Steady-state Visual Evoked Potential Responses Research Articles

Prediction of SSVEP-based BCI performance by the resting-state EEG network

Objective. The prediction of brain–computer interface (BCI) performance is a significant topic in the BCI field. Some researches have demonstrated that resting-state data are promising candidates to achieve the goal. However, so far the relationships between the resting-state networks and the steady-state visual evoked potential (SSVEP)-based BCI have not been investigated. In this paper, we investigate the possible relationships between the SSVEP responses, the classification accuracy of five stimulus frequencies and the closed-eye resting-state network topology. Approach. The resting-state functional connectivity networks of the corresponding five stimulus frequencies were created by coherence, and then three network topology measures—the mean functional connectivity, the clustering coefficient and the characteristic path length of each network—were calculated. In addition, canonical correlation analysis was used to perform frequency recognition with the SSVEP data. Main results. Interestingly, we found that SSVEPs of each frequency were negatively correlated with the mean functional connectivity and clustering coefficient, but positively correlated with characteristic path length. Each of the averaged network topology measures across the frequencies showed the same relationship with the SSVEPs averaged across frequencies between the subjects. Furthermore, our results also demonstrated that the classification accuracy can be predicted by three averaged network measures and their combination can further improve the prediction performance. Significance. These findings indicate that the SSVEP responses and performance are predictable using the information at the resting-state, which may be instructive in both SSVEP-aided cognition studies and SSVEP-based BCI applications.

On the Quantification of SSVEP Frequency Responses in Human EEG in Realistic BCI Conditions

This article concerns one of the most important problems of brain-computer interfaces (BCI) based on Steady State Visual Evoked Potentials (SSVEP), that is the selection of the a-priori most suitable frequencies for stimulation. Previous works related to this problem were done either with measuring systems that have little in common with actual BCI systems (e.g., single flashing LED) or were presented on a small number of subjects, or the tested frequency range did not cover a broad spectrum. Their results indicate a strong SSVEP response around 10 Hz, in the range 13–25 Hz, and at high frequencies in the band of 40–60 Hz. In the case of BCI interfaces, stimulation with frequencies from various ranges are used. The frequencies are often adapted for each user separately. The selection of these frequencies, however, was not yet justified in quantitative group-level study with proper statistical account for inter-subject variability. The aim of this study is to determine the SSVEP response curve, that is, the magnitude of the evoked signal as a function of frequency. The SSVEP response was induced in conditions as close as possible to the actual BCI system, using a wide range of frequencies (5–30 Hz, in step of 1 Hz). The data were obtained for 10 subjects. SSVEP curves for individual subjects and the population curve was determined. Statistical analysis were conducted both on the level of individual subjects and for the group. The main result of the study is the identification of the optimal range of frequencies, which is 12–18 Hz, for the registration of SSVEP phenomena. The applied criterion of optimality was: to find the largest contiguous range of frequencies yielding the strong and constant-level SSVEP response.

Brain–computer interface based on intermodulation frequency

Objective. Most recent steady-state visual evoked potential (SSVEP)-based brain–computer interface (BCI) systems have used a single frequency for each target, so that a large number of targets require a large number of stimulus frequencies and therefore a wider frequency band. However, human beings show good SSVEP responses only in a limited range of frequencies. Furthermore, this issue is especially problematic if the SSVEP-based BCI takes a PC monitor as a stimulator, which is only capable of generating a limited range of frequencies. To mitigate this issue, this study presents an innovative coding method for SSVEP-based BCI by means of intermodulation frequencies. Approach. Simultaneous modulations of stimulus luminance and color at different frequencies were utilized to induce intermodulation frequencies. Luminance flickered at relatively large frequency (10, 12, 15 Hz), while color alternated at low frequency (0.5, 1 Hz). An attractive feature of the proposed method was that it would substantially increase the number of targets at a single flickering frequency by altering color modulated frequencies. Based on this method, the BCI system presented in this study realized eight targets merely using three flickering frequencies. Main results. The online results obtained from 15 subjects (14 healthy and 1 with stroke) revealed that an average classification accuracy of 93.83% and information transfer rate (ITR) of 33.80 bit min−1 were achieved using our proposed SSVEP-based BCI system. Specifically, 5 out of the 15 subjects exhibited an ITR of 40.00 bit min−1 with a classification accuracy of 100%. Significance. These results suggested that intermodulation frequencies could be adopted as steady responses in BCI, for which our system could be used as a practical BCI system.

