Visual Speller Research Articles

Introducing block-Toeplitz covariance matrices to remaster linear discriminant analysis for event-related potential brain–computer interfaces

Objective. Covariance matrices of noisy multichannel electroencephalogram (EEG) time series data provide essential information for the decoding of brain signals using machine learning methods. However, small datasets and high dimensionality make it hard to estimate these matrices. In brain–computer interfaces (BCI) based on event-related potentials (ERP) and a linear discriminant analysis (LDA) classifier, the state of the art covariance estimation uses shrinkage regularization. As this is a general covariance regularization approach, we aim at improving LDA further by better exploiting the domain-specific characteristics of the EEG to regularize the covariance estimates. Approach. We propose to enforce a block-Toeplitz structure for the covariance matrix of the LDA, which implements an assumption of signal stationarity in short time windows for each channel. Main results. An offline re-analysis of data collected from 213 subjects under 13 different event-related potential BCI protocols showed a significantly increased binary classification performance of this ‘ToeplitzLDA’ compared to shrinkage regularized LDA (up to 6 AUC points, p < 0.001) and Riemannian classification approaches (up to 2 AUC points, p < 0.001). In an unsupervised visual speller application, this improvement would translate to a relative reduction of spelling errors by 81% on average for 25 subjects. Additionally, aside from lower memory and reduced time complexity for LDA training, ToeplitzLDA proves to be robust against drastic increases of the number of temporal features. Significance. The proposed covariance estimation allows BCI researchers to improve classification rates and reduce calibration times of BCI protocols using event-related potentials and thus support the usability of corresponding applications. Its lower computational and memory needs could make it a valuable algorithm especially for mobile BCIs.

The Effect of SOA on An Asynchronous ERP and VEP-Based BCI

Our previous study established an asynchronous BCI system by using the oddball paradigm to simultaneously induce event-related potentials (ERPs) and visual evoked potentials (VEPs) (E-V BCIs). We found that stimulus onset asynchrony (SOA) is an important factor for performance since it significantly affects the ERP and VEP. Increasing the SOA increases the ERP, which improves the accuracy of detecting target stimuli. However, a larger SOA leads to a lower VEP frequency, which causes the VEP to have poor accuracy when discriminating between the brain states. How to balance the two potentials and accuracies is a problem. This study established eight SOAs from 100 ms to 375 ms that were composed of different interstimulus intervals and the same stimulus duration of 80 ms. We used a probability-based Fisher linear discriminant analysis (P-FLDA) classifier to calculate the classification accuracies of the ERP-based visual speller, VEP-based brain state discrimination, and E-V BCI. The results show that as the SOA increases, the amplitudes of N200 and P300 increase, and the accuracy also shows an increasing trend. However, the frequency of the VEP and the accuracies of state discrimination show downward trends. The change in accuracies of the E-V BCI system combining these two parts is nonlinear, and the SOA optimal value is 125 ms. The SOA of 125 ms yields the best accuracies of 95.83% and practical bit rate of 57.17 bits/min in the E-V BCI system, which provides a guideline for selecting the SOA to improve the performance.

The Effect That Auditory Distractions Have on a Visual P300 Speller While Utilizing Low-Cost Off-the-Shelf Equipment

This paper investigates the effect that selected auditory distractions have on the signal of a visual P300 Speller in terms of accuracy, amplitude, latency, user preference, signal morphology, and overall signal quality. In addition, it ensues the development of a hierarchical taxonomy aimed at categorizing distractions in the P300b domain and the effect thereof. This work is part of a larger electroencephalography based project and is based on the P300 speller brain–computer interface (oddball) paradigm and the xDAWN algorithm, with eight to ten healthy subjects, using a non-invasive brain–computer interface based on low-fidelity electroencephalographic (EEG) equipment. Our results suggest that the accuracy was best for the lab condition (LC) at 100%, followed by music at 90% (M90) at 98%, trailed by music at 30% (M30) and music at 60% (M60) equally at 96%, and shadowed by ambient noise (AN) at 92.5%, passive talking (PT) at 90%, and finally by active listening (AL) at 87.5%. The subjects’ preference prodigiously shows that the preferred condition was LC as originally expected, followed by M90, M60, AN, M30, AL, and PT. Statistical analysis between all independent variables shows that we accept our null hypothesis for both the amplitude and latency. This work includes data and comparisons from our previous papers. These additional results should give some insight into the practicability of the aforementioned P300 speller methodology and equipment to be used for real-world applications.

