Objectively Assessing Sports Concussion Utilizing Visual Evoked Potentials

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.

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

Steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) systems suffer from low SSVEP response intensity and visual fatigue, resulting in lower accuracy when operating the system for continuous commands, such as an electric wheelchair control. This study proposes two SSVEP improvements to create a practical BCI for communication and control in disabled people. The first is flicker pattern modification for increasing SSVEP response through mixing (1) fundamental and first harmonic frequencies, and (2) two fundamental frequencies for an additional number of commands. The second method utilizes a quick response (QR) code for visual stimulus patterns to increase the SSVEP response and reduce visual fatigue. Eight different stimulus patterns from three flickering frequencies (7, 13, and 17 Hz) were presented to twelve participants for the test and score levels of visual fatigue. Two popular SSVEP methods, i.e., power spectral density (PSD) with Welch periodogram and canonical correlation analysis (CCA) with overlapping sliding window, are used to detect SSVEP intensity and response, compared to the checkerboard pattern. The results suggest that the QR code patterns can yield higher accuracy than checkerboard patterns for both PSD and CCA methods. Moreover, a QR code pattern with low frequency can reduce visual fatigue; however, visual fatigue can be easily affected by high flickering frequency. The findings can be used in the future to implement a real-time, SSVEP-based BCI for verifying user and system performance in actual environments.

Previous studies have shown that the brain network topology correlates with the cognitive function. However, few studies have investigated the relationship between functional brain networks that process sensory inputs and outputs. In this study, we focus on steady-state paradigms using a periodic visual stimulus, which are increasingly being used in both brain-computer interface (BCI) and cognitive neuroscience researches. Using the graph theoretical analysis, we investigated the relationship between the topology of functional networks entrained by periodic stimuli and steady state visually evoked potentials (SSVEP) using two frequencies and eleven subjects. First, the entire functional network (Network 0) of each frequency for each subject was constructed according to the coherence between electrode pairs. Next, Network 0 was divided into three sub-networks, in which the connection strengths were either significantly (positively for Network 1, negatively for Network 3) or non-significantly (Network 2) correlated with the SSVEP responses. Our results revealed that the SSVEP responses were positively correlated to the mean functional connectivity, clustering coefficient, and global and local efficiencies, while these responses were negatively correlated with the characteristic path length of Networks 0, 1 and 2. Furthermore, the strengths of these connections that significantly correlated with the SSVEP (both positively and negatively) were mainly found to be long-range connections between the parietal-occipital and frontal regions. These results indicate that larger SSVEP responses correspond with better functional network topology structures. This study may provide new insights for understanding brain mechanisms when using SSVEPs as frequency tags.

Efforts to construct an effective brain-computer interface (BCI) system based on Steady State Visual Evoked Potentials (SSVEP) commonly focus on sophisticated mathematical methods for data analysis. The role of different stimulus features in evoking strong SSVEP is less often considered and the knowledge on the optimal stimulus properties is still fragmentary. The goal of this study was to provide insight into the influence of stimulus characteristics on the magnitude of SSVEP response. Five stimuli parameters were tested: size, distance, colour, shape, and presence of a fixation point in the middle of each flickering field. The stimuli were presented on four squares on LCD screen, with each square highlighted by LEDs flickering with different frequencies. Brighter colours and larger dimensions of flickering fields resulted in a significantly stronger SSVEP response. The distance between stimulation fields and the presence or absence of the fixation point had no significant effect on the response. Contrary to a popular belief, these results suggest that absence of the fixation point does not reduce the magnitude of SSVEP response. However, some parameters of the stimuli such as colour and the size of the flickering field play an important role in evoking SSVEP response, which indicates that stimuli rendering is an important factor in building effective SSVEP based BCI systems.

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.

Brain–computer interfaces (BCIs) based on steady-state visually evoked potentials (SSVEPs) have been well researched due to their easy system configuration, little or no user training and high information transfer rates. To elicit an SSVEP, a repetitive visual stimulus (RVS) is presented to the user. The properties of this RVS (e.g., frequency, luminance) have a significant influence on the BCI performance and user comfort. Several studies in this area in the last one-and-half decades have focused on evaluating different stimulus parameters (i.e., properties). However, there is little research on the synthesis of the existing studies, as the last review on the subject was published in 2010. Consequently, we conducted a scoping review of related studies on the influence of stimulus parameters on SSVEP response and user comfort, analyzed them and summarized the findings considering the physiological and neurological processes associated with BCI performance. In the review, we found that stimulus type, frequency, color contrast, luminance contrast and size/shape of the retinal image are the most important stimulus properties that influence SSVEP response. Regarding stimulus type, frequency and luminance, there is a trade-off between the best SSVEP response quality and visual comfort. Finally, since there is no unified measuring method for visual comfort and a lack of differentiation in the high-frequency band, we proposed a measuring method and a division of the band. In summary, the review highlights which stimulus properties are important to consider when designing SSVEP BCIs. It can be used as a reference point for future research in BCI, as it will help researchers to optimize the design of their SSVEP stimuli.

