Enhancement of steady-state visual evoked potentials using parameter-tuned stochastic resonance

Decision letter: Neural markers of predictive coding under perceptual uncertainty revealed with Hierarchical Frequency Tagging

In this paper, to develop a hybrid brain-computer interface based on Steady State Visual Evoked Potential(SSVEP) and P300, we validate a SSVEP brain-computer interface (BCI) with the hybrid stimuli of SSVEP and P300, and analyze the effect of P300 visual stimuli on the classification accuracy of the SSVEP BCI. The hybrid stimuli include the SSVEP stimuli, which consist of twochessboardimagespresented on a LCD screen and flashing at particular frequencies, respectively, and the P300 stimuli, which include nine characters and locate between the SSVEP stimuli. We extract the fundamental frequencies and second harmonics of the frequencies of the SSVEP stimulifrom EEG data as features, andapply a Fisher's linear discriminant analysis to build the classifier. The experimental results from three subjects show that the SSVEP potentials can be elicited well under the hybrid stimuli of SSVEP and P300, and the P300 stimuli under the hybrid condition have no significant effect on the SSVEP BCI. This work laysa foundationfor developing a hybrid BCIbased on SSVEP and P300.

Application of a reconstruction technique in detection of dominant SSVEP frequency

A variety of assessment tools are currently available to help clinicians assess Sports Related Concussion (SRC). Currently, the most widely available tools are neither objective nor portable, and are therefore not ideal for assessment at the site and time of a suspected injury. A portable system was developed to deliver a measurement of the steady-state visual-evoked potential (SSVEP). This system involved a smartphone housed in a Google Cardboard frame, which delivered a 15-Hz flicker visual stimulus while an electroencephalography (EEG) headset recorded EEG signals. Sixty-five rugby union players were tested during their regular season and were stratified into healthy, concussed, and recovered groups based on clinical examination. Their SSVEP response was quantified into a signal-to-noise ratio (SNR). The SNRs of players in each study group were summarized. Additionally, the SNRs of individual players who had baseline, post-injury, and post-recovery readings were analyzed. Sixty-five participants completed a baseline evaluation to measure their SSVEP. Twelve of these participants sustained a medically diagnosed concussion and completed SSVEP re-testing within 72 h. Eight concussed players received follow-up SSVEP testing after recovery. Concussed participants had a lower SNR [2.20 (2.04–2.38)] when compared to their baseline [4.54 (3.79–5.10)]. When clinically recovered, participant SNR was not significantly different to their baseline [4.82 (4.13–5.18)]. The baseline SNRs of the players who experienced a concussion during the season were not different to those of players who did not experience a concussion [4.80 (4.07–5.68)]. This is the first study to identify differences in SSVEP responses in male amateur rugby union players with and without concussion. It is also the first SSVEP demonstration for concussion evaluation at point-of-care. SSVEPs are significantly attenuated in the presence of concussion in these male athletes. Individuals returned to their baseline SSVEP following clinical recovery from the concussive injury. The use of SSVEPs has the potential to be a supplemental aid for the assessment and management of concussion.

Objective. A visual stimulator plays a vital part in brain–computer interfaces (BCIs) based on steady-state visual evoked potential (SSVEP). The properties of visual stimulation, such as frequency, color, and waveform, will influence SSVEP-based BCI performance to some extent. Recently, the computer monitor serves as a visual stimulator that is widespread in SSVEP-based BCIs because of its great flexibility in generating visual stimuli. However, stimulation properties based on a computer monitor have received very little attention. For a better comprehension of SSVEPs, this study explored the stimulation effects of waveforms and frequencies, when evoking SSVEPs through a computer monitor. Approach. This study utilized the approximation methods to realize sine- and square-wave temporal modulations at 18 stimulation frequencies ranging from 6 to 40 Hz on a conventional 120 Hz LCD screen. We collected electroencephalogram (EEG) datasets from 12 healthy subjects and compared the signal-to-noise ratios (SNRs), amplitudes, and topographic mapping of SSVEPs evoked by these two temporal modulation flickers (sine- and square-wave). In addition, a BCI experiment with two nine-target BCIs (i.e. low-frequency BCI and high-frequency BCI) was implemented to compare the two stimulation waveforms in terms of BCI performance. Main results. For both sine- and square-wave stimulation conditions, strong SSVEPs over the occipital area were observed for each stimulation frequency. SSVEP amplitudes at the stimulation frequency exhibited a global peak in the low-frequency band. The second harmonic SSVEP frequency-response functions showed the largest amplitude at 6 Hz and fell sharply for higher frequencies. In the BCI experiment, the classification performance of the square-wave stimuli was notably higher than that of the sine-wave stimuli when using shorter data lengths. Significance. These results suggested that the square-wave flicker was more efficient at implementing high-speed BCIs based on SSVEP when using a computer monitor as a visual stimulator.