New approach for designing cVEP BCI stimuli based on superposition of edge responses

The purpose of this study is to develop a new methodology for designing stimulus sequences for Brain Computer Interfaces that utilize code modulated Visually Evoked Potentials (cVEP BCIs), based on experimental results regarding the behavior and the properties of the actual EEG responses of the visual system to binary-coded visual stimuli, such that training time is reduced and possible number of targets is increased. EEG from 8 occipital sites is recorded with 2000 sps, in response to visual stimuli presented on a computer monitor with 60 Hz refresh rate. EEG responses of the visual system to black-to-white and white-to-black transitions of a target area on the monitor are recorded for 500 ms, for 160 trials, and signal-averaged to obtain the onset (positive edge) and offset (negative edge) responses, respectively. It is found that both edge responses are delayed by 50 ms and wane completely within 350 ms. These edge responses are then used to generate (predict) the EEG responses to arbitrary binary stimulus sequences using the superposition principle. It is found that the generated and the measured EEG responses to certain (16) simple short sequences (16.67–350 ms) are highly correlated. These ‘optimal short patterns’ are then randomly combined to design the long (120 bit, 2 sec) ‘Superposition Optimized Pulse (SOP)’ sequences, and their EEG response templates are obtained by superposition of the edge responses. A SOP sequence-based Visual Speller BCI application yielded higher accuracy (95.9%) and Information Transfer Rate (ITR) (57.2 bpm), compared to when superposition principle is applied to conventional m-sequences and randomly generated sequences. Training for the BCI application involves only the acquisition of the edge responses and takes less than 4 min. This is the first study in which the EEG templates for cVEP BCI sequences are obtained by the superposition of edge responses.

An idle state-detecting method based on transient visual evoked potentials for an asynchronous ERP-based BCI

BackgroundAn asynchronous brain-computer interface (BCI) allows subject to freely switch between the working state and the idle state, improving the subject’s comfort. However, using only the event-related potential (ERP) to detect these two states is difficult because of the small amplitude of the ERP. MethodOur previous study finds that an odd-ball paradigm could evoke transient visual evoked potentials (TSVEPs) simultaneously with ERPs. This study adopts the TSVEP and the ERP to detect the idle state in the design of an asynchronous TSVEP-ERP-based BCI (T-E BCI). The T-E BCI extracts time and frequency features from brain signals and uses a novel probability-based fisher linear discriminant analysis (P-FLDA) to combine the classification results of the ERP and the TSVEP. ResultTen subjects perform visual speller and video watching experiments, and their brain signals are measured under the working and idle states. The main results show that the T-E BCI achieves a higher accuracy than the ERP-based BCI when judging the subject’s intentions and the two states. The P-FLDA performs better than the FLDA in combining the classification results. ConclusionsThe study demonstrates that adding the TSVEP can substantially reduce the number of wrongly detected trials. The T-E BCI provides a new way of designing an asynchronous BCI without adding any additional visual stimuli, which makes the BCI more practical.