Aiming at increasing the signal-to-noise ratio of the steady state visual evoked potential(SSVEP),a method based on parameter-tuned stochastic resonance(SR) for enhancing SSVEP is proposed in this paper.This method applied a whitening filter based on an autoregressive(AR) model to remove the 1/f trend in EEG spectrum,and then linearly converted the SSVEP frequency to one smaller than 1 Hz to meet the condition where SR could occur by changing the sample frequency smaller.Finally,SR effect was achieved by tuning the parameters of a bistable system so that the SSVEP can be enhanced.The result shows that a peak at the SSVEP frequency appears in EEG spectrum after processing with the proposed method and the signal-to-noise ratio of the SSVEP increases from 3.07 to 5.76,1.88 times as against before.It is demonstrated that this method is helpful in detecting SSVEP.

Visual evoked potential (VEP)-related EEG signals have attracted widespread attention in the construction of brain-computer interface (BCI) systems. However, long-term use of the BCI system is prone to fatigue. Effectively coping with fatigue is the key to expanding BCI application scenarios. In this brief, we conduct two VEP experiments and propose a novel, concise and practical convolutional neural network (CNN) model to study and solve this problem. In detail, firstly, in terms of the stimulus frequencies in VEP experiments, we intercept the spectrum sequences of the fundamental waves and two harmonics. All these spectrum sequences are then paralleled input into the proposed multi-harmonic linkage CNN (MHLCNN) model, through three branches. Finally, the linkage characteristics of multiple harmonics are combined for VEP signal classification. The experimental results demonstrate that the proposed model shows good scalability, and can respectively improve the average classification accuracy by 23.17% and 15.85% when applied to steady-state VEP (SSVEP) signals in fatigue state and steady-state motion VEP (SSMVEP) signals, compared with the benchmark methods. All these provide a feasible and high-quality solution for reducing the impact of fatigue on the performance of VEP-based BCI systems.

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.

We propose a system for estimating driver attention levels using Visually evoked potentials (VEP), computed from the EEG signals of the visual cortex. We investigate the use of both steady state VEP (SSVEP) and pattern onset VEP (POVEP) for this purpose. The subject fixates on a flickering stimulus, generating a Steady State VEP (SSVEP). Occasionally, a random stimulus is flashed on the screen, and the subsequent POVEP is also analyzed. It is seen that the SSVEP is related to the attention levels of the subjects. The sudden stimulus also generates local maxima/minima values for the POVEP, at the P2 and N2 components. Entropy measures of the frequency response of both the responses could also be used to characterize the occurrence of stimuli. We also propose a system architecture for a driver alertness system, which fuses the above process and a vision based traffic analysis system, to alert the driver well in advance of any decrease in attention.

Over the years, the driver-vehicle interface has been improved, but interacting with in-vehicle features can still increase distraction and affect road safety. This study aims to introduce brain-machine interface (BMI)- based solution to potentially enhance road safety. To achieve this goal, we evaluated visual stimuli properties (SPs) for a steady state visually evoked potentials (SSVEP)-based BMI system. We used a heads-up display (HUD) as the primary screen to present icons for controlling in-vehicle functions such as music, temperature, settings, and navigation. We investigated the effect of various SPs on SSVEP detection performance including the duty cycle and signal-to-noise ratio of visual stimuli, the size, color, and frequency of the icons, and array configuration and location. The experiments were conducted with 10 volunteers and the signals were analyzed using the canonical correlation analysis (CCA), filter bank CCA (FBCCA), and power spectral density analysis (PSDA). Our experimental results suggest that stimuli with a green color, a duty cycle of 50%, presented at a central location, with a size of 36 cm2 elicit a significantly stronger SSVEP response and enhanced SSVEP detection time. We also observed that lower SNR stimuli significantly affect SSVEP detection performance. There was no statistically significant difference observed in SSVEP response between the use of an LCD monitor and a HUD.

Application of a reconstruction technique in detection of dominant SSVEP frequency

Steady State Visually Evoked Potential (SSVEP) is the electro encephalograph (EEG) response observed at the occipital region of the brain to a visual stimulation having a periodic intensity variation. When the retina is excited by a visual stimulus ranging from 3.5 Hz to 75 Hz, the brain generates electrical activity at the same or multiples of frequency of the visual stimulus. SSVEP response can be used to determine the light source the subject is viewing and can form an useful part of a brain machine interface (BMI). Here we present an Android Application that produces visual stimuli, receives EEG signals and performs real time signal processing that enables the classification of the signal spectrum. This is aimed at providing a speller technology for the disabled.

Objective. Steady-state visual evoked potential (SSVEP) is an important control method of the brain–computer interface (BCI) system. The development of an efficient SSVEP feature decoding algorithm is the core issue in SSVEP-BCI. It has been proposed to use user training data to reduce the spontaneous electroencephalogram activity interference on SSVEP response, thereby improving the feature recognition accuracy of the SSVEP signal. Nevertheless, the tedious data collection process increases the mental fatigue of the user and severely affects the applicability of the BCI system. Approach. A cross-subject spatial filter transfer (CSSFT) method that transfer the existing user model with good SSVEP response to the new user test data without collecting any training data from the new user is proposed. Main results. Experimental results demonstrate that the transfer model increases the distinction of the feature discriminant coefficient between the gaze following target and the non-gaze following target and accurately identifies the wrong target in the fundamental algorithm model. The public datasets show that the CSSFT method significantly increases the recognition performance of canonical correlation analysis (CCA) and filter bank CCA. Additionally, when the data used to calculate the transfer model contains one data block only, the CSSFT method retains its effective feature recognition capabilities. Significance. The proposed method requires no tedious data calibration process for new users, provides an effective technical solution for the transfer of the cross-subject model, and has potential application value for promoting the application of the BCI system.