Felix Gembler

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).

Age-related differences in SSVEP-based BCI performance

BrainComputer 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.

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.

A Comparison of SSVEP-Based BCI-Performance Between Different Age Groups

In this paper we compare the performance of a SSVEP-based BCI spelling application of two different equally sized age groups (five subjects each, ranging from 19 to 27 years and 66 to 70 years). Our results confirm that elderly people may have a slightly deteriorated information transfer rate (ITR). The mean (SD) ITR of the young age group was 27.18 (8.82) bit/min while the elderly people achieved an ITR of 14.42 (6.29) bit/min. The results show that the subject age must be taken into account during the development of a SSVEP-based application.

Evaluation of Suitable Frequency Differences in SSVEP-Based BCIs

Frame-based frequency approximation methods are a popular approach to realize visual stimuli that can be used to elicit steady-state visual evoked potentials (SSVEPs) at various frequencies on computer screens and allows the development of multi-target Brain-Computer Interface (BCI)-Systems. In this paper we investigate appropriate selection of visual stimuli for multi-target BCIs using a frequency approximation method. Twelve sets of frequencies from different bands and with different resolutions have been compared among each other during an on-line BCI-task with six healthy subjects. Our results confirm that equidistant frequency sets are not optimal, as the results from the sets with lower frequency ranges (\(<\)12 Hz) surpass those of the mid-range sets, even if a higher resolution is used. Interestingly, the study shows, that SSVEPs elicted by stimuli from lower bands with a very high frequency resolution of 0.05 Hz could still be classified with adequate accuracy (around 90%). The results confirm that careful stimuli choice has high impact on SSVEP based BCI performance.

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.

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.

SSVEP-Based BCI in a Smart Home Scenario

Steady state visual evoked potentials (SSVEPs)-based Brain-Computer Interfaces (BCIs) can provide hand-free human interaction with the environment. In the presented study, visual stimuli were displayed on Epson Moverio BT-200 augmented reality glasses, which can be easily used in smart homes. QR codes were used to identify the devices to be controlled with the BCI. In order to simulate a real life scenario, participants were instructed to go out of the lab to get a coffee. During this task light switches, elevator and a coffee machine were controlled by focusing on SSVEP stimuli displayed on the smart glasses. An average accuracy of 85.70% was achieved, which suggests that augmented reality may be used together with SSVEP to control external devices.

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.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.

Suitable Number of Visual Stimuli for SSVEP-Based BCI Spelling Applications

Steady state visual evoked potentials (SSVEPs)-based Brain-Computer interfaces (BCIs) provide a pathway for re-establishing communication to people with severe disabilities. In the presented study, we compared accuracy and speed of three SSVEP-based BCI spelling applications in order to investigate the influence of the number of visual stimuli on the BCI performance. Three systems with four, six and 28 stimulating frequencies were tested. Ten subjects (one female) participated in this study. The highest ITR achieved in the experiment was 51.77 bpm. It is interesting, that it was achieved with the system based on six flickering targets. Our results confirm that the number of stimuli has high impact on classification accuracy and BCI literacy of SSVEP-based BCIs.