Effect of the concreteness of robot motion visual stimulus on an event-related potential-based brain-computer interface

Event-related potential (ERP)-based brain-computer interface (BCI) has been widely used in robot control. Increasing the amplitude of the ERPs is key for improving the performance of ERP-based BCI. However, using images of robot motion as visual stimuli has not been studied widely. The aim of this study is to explore the concreteness of robot motion images on ERPs. Fifteen subjects used five kinds of visual spellers employing different images as visual stimuli: squares, arrows, a single kind of robot motion, multiple kinds of robot motions, and multiple kinds of robot motions with arrows. The three robot motion stimuli induced larger N200 and P300 potentials than non-robot motion stimuli. The topography shows that robot motion stimuli also evoke stronger negativities in the anterior and occipital areas. Concrete images provide more information to the subject about the robot motion, which might help the brain extract the meaning of the image more automatically. We use a support vector machine to detect the subject’s intentions. There is substantial improvement in the classification performance when using robot motion images as visual stimuli, which implies that concrete visual stimuli improve the performance of the ERP-based BCI.

Unsupervised Learning for Brain-Computer Interfaces Based on Event-Related Potentials: Review and Online Comparison [Research Frontier

One of the fundamental challenges in brain-computer interfaces (BCIs) is to tune a brain signal decoder to reliably detect a user's intention. While information about the decoder can partially be transferred between subjects or sessions, optimal decoding performance can only be reached with novel data from the current session. Thus, it is preferable to learn from unlabeled data gained from the actual usage of the BCI application instead of conducting a calibration recording prior to BCI usage. We review such unsupervised machine learning methods for BCIs based on event-related potentials of the electroencephalogram. We present results of an online study with twelve healthy participants controlling a visual speller. Online performance is reported for three completely unsupervised learning methods: (1) learning from label proportions, (2) an expectation-maximization approach and (3) MIX, which combines the strengths of the two other methods. After a short ramp-up, we observed that the MIX method not only defeats its two unsupervised competitors but even performs on par with a state-of-the-art regularized linear discriminant analysis trained on the same number of data points and with full label access. With this online study, we deliver the best possible proof in BCI that an unsupervised decoding method can in practice render a supervised method unnecessary. This is possible despite skipping the calibration, without losing much performance and with the prospect of continuous improvement over a session. Thus, our findings pave the way for a transition from supervised to unsupervised learning methods in BCIs based on eventrelated potentials.

Improving zero-training brain-computer interfaces by mixing model estimators

Objective. Brain-computer interfaces (BCI) based on event-related potentials (ERP) incorporate a decoder to classify recorded brain signals and subsequently select a control signal that drives a computer application. Standard supervised BCI decoders require a tedious calibration procedure prior to every session. Several unsupervised classification methods have been proposed that tune the decoder during actual use and as such omit this calibration. Each of these methods has its own strengths and weaknesses. Our aim is to improve overall accuracy of ERP-based BCIs without calibration. Approach. We consider two approaches for unsupervised classification of ERP signals. Learning from label proportions (LLP) was recently shown to be guaranteed to converge to a supervised decoder when enough data is available. In contrast, the formerly proposed expectation maximization (EM) based decoding for ERP–BCI does not have this guarantee. However, while this decoder has high variance due to random initialization of its parameters, it obtains a higher accuracy faster than LLP when the initialization is good. We introduce a method to optimally combine these two unsupervised decoding methods, letting one method’s strengths compensate for the weaknesses of the other and vice versa. The new method is compared to the aforementioned methods in a resimulation of an experiment with a visual speller. Main results. Analysis of the experimental results shows that the new method exceeds the performance of the previous unsupervised classification approaches in terms of ERP classification accuracy and symbol selection accuracy during the spelling experiment. Furthermore, the method shows less dependency on random initialization of model parameters and is consequently more reliable. Significance. Improving the accuracy and subsequent reliability of calibrationless BCIs makes these systems more appealing for frequent use.