Classification of visual stimuli with different spatial patterns for single‐frequency, multi‐class SSVEP BCI

A new approach for a multi-class steady-state visual-evoked potential (SSVEP)-based brain–computer interface (BCI) is proposed. It was demonstrated through preliminary experiments that spatial patterns of SSVEP responses recorded using high-density electroencephalography while presenting pattern reversal checkerboard stimuli with different spatial patterns can be classified with fairly high accuracy. The average classification accuracies in two of the three subjects were 91.7% (12-class) and 93.3% (15-class), suggesting that the proposed visual stimulation can potentially be used for multi-class SSVEP-based BCI.

SSVEP response is related to functional brain network topology entrained by the flickering stimulus.

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.

Cortical network properties revealed by SSVEP in anesthetized rats

Steady state visual evoked potentials (SSVEP) are assumed to be regulated by multiple brain areas, yet the underlying mechanisms are not well understood. In this study, we utilized multi-channel intracranial recordings together with network analysis to investigate the underlying relationships between SSVEP and brain networks in anesthetized rat. We examined the relationship between SSVEP amplitude and the network topological properties for different stimulation frequencies, the synergetic dynamic changes of the amplitude and topological properties in each rat, the network properties of the control state, and the individual difference of SSVEP network attributes existing among rats. All these aspects consistently indicate that SSVEP response is closely correlated with network properties, the reorganization of the background network plays a crucial role in SSVEP production, and the background network may provide a physiological marker for evaluating the potential of SSVEP generation.

Adaptive SSVEP-Based BCI System With Frequency and Pulse Duty-Cycle Stimuli Tuning Design

This study aims to design a steady state visual evoked potentials (SSVEP) based brain-computer interface (BCI) system with only three electrodes. It is known that low frequency flickering induces more intensive SSVEP, but might cause users feel uncomfortable and easily tired. Therefore, this paper proposes a novel middle/high frequency flickering stimulus. However, users show different SSVEP responses when gazing at the same stimuli. It is improper to design fixed frequency flickering stimuli for all users. This study firstly proposes a strategy to adjust the stimuli frequency for each user that could cause better SSVEP. Moreover, to further enhance the SSVEP, this study incorporates flickering duty-cycle for stimuli design, which has been discussed less for SSVEP-based BCI systems. The proposed system consists of two modes, flicker frequency/duty-cycle selection mode and application mode. The flicker frequency/duty-cycle selection mode obtains two best frequencies between 24 and 36 Hz with their related optimal duty-cycle. Then the system goes into the application mode to control the devices. A new fact that has been found is that the optimal flicker frequency and duty-cycle do not vary with time. It means once the optical flicker frequency and duty-cycle is determined the first time, flicker frequency/duty-cycle selection mode does not need to operate the next time. Furthermore, the phase coding technology is used to extend the one command/one frequency to multi command/one frequency. Experimental results show the proposed system has good performance with average accuracy 95% and average command transfer interval 4.4925 s per command.

A new dual-frequency stimulation method to increase the number of visual stimuli for multi-class SSVEP-based brain–computer interface (BCI)

In the present study, we introduce a new dual-frequency stimulation method that can produce more visual stimuli with limited number of stimulation frequencies for use in multiclass steady-state visual evoked potential (SSVEP)-based brain–computer interface (BCI) systems. Methods for increasing the number of visual stimuli are necessary, particularly for the implementation of multi-class SSVEP-based BCI, as available stimulation frequencies are generally limited when visual stimuli are presented through a computer monitor. The new stimulation was based on a conventional black–white checkerboard pattern; however, unlike the conventional approach, ten visual stimuli eliciting distinct SSVEP responses at different frequencies could be generated by combining four different stimulation frequencies. Through the offline experiments conducted with eleven participants, we confirmed that all ten visual stimuli could evoke distinct and discriminable single SSVEP peaks, of which the signal-to-noise ratios were high enough to be used for practical SSVEP-based BCI systems. In order to demonstrate the possibility of the practical use of the proposed method, a mental keypad system was implemented and online experiments were conducted with additional ten participants. We achieved an average information transfer rate of 33.26bits/min and an average accuracy of 87.23%, and all ten participants succeeded in calling their mobile phones using our online BCI system.