Spectrum-Enhanced TRCA (SE-TRCA): A novel approach for direction detection in SSVEP-based BCI

In EEG studies, one of the most common ways to detect a weak periodic signal in the steady-state visual evoked potential (SSVEP) is spectral evaluation, a process that detects peaks of power present at notable temporal frequencies. However, the presence of noise decreases the signal-to-noise ratio (SNR), which in turn lowers the probability of successful detection of these spectral peaks. In this paper, using a single EEG channel, we compare the detection performance of four different metrics to analyse the SSVEP: two metrics that use spectral power density, and two other metrics that use phase coherency. We employ these metrics find weak signals with a known temporal frequency hidden in the SSVEP, using both simulation and real data from a stereoscopic apparent depth movement perception task. We demonstrate that out of these metrics, the phase coherency analysis is the most sensitive way to find weak signals in the SSVEP, provided that the phase information of the stimulus eliciting the SSVEP is preserved.

A portable system capable of measuring steady-state visual-evoked potentials (SSVEP) was developed to provide an objective, quantifiable method of electroencephalogram (EEG) testing following a traumatic event. In this study, the portable system was used on 65 healthy rugby players throughout a season to determine whether SSVEP are a reliable electrophysiological biomarker for concussion. Preceding the competition season, all players underwent a baseline SSVEP assessment. During the season, players were re-tested within 72 h of a match for either test-retest reliability or post-injury assessment. In the case of a medically diagnosed concussion, players were reassessed again once deemed recovered by a physician. The SSVEP system consisted of a smartphone housed in a VR-frame delivering a 15 Hz flicker stimulus, while a wireless EEG headset recorded occipital activity. Players were instructed to stare at the screen's fixation point while remaining seated and quiet. Electrodes were arranged according to the 10-20 EEG-positioning nomenclature, with O1-O2 being the recording channels while P1-P2 the references and bias, respectively. All EEG data was processed using a Butterworth bandpass filter, Fourier transformation, and normalization to convert data for frequency analysis. Players SSVEP responses were quantified into a signal-to-noise ratio (SNR), with 15 Hz being the desired signal, and summarized into respective study groups for comparison. Concussed players were seen to have a significantly lower SNR compared to their baseline; however, post-recovery, their SNR was not significantly different from the baseline. Test-retest indicated high device reliability for the portable system. An improved portable SSVEP system was also validated against an established EEG amplifier to ensure the investigative design is capable of obtaining research quality EEG measurements. This is the first study to identify differences in SSVEP responses in amateur athletes following a concussion and indicates the potential for SSVEP as an aid in concussion assessment and management.

Multiple concurrently presented stimuli are thought to compete for neuronal processing resources. Such competitive stimulus interactions can be investigated by "frequency tagging" each stimulus with an individual temporal frequency. In this case, all stimuli will drive distinct steady-state visual evoked potentials (SSVEPs), hence allowing for an assessment of the distribution of processing resources. Here, we investigated whether competitive effects on SSVEP amplitudes are dependent upon the choice of tagging frequency of either the driving stimulus or a close-by competing stimulus. In particular, we were interested whether changes in amplitude are specific to a 10-Hz SSVEP, as it has been suggested that tagging frequencies within the alpha band drive uniquely characterized neural networks. If this was the case, an additional competition might be introduced when two stimuli are tagged with frequencies within the alpha band and thus compete for processing resources in similar networks. Additionally, we tested whether effects on SSVEP amplitude differ when the competing stimulus is tagged with a frequency of 12 Hz that produces a perceptible flicker when compared to an imperceptible 60-Hz flicker. We found a significant decrease in amplitude of 10- and 15-Hz SSVEPs upon presentation of the competing stimulus regardless of its tagging frequency. Our results clearly indicate that an SSVEP with a frequency within the alpha band and a 15-Hz SSVEP show similar sensitivity to effects of competition. Furthermore, the observed effects of competition on SSVEP amplitude occur independently of flicker perceptibility.

Binocular disparate stimulation based on steady-state visual evoked potentials (SSVEP) generates more recognizable features but also introduces complexity hardening the interpretability of SSVEP responses. The individual contribution of each visual pathway when stimulating parts of the visual field remains unclear and is further obfuscated by the large spatial integration of EEG systems. We propose "independent visual field stimulation" (IVFS), utilizing a head-mounted display (HMD) as a novel experimental paradigm to improve the interpretability of SSVEP responses. By stimulating only half of the visual field and "coding" the stimulus further with different phases individual contributions of pathways and eyes can be easily separated and thus the mode of action becomes clearer. In a first proof-of-principles study on 15 subjects we demonstrate that IVFS with a 180° phase difference causes left and right scalp SSVEPs to exhibit a similar phase difference, and the propagation mechanism of SSVEPs conforms to the standing wave mode. Stimulating ipsilateral or contralateral pathways individually does influence temporal information processing but not SSVEP amplitudes. Utilizing the phase of the stimulus in addition to its frequency creates a new dimension and thus helps significantly to overcome frequency limitations in SSVEP research which otherwise dramatically hinder the read-out due to the prominent lowpass nature of the human head.