A P300-Based Brain–Computer Interface for Chinese Character Input

ABSTRACTThe majority of previously developed assistive communication brain–computer interface systems have primarily focused on languages that are written in alphabetic scripts. However, languages that are written in logographic scripts, such as those in Chinese hanzi (or sinograms), pose a challenge for the implementation of visual spelling systems because it is impossible to simultaneously display thousands of items in a stimulus matrix of a reasonable size. In this study, a P300 visual spelling system that uses a novel method to input Chinese sinograms developed with a Hanyu Pinyin-based method is presented. This method transcribes a Chinese Pinyin into initial consonant and vowel components according to its Mandarin pronunciation. In this paradigm, each sinogram is input by selecting the initial consonant and then the vowel components and subsequently selecting the sinogram itself. Ten healthy subjects participated in the study and achieved an average offline accuracy of 92.6% with a mean information transfer rate of 39.2 bits/min and an average online input speed of one sinogram per 43.9 s. The preliminary results presented here indicated that the online input of Chinese text using a Pinyin-based visual speller is feasible.

Broad-Band Visually Evoked Potentials: Re(con)volution in Brain-Computer Interfacing.

Brain-Computer Interfaces (BCIs) allow users to control devices and communicate by using brain activity only. BCIs based on broad-band visual stimulation can outperform BCIs using other stimulation paradigms. Visual stimulation with pseudo-random bit-sequences evokes specific Broad-Band Visually Evoked Potentials (BBVEPs) that can be reliably used in BCI for high-speed communication in speller applications. In this study, we report a novel paradigm for a BBVEP-based BCI that utilizes a generative framework to predict responses to broad-band stimulation sequences. In this study we designed a BBVEP-based BCI using modulated Gold codes to mark cells in a visual speller BCI. We defined a linear generative model that decomposes full responses into overlapping single-flash responses. These single-flash responses are used to predict responses to novel stimulation sequences, which in turn serve as templates for classification. The linear generative model explains on average 50% and up to 66% of the variance of responses to both seen and unseen sequences. In an online experiment, 12 participants tested a 6 × 6 matrix speller BCI. On average, an online accuracy of 86% was reached with trial lengths of 3.21 seconds. This corresponds to an Information Transfer Rate of 48 bits per minute (approximately 9 symbols per minute). This study indicates the potential to model and predict responses to broad-band stimulation. These predicted responses are proven to be well-suited as templates for a BBVEP-based BCI, thereby enabling communication and control by brain activity only.

Toward a minimally invasive brain–computer interface using a single subdural channel: A visual speller study

Electrocorticography (ECoG) has attracted increasing interest for implementing advanced brain-computer interfaces (BCIs) in the past decade. However, real-life application of ECoG BCI demands mitigation of its invasive nature by minimizing both the size of the involved brain regions and the number of implanted electrodes. In this study, we employed a recently proposed BCI paradigm that utilizes the attentional modulation of visual motion response. With ECoG data collected from five epilepsy patients, power increase of the high gamma (60-140Hz) frequency range was found to be associated with the overtly attended moving visual stimuli in the parietal-temporal-occipital junction and the occipital cortex. Event-related potentials (ERPs) were elicited as well but with broader cortical distribution. We achieved significantly higher BCI classification accuracy by employing both high gamma and ERP responses from a single ECoG electrode than by using ERP responses only (84.22±5.54% vs. 75.48±4.18%, p<0.005, paired t-test, 3-trial averaging, binary results of attended vs. unattended). More importantly, the high gamma responses were located within brain regions specialized in visual motion processing as mapped by fMRI, suggesting the spatial location for electrode implantation can be determined prior to surgery using non-invasive imaging. Our findings demonstrate the feasibility of implementing a minimally invasive ECoG BCI.

