61 healthy subjects. All subjects were asked to spell the phrase "RHINE WAAL UNIVERSITY" with a spelling application after key parameters were determined by the wizard. Results show that all subjects were able to control the spelling application. A mean (SD) accuracy of 97.14 (3.73)% was reached (all subjects reached an accuracy above 85% and 25 subjects even reached 100% accuracy).

Evaluation of different spelling layouts for SSVEP based BCIs.

Brain-computer interface (BCI) systems enable humans to communicate with their environment by directly using brain signals. This way, body movement is not explicitly required for communication making this technology especially useful for people with limited mobility. In this study, the system performance and well-being of 38 subjects are investigated using two different layouts of graphical user interfaces (GUI) presented on a computer screen. A steady state visual evoked potential (SSVEP) based BCI speller is used. Furthermore, three different predefined stimulus frequency sets are tested. Results show that the system works best for 55 % of the test subjects using visual stimuli in the range of 8.57 Hz-15 Hz. The majority of subjects (71 %), prefers the graphical user interface layout called Layout 2. Main advantage of this layout is that each desired letter or symbol can be selected with only two commands in contrast to Layout 1, where usually more than two commands are needed to select a desired object.

SSVEP-based Bremen-BCI interface--boosting information transfer rates.

In recent years, there has been increased interest in using steady-state visual evoked potentials (SSVEP) in brain-computer interface (BCI) systems; the SSVEP approach currently provides the fastest and most reliable communication paradigm for the implementation of a non-invasive BCI. This paper presents recent developments in the signal processing of the SSVEP-based Bremen BCI system, which allowed one of the subjects in an online experiment to reach a peak information transfer rate (ITR) of 124 bit min(-1). It is worth mentioning that this ITR value is higher than all values previously published in the literature for any kind of BCI paradigm.

BCI demographics II: how many (and what kinds of) people can use a high-frequency SSVEP BCI?

Brain-computer interface (BCI) systems use brain activity as an input signal and enable communication without movement. This study is a successor of our previous study (BCI demographics I) and examines correlations among BCI performance, personal preferences, and different subject factors such as age or gender for two sets of steady-state visual evoked potential (SSVEP) stimuli: one in the medium frequency range (13, 14, 15 and 16 Hz) and another in the high-frequency range (34, 36, 38, 40 Hz). High-frequency SSVEPs (above 30 Hz) diminish user fatigue and risk of photosensitive epileptic seizures. Results showed that most people, despite having no prior BCI experience, could use the SSVEP-based Bremen-BCI system in a very noisy field setting at a fair. Results showed that demographic parameters as well as handedness, tiredness, alcohol and caffeine consumption, etc., have no significant effect on the performance of SSVEP-based BCI. Most subjects did not consider the flickering stimuli annoying, only five out of total 86 participants indicated change in fatigue during the experiment. 84 subjects performed with a mean information transfer rate of 17.24 ±6.99 bit/min and an accuracy of 92.26 ±7.82% with the medium frequency set, whereas only 56 subjects performed with a mean information transfer rate of 12.10 ±7.31 bit/min and accuracy of 89.16 ±9.29% with the high-frequency set. These and other demographic analyses may help identify the best BCI for each user.

Brain-computer interface using water-based electrodes.

Current brain-computer interfaces (BCIs) that make use of EEG acquisition techniques require unpleasant electrode gel causing skin abrasion during the standard preparation procedure. Electrodes that require tap water instead of electrolytic electrode gel would make both daily setup and clean up much faster, easier and comfortable. This paper presents the results from ten subjects that controlled an SSVEP-based BCI speller system using two EEG sensor modalities: water-based and gel-based surface electrodes. Subjects performed in copy spelling mode using conventional gel-based electrodes and water-based electrodes with a mean information transfer rate (ITR) of 29.68 ± 14.088 bit min(-1) and of 26.56 ± 9.224 bit min(-1), respectively. A paired t-test failed to reveal significant differences in the information transfer rates and accuracies of using gel- or water-based electrodes for EEG acquisition. This promising result confirms the operational readiness of water-based electrodes for BCI applications.

Toward BCI Wizard - best BCI approach for each user.

Modern brain-computer interface (BCI) systems use different types of neural activity for control. Most BCI systems only allow the customization of very few parameters and focus only on one type of BCI approach. Many articles reported that a certain BCI did not work for some users (so called BCI illiteracy). We are introducing the BCI wizard as a system that automatically identifies key parameters to customize the best BCI paradigm for each user. With a BCI wizard it is possible to develop an interface that relies on the best mental strategy for each user and therefore makes the difference between an ineffective system and a working BCI. This work presents a preliminary study that aims to develop a BCI wizard exploring the two most effective BCI approaches (SSVEP and P300). These types of non-invasive BCIs were tested and evaluated in a group of 14 healthy subjects. During online tests all subjects were asked to spell three words with two spelling applications and at the end of the experiment they chose their preferred approach. Results showed that all subjects could communicate with the P300-based BCI with an accuracy above 69% (5 reached 100% accuracy), 10 out of 14 subjects could effectively use the SSVEP-based BCI (2 reached 100% accuracy). These promising results confirm that BCI wizard will enable BCIs customized to each user with considerably greater flexibility and independence than present systems allow.