SSVEP Responses Reveal the Efficiency of Functional Brain Network Entrained by the Flickering Stimulus

In this study, we used the graph theoretical analysis to investigate the association between the functional networks entrained by periodic stimuli and the steady state visual evoked potentials (SSVEP) responses. Using 12.5Hz and 16.6Hz, we found that the SSVEP responses were positively correlated with the clustering coefficient, global and local efficiencies, while negatively correlated with the characteristic path length of the functional networks. These findings indicated that larger SSVEP responses correspond with better functional networks structures. This study may bring new insights into understanding brain mechanisms when using SSVEPs as frequency tags.

The neuronal mechanisms of Steady State Visually Evoked Potential (SSVEP) studied in the fly brains with multi-contact electrodes

Event Abstract Back to Event The neuronal mechanisms of Steady State Visually Evoked Potential (SSVEP) studied in the fly brains with multi-contact electrodes Dror Cohen1*, Angelique Christine Paulk2, Bruno Van Swinderen2 and Naotsugu Tsuchiya1 1 School of Psychology and Psychiatry, Monash University, Australia 2 Queensland Brain Institute, The University of Queensland, Australia Background Flickering stimuli that induce Steady State Visually Evoked Potentials (SSVEP) have been successfully used to study cognitive processes in humans and animals. In particular, covert shifts of attention have been shown to enhance the SSVEP responses for the attended flickers and to reduce those for the ignored flickers. Surprisingly, homologous attentional modulation has been reported in fruit flies using SSVEP. The SSVEP, combined with the genetic manipulations available for flies, opens up possible dissection of high-level cognitive processes such as attention at the neuro-circuit level. Here, we investigated the neuronal mechanisms of SSVEP in flies using electrophysiological techniques. Methods We recorded local neural activity (Local Field Potentials, LFP) in the brains of fruit flies (Drosophila Melanogaster), using a 16-channel micro linear array inserted laterally across the fly brain. During recording, a flicker stimulus was presented either at the left, right or the both sides of the visual field. The flicker frequency was either 13Hz or 16Hz. We characterised the physiological response properties of SSVEP by analysing the LFPs in time and frequency domains. To quantify the information about the stimulus represented in the fly brain, we used a multi-variate decoding technique; specifically, we investigated when, where, and at which frequencies, the information about the flickers was encoded in the LFPs. Results The analysis in time and frequency revealed precise and consistent responses to the flickers in the peripheral visual systems. However, significant responses were also observed in central brain areas. Notably, in addition to the expected SSVEP response at the flicker frequencies we observed strong responses at the harmonics (up to 7th harmonic) and inter-modulatory frequencies (e.g., 29Hz = 13Hz+16Hz). Decoding analyses confirmed highly localized information both in space and frequency and nearly perfect accuracy in classifying the stimuli. Discussion Our initial step towards understanding SSVEP in files is highly promising. Ongoing experiments will test the neuronal basis of attentional modulation using various paradigms. For example, we will make one flickering stimuli more salient by pairing it with reward/punishment. By combining with genetic manipulations of local neural circuits, we will extend our analysis to quantify how information about the attended and ignored flickers is represented in the brains. Keywords: SSVEP, Drosophila melanogaster, Attention, Decoding, local field potential (LFP), Intermodulation, multi-channel recording Conference: ACNS-2013 Australasian Cognitive Neuroscience Society Conference, Clayton, Melbourne, Australia, 28 Nov - 1 Dec, 2013. Presentation Type: Poster Topic: Sensation and Perception Citation: Cohen D, Paulk A, Van Swinderen B and Tsuchiya N (2013). The neuronal mechanisms of Steady State Visually Evoked Potential (SSVEP) studied in the fly brains with multi-contact electrodes. Conference Abstract: ACNS-2013 Australasian Cognitive Neuroscience Society Conference. doi: 10.3389/conf.fnhum.2013.212.00038 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: 15 Oct 2013; Published Online: 25 Nov 2013. * Correspondence: Mr. Dror Cohen, School of Psychology and Psychiatry, Monash University, Melbourne, Australia, dror.cohen@monash.edu 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 Dror Cohen Angelique Christine Paulk Bruno Van Swinderen Naotsugu Tsuchiya Google Dror Cohen Angelique Christine Paulk Bruno Van Swinderen Naotsugu Tsuchiya Google Scholar Dror Cohen Angelique Christine Paulk Bruno Van Swinderen Naotsugu Tsuchiya PubMed Dror Cohen Angelique Christine Paulk Bruno Van Swinderen Naotsugu Tsuchiya 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.