Introducing the tactile speller: an ERP-based brain–computer interface for communication

In this study, a tactile speller was developed and compared with existing visual speller paradigms in terms of classification performance and elicited event-related potentials (ERPs). The fingertips of healthy participants were stimulated with short mechanical taps while electroencephalographic activity was measured. The letters of the alphabet were allocated to different fingers and subjects could select one of the fingers by silently counting the number of taps on that finger. The offline and online performance of the tactile speller was compared to the overt and covert attention visual matrix speller and the covert attention Hex-o-Spell speller. For the tactile speller, binary target versus non-target classification accuracy was 67% on average. Classification and decoding accuracies of the tactile speller were lower than the overt matrix speller, but higher than the covert matrix speller, and similar to Hex-o-Spell. The average maximum information transfer rate of the tactile speller was 7.8 bits min−1 (1.51 char min−1), with the best subject reaching a bit-rate of 27 bits min−1 (5.22 char min−1). An increased amplitude of the P300 ERP component was found in response to attended stimuli versus unattended stimuli in all speller types. In addition, the tactile and overt matrix spellers also used the N2 component for discriminating between targets and non-targets. Overall, this study shows that it is possible to use a tactile speller for communication. The tactile speller provides a useful alternative to the visual speller, especially for people whose eye gaze is impaired.

Exploring motion VEPs for gaze-independent communication

Motion visually evoked potentials (mVEPs) have recently been explored as input features for brain–computer interfaces, in particular for the implementation of visual spellers. Due to low contrast and luminance requirements, motion-based intensification is less discomforting to the user than conventional approaches. So far, mVEP spellers were operated in the overt attention mode, wherein eye movements were allowed. However, the dependence on eye movements limits clinical applicability. Hence, the purpose of this study was to evaluate the suitability of mVEPs for gaze-independent communication. Sixteen healthy volunteers participated in an online study. We used a conventional speller layout wherein the possible selections are presented at different spatial locations both in the overt attention mode (fixation of the target) and the covert attention mode (central fixation). Additionally, we tested an alternative speller layout wherein all stimuli are sequentially presented at the same spatial location (foveal stimulation), i.e. eye movements are not required for selection. As can be expected, classification performance breaks down when switching from the overt to the covert operation. Despite reduced performance in the covert setting, conventional mVEP spellers are still potentially useful for users with severely impaired eye movements. In particular, they may offer advantages—such as less visual fatigue—over spellers using flashing stimuli. Importantly, the novel mVEP speller presented here recovers good performance in a gaze-independent setting by resorting to the foveal stimulation.

A Chinese Text Input Brain–Computer Interface Based on the P300 Speller

A visual speller is a brain–computer interface that empowers users with limited motor functionality to input text into a computer by measuring their electroencephalographic responses to visual stimuli. Most prior research on visual spellers has focused on input of alphabetic text. Adapting a speller for other types of segmental or syllabic script is straightforward because such scripts comprise sufficiently few characters that they may all be displayed to the user simultaneously. Logographic scripts, such as Chinese hanzi, however, impose a challenge: How should the thousands of Chinese characters be displayed to the user? Here, we present a visual speller, based on Farwell and Donchin's P300 Speller, for Chinese character input. The speller uses a novel shape-based method called the First–Last, or FLAST, method to encode more than 7,000 Chinese characters. Characters are input by selecting two components, from a set of 56 distinct components, that match the shape of the target character, followed by selection of the character itself. At the input speed of one character per 107 s, 24 able-bodied participants achieved mean online accuracy of 82.8% per component selection and 63.5% per character input. At the faster input speed of one character per 77 s, mean online accuracy was 59.4% per component selection and 33.3% per character input.

Dense codes at high speeds: varying stimulus properties to improve visual speller performance

This paper investigates the effect of varying different stimulus properties on performance of the visual speller. Each of the different stimulus properties has been tested in previous literature and has a known effect on visual speller performance. This paper investigates whether a combination of these types of stimuli can lead to a greater improvement. It describes an experiment aimed at answering the following questions. (i) Does visual speller performance suffer from high stimulus rates? (ii) Does an increase in stimulus rate lead to a lower training time for an online visual speller? (iii) What aspect of the difference in the event related potential to a flash or a flip stimulus causes the increase in accuracy. (iv) Can an error-correcting (dense) stimulus code overcome the reduction in performance associated with decreasing target-to-target intervals? We found that higher stimulus rates can improve the visual speller performance and can lead to less time required to train the system. We also found that a proper stimulus code can overcome the stronger response to rows and columns, but cannot greatly improve speller performance.