Objective. Steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) can provide relatively easy, reliable and high speed communication. However, the performance is still not satisfactory, especially in some users who are not able to generate strong enough SSVEP signals. This work aims to strengthen a user’s SSVEP by alpha down-regulating neurofeedback training (NFT) and consequently improve the performance of the user in using SSVEP-based BCIs. Approach. An experiment with two steps was designed and conducted. The first step was to investigate the relationship between the resting alpha activity and the SSVEP-based BCI performance, in order to determine the training parameter for the NFT. Then in the second step, half of the subjects with ‘low’ performance (i.e. BCI classification accuracy <80%) were randomly assigned to a NFT group to perform a real-time NFT, and the rest half to a non-NFT control group for comparison. Main results. The first step revealed a significant negative correlation between the BCI performance and the individual alpha band (IAB) amplitudes in the eyes-open resting condition in a total of 33 subjects. In the second step, it was found that during the IAB down-regulating NFT, on average the subjects were able to successfully decrease their IAB amplitude over training sessions. More importantly, the NFT group showed an average increase of 16.5% in the SSVEP signal SNR (signal-to-noise ratio) and an average increase of 20.3% in the BCI classification accuracy, which was significant compared to the non-NFT control group. Significance. These findings indicate that the alpha down-regulating NFT can be used to improve the SSVEP signal quality and the subjects’ performance in using SSVEP-based BCIs. It could be helpful to the SSVEP related studies and would contribute to more effective SSVEP-based BCI applications.

Perception of a visual target can strongly deteriorate in the presence of flanking elements (crowding). Crowding is the main limiting factor in reading and a major contributor to poor vision in amblyopia. In crowding, elements appear jumbled and thus are hard to recognize. Classic pooling models propose that crowding occurs via pooling of information from low-level to high-level visual areas. Because of pooling, receptive fields become larger and, at final stages, encompass both the target's and flankers' locations. As a result, the brain is unable to perceive the target separately from the flankers. Pooling models predict that adding flankers or increasing the size of the flankers should always increase crowding strength. In a series of previous studies from our laboratory and other groups, this view was shown to be over-simplistic and incomplete. In many situations, crowding can be reduced and good performance recovered by adding flanking elements or by increasing their size. This can occur when flankers ungroup from the target and group with each other. Grouping between elements is thus a key component in crowding. The neural mechanisms of crowding are unknown at the moment. Here, I used EEG to study the time course of crowding and the brain areas involved in the processing of crowding. Using event-related potentials (ERPs), I showed that crowding is a late visual process. In particular, the N1 component of ERPs (around 190 ms) was suppressed in crowding while earlier components did not correlate with crowding. The N1 suppression was caused by reduced neural activity in high-level visual areas such as the lateral occipital cortex. In a second study, I attempted to replicate the results with gabor stimuli. In a third study, I used steady-state visually evoked potentials (ssVEPs) to disentangle target and flanker processing in crowding. In this study, I showed that the target is selectively suppressed when it groups with the flankers. In addition to these three projects on crowding, I worked on time-frequency analysis that I performed on backward masking data in schizophrenia patients.

Steady-state visually evoked potential (SSVEP) studies routinely employ simultaneous presentation of two temporally modulated stimuli, with SSVEP amplitude modulations serving to index top-down cognitive processes. However, the nature of SSVEP amplitude modulations as a function of competing temporal frequency (TF) has not been systematically studied, especially in relation to the normalization framework which has been extensively used to explain visual responses to multiple stimuli. We recorded spikes and local field potential (LFP) from the primary visual cortex (V1) as well as EEG from two awake macaque monkeys while they passively fixated plaid stimuli with components counterphasing at different TFs. We observed asymmetric SSVEP response suppression by competing TFs (greater suppression for lower TFs), which further depended on the relative orientations of plaid components. A tuned normalization model, adapted to SSVEP responses, provided a good account of the suppression. Our results provide new insights into processing of temporally modulated visual stimuli.

This paper presents an electroencephalogram (EEG) based brain computer interface (BCI) design as a control mechanism for the Open Vibe Space shooter game. BCI using Steady State Visual Evoked Potential (SSVEP) is designed. The SSVEP is generated in response to a flickering visual stimulus. The setup uses different flickering frequencies which are displayed on a Liquid Crystal Display (LCD) monitor to elicit SSVEP responses. The EEG signals are recorded over the visual cortex of the brain. These signals are pre-processed to remove undesired information. To extract features, the power information contained in the corresponding frequency bands are calculated. The feature set is fed as an input to the classifier to identify the target class. This study tried to compare the performance of different classification algorithms in terms of accuracy, information transfer rate and the response time. Based on the outcome, support vector machine classifier with radial basis function recorded the best average accuracy of 90%.

We present the classification of Steady State Visual Evoked Potential (SSVEP) using adaptive filters. Those filters are based on the optimization theory and they have the capability of modifying their properties according to selected features of the signals being analyzed. There have been proposed two schemes to classify SSVEP, one using the SSVEP fundamental frequencies and the other one adding their harmonics. The results obtained with adaptive filters are higher than those obtained with classical filters schemes (e.g. Chevyschev II filter). The proposed method is able to extract SSVEP with its harmonics and it allows better classification results than the best standard filter. Besides, this algorithm is fast and can be used for real-time BCI.