Steady state visually evoked potential (SSVEP) topography changes associated with cocoa flavanol consumption

In a randomized, double-blind placebo controlled trial, 63 middle-aged volunteers aged between 40 and 65years were administered a daily chocolate drink containing 250mg or 500mg cocoa flavanols versus a low cocoa flavanol (placebo) drink over a 30-day period. Participants were tested at baseline as well as at the end of the treatment period on a test of Spatial Working Memory. Steady State Probe Topography (SST) was used to assess neurocognitive changes associated with cocoa flavanol supplementation during the completion of the Spatial Working Memory task. SST is an electrophysiological technique which utilizes a 13Hz diffuse visual flicker in order to generate a steady state visually evoked potential (SSVEP). Changes in the amplitude and phase of the SSVEP response after 30days were compared between treatment groups. Behavioral measures of accuracy and reaction time were not found to be significantly different between treatment groups, while average SSVEP amplitude and phase differences at a number of posterior parietal and centro-frontal sites were found to be significantly different between groups during memory encoding, the working memory hold period and retrieval. In the absence of significant behavioral effects, these differences in brain activation can be interpreted as evidence of increased neural efficiency in spatial working memory function associated with chronic cocoa flavanol consumption.

Enhancing the classification accuracy of steady-state visual evoked potential-based brain–computer interfaces using phase constrained canonical correlation analysis

In this study, a novel method of phase constrained canonical correlation analysis (p-CCA) is presented for classifying steady-state visual evoked potentials (SSVEPs) using multichannel electroencephalography (EEG) signals. p-CCA is employed to improve the performance of the SSVEP-based brain–computer interface (BCI) system using standard CCA. SSVEP response phases are estimated based on the physiologically meaningful apparent latency and are added as a reliable constraint into standard CCA. The results of EEG experiments involving 10 subjects demonstrate that p-CCA consistently outperforms standard CCA in classification accuracy. The improvement is up to 6.8% using 1–4 s data segments. The results indicate that the reliable measurement of phase information is of importance in SSVEP-based BCIs.

A time–frequency convolutional neural network for the offline classification of steady-state visual evoked potential responses

A new convolutional neural network architecture is presented. It includes the fast Fourier transform between two hidden layers to switch the signal analysis from the time domain to the frequency domain inside the network. This technique allows the signal classification without any special pre-processing and uses knowledge from the problem in the network topology. The first step allows the creation of different spatial and time filters. The second step is dedicated to the signal transformation in the frequency domain. The last step is the classification. The system is tested offline on the classification of EEG signals that contain steady-state visual evoked potential (SSVEP) responses. The mean recognition rate of the classification of five different types of SSVEP response is 95.61% on a time segment length of 1s. The proposed strategy outperforms other classical neural network architecures.

Cue Competition Affects Temporal Dynamics of Edge-assignment in Human Visual Cortex

Edge-assignment determines the perception of relative depth across an edge and the shape of the closer side. Many cues determine edge-assignment, but relatively little is known about the neural mechanisms involved in combining these cues. Here, we manipulated extremal edge and attention cues to bias edge-assignment such that these two cues either cooperated or competed. To index their neural representations, we flickered figure and ground regions at different frequencies and measured the corresponding steady-state visual-evoked potentials (SSVEPs). Figural regions had stronger SSVEP responses than ground regions, independent of whether they were attended or unattended. In addition, competition and cooperation between the two edge-assignment cues significantly affected the temporal dynamics of edge-assignment processes. The figural SSVEP response peaked earlier when the cues causing it cooperated than when they competed, but sustained edge-assignment effects were equivalent for cooperating and competing cues, consistent with a winner-take-all outcome. These results provide physiological evidence that figure-ground organization involves competitive processes that can affect the latency of figural assignment.