Gaze-independent brain–computer interfaces based on covert attention and feature attention

There is evidence that conventional visual brain–computer interfaces (BCIs) based on event-related potentials cannot be operated efficiently when eye movements are not allowed. To overcome this limitation, the aim of this study was to develop a visual speller that does not require eye movements. Three different variants of a two-stage visual speller based on covert spatial attention and non-spatial feature attention (i.e. attention to colour and form) were tested in an online experiment with 13 healthy participants. All participants achieved highly accurate BCI control. They could select one out of thirty symbols (chance level 3.3%) with mean accuracies of 88%–97% for the different spellers. The best results were obtained for a speller that was operated using non-spatial feature attention only. These results show that, using feature attention, it is possible to realize high-accuracy, fast-paced visual spellers that have a large vocabulary and are independent of eye gaze.

A Graphical Model Framework for Decoding in the Visual ERP-Based BCI Speller

We present a graphical model framework for decoding in the visual ERP-based speller system. The proposed framework allows researchers to build generative models from which the decoding rules are obtained in a straightforward manner. We suggest two models for generating brain signals conditioned on the stimulus events. Both models incorporate letter frequency information but assume different dependencies between brain signals and stimulus events. For both models, we derive decoding rules and perform a discriminative training. We show on real visual speller data how decoding performance improves by incorporating letter frequency information and using a more realistic graphical model for the dependencies between the brain signals and the stimulus events. Furthermore, we discuss how the standard approach to decoding can be seen as a special case of the graphical model framework. The letter also gives more insight into the discriminative approach for decoding in the visual speller system.

Towards gaze-independent visual brain-computer interfaces

Event Abstract Back to Event Towards gaze-independent visual brain-computer interfaces Matthias S. Treder1*, Nico M. Schmidt1 and Benjamin Blankertz1, 2, 3 1 Berlin Institute of Technology, Machine Learning Laboratory, Germany 2 Fraunhofer FIRST, IDA, Germany 3 Berlin Institute of Technology, Bernstein Focus: Neurotechnology, Germany BACKGROUND The designated goal of a brain-computer interface (BCI) is to establish a direct link from the brainto a machine, thereby circumventing motor behavior. A popular approach to BCIs is the use of the eventrelated potential (ERP). There is now ample evidence that conventional brain-computer interfaces based on event-related potentials can be operated e ciently only when targets ffi are fixated with the eye [1,2]. Critically, this limits the scope of visual spellers to the scope of eyetrackers, that is, it is basically a means to recover eye gaze. Furthermore, users with impaired control of eye movements cannot use conventional visual BCIs in an efficient way. To overcome these limitations, the aim of the present study was to develop visual spellers that are highly accurate, fast-paced, and independent of eye gaze. Three different variants of a visual speller based on the ERP Hex-o-Spell [1] were tested in an online experiment with 13 healthy participants. Participants could use both covert spatial attention and feature attention (i.e., attention to color and form) to attend to the targets. METHOD AND RESULTS For each kind of speller, participants traversed a calibration phase (on basis of a classifier was trained), a copy-spelling phase (wherein they spelled a pre-defined sentence), and a free-spelling phase (wherein they spelled a self-invented phrase). For classification, we used linear discriminant analysis with shrinkage of the covariance matrix [3]. For all participants, high-accuracy BCI control was achieved, see Figure 1. Selecting one out of thirty symbols (chance level 3.3%) for the three spellers yielded mean accuracies of 90.43%, 87.95%, and 97.03%, respectively. CONCLUSION The present results are a proof of concept: It is possible to realize fast-paced, high-accuracy visual spellers that have a large vocabulary and that are independent of eye gaze. The most significant implication is that the scope of visual spellers is broadened from simply recovering eye gaze to recovering attentional focus independent of eye gaze. Figure 1: Symbol selection accuracies for the three types of spellers. Bars depict mean accuracy for all participants and colored dots represent accuracies obtained for individual participants. Figure 1 References [1] Treder MS, Blankertz B: (C)overt attention and visual speller design in an ERP-based braincomputer interface. Behav Brain Funct, 2010, 6:28, [http://www.behavioralandbrainfunctions.com/content/6/1/28]. [2] Brunner P, Joshi S, Briskin S, Wolpaw JR, Bischof H, Schalk G: Does the ”P300” Speller Depend on Eye Gaze? J Neural Eng 2010, in press. [3] Blankertz B, Lemm S, Treder MS, Haufe S, Müller KR, Single-trial analysis and classification of ERP components – a tutorial, NeuroImage 2010, in press. Keywords: computational neuroscience Conference: Bernstein Conference on Computational Neuroscience, Berlin, Germany, 27 Sep - 1 Oct, 2010. Presentation Type: Presentation Topic: Bernstein Conference on Computational Neuroscience Citation: Treder MS, Schmidt NM and Blankertz B (2010). Towards gaze-independent visual brain-computer interfaces. Front. Comput. Neurosci. Conference Abstract: Bernstein Conference on Computational Neuroscience. doi: 10.3389/conf.fncom.2010.51.00117 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 09 Sep 2010; Published Online: 23 Sep 2010. * Correspondence: Dr. Matthias S Treder, Berlin Institute of Technology, Machine Learning Laboratory, Berlin, Germany, matthias.treder@tu-berlin.de Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Matthias S Treder Nico M Schmidt Benjamin Blankertz Google Matthias S Treder Nico M Schmidt Benjamin Blankertz Google Scholar Matthias S Treder Nico M Schmidt Benjamin Blankertz PubMed Matthias S Treder Nico M Schmidt Benjamin Blankertz Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.

(C)overt attention and visual speller design in an ERP-based brain-computer interface.

BackgroundIn a visual oddball paradigm, attention to an event usually modulates the event-related potential (ERP). An ERP-based brain-computer interface (BCI) exploits this neural mechanism for communication. Hitherto, it was unclear to what extent the accuracy of such a BCI requires eye movements (overt attention) or whether it is also feasible for targets in the visual periphery (covert attention). Also unclear was how the visual design of the BCI can be improved to meet peculiarities of peripheral vision such as low spatial acuity and crowding.MethodHealthy participants (N = 13) performed a copy-spelling task wherein they had to count target intensifications. EEG and eye movements were recorded concurrently. First, (c)overt attention was investigated by way of a target fixation condition and a central fixation condition. In the latter, participants had to fixate a dot in the center of the screen and allocate their attention to a target in the visual periphery. Second, the effect of visual speller layout was investigated by comparing the symbol Matrix to an ERP-based Hex-o-Spell, a two-levels speller consisting of six discs arranged on an invisible hexagon.ResultsWe assessed counting errors, ERP amplitudes, and offline classification performance. There is an advantage (i.e., less errors, larger ERP amplitude modulation, better classification) of overt attention over covert attention, and there is also an advantage of the Hex-o-Spell over the Matrix. Using overt attention, P1, N1, P2, N2, and P3 components are enhanced by attention. Using covert attention, only N2 and P3 are enhanced for both spellers, and N1 and P2 are modulated when using the Hex-o-Spell but not when using the Matrix. Consequently, classifiers rely mainly on early evoked potentials in overt attention and on later cognitive components in covert attention.ConclusionsBoth overt and covert attention can be used to drive an ERP-based BCI, but performance is markedly lower for covert attention. The Hex-o-Spell outperforms the Matrix, especially when eye movements are not permitted, illustrating that performance can be increased if one accounts for peculiarities of peripheral vision.