Emotional faces boost up steady-state visual responsesforbrain–computer interface

Steady-state visual evoked potentials (SSVEPs) can be used successfully for brain-computer interfaces (BCI) with multiple commands and high information transfer rates. In this study, we investigated a novel affective SSVEP paradigm using flickering video clips of emotional human faces, and evaluated their performance in an 8-command BCI controlling a robotic arm in near real-time. Single-trial affective SSVEP responses, estimated using a new phase-locking value variability and a wavelet energy variability measures, were significantly enhanced compared with blurred-face flicker and standard checkerboards. For multicommand SSVEP-based BCI, affective face-flicker boosted up the information transfer rates from 50 to 64 bits/min, while reducing user fatigue and enhancing visual attention and reliability. In the 5-12 Hz flicker frequency range, the strongest affective SSVEP responses were obtained at 10 Hz. These findings suggest new directions for SSVEP-based neural applications, including affective BCI and enhanced steady-state clinical probes.

Square or Sine: Finding a Waveform with High Success Rate of Eliciting SSVEP

Steady state visual evoked potential (SSVEP) is the brain's natural electrical potential response for visual stimuli at specific frequencies. Using a visual stimulus flashing at some given frequency will entrain the SSVEP at the same frequency, thereby allowing determination of the subject's visual focus. The faster an SSVEP is identified, the higher information transmission rate the system achieves. Thus, an effective stimulus, defined as one with high success rate of eliciting SSVEP and high signal-noise ratio, is desired. Also, researchers observed that harmonic frequencies often appear in the SSVEP at a reduced magnitude. Are the harmonics in the SSVEP elicited by the fundamental stimulating frequency or by the artifacts of the stimuli? In this paper, we compare the SSVEP responses of three periodic stimuli: square wave (with different duty cycles), triangle wave, and sine wave to find an effective stimulus. We also demonstrate the connection between the strength of the harmonics in SSVEP and the type of stimulus.

Feature-selective attention: Evidence for a decline in old age

Although attention in older adults is an active research area, feature-selective aspects have not yet been explicitly studied. Here we report the results of an exploratory study involving directed changes in feature-selective attention. The stimuli used were two random dot kinematograms (RDKs) of different colours, superimposed and centrally presented. A colour cue with random onset after the beginning of each trial instructed young and older subjects to attend to one of the RDKs and detect short intervals of coherent motion while ignoring analogous motion events in the non-cued RDK. Behavioural data show that older adults could detect motion, but discriminated target from distracter motion less reliably than young adults. The method of frequency tagging allowed us to separate the EEG responses to the attended and ignored stimuli and directly compare steady-state visual evoked potential (SSVEP) amplitudes elicited by each stimulus before and after cue onset. We found that younger adults show a clear attentional enhancement of SSVEP amplitude in the post-cue interval, while older adults’ SSVEP responses to attended and ignored stimuli do not differ. Thus, in situations where attentional selection cannot be spatially resolved, older adults show a deficit in selection that is not shared by young adults.

An SSVEP-Actuated Brain Computer Interface Using Phase-Tagged Flickering Sequences: A Cursor System

This study presents a new steady-state visual evoked potential (SSVEP)-based brain computer interface (BCI). SSVEPs, induced by phase-tagged flashes in eight light emitting diodes (LEDs), were used to control four cursor movements (up, right, down, and left) and four button functions (on, off, right-, and left-clicks) on a screen menu. EEG signals were measured by one EEG electrode placed at Oz position, referring to the international EEG 10-20 system. Since SSVEPs are time-locked and phase-locked to the onsets of SSVEP flashes, EEG signals were bandpass-filtered and segmented into epochs, and then averaged across a number of epochs to sharpen the recorded SSVEPs. Phase lags between the measured SSVEPs and a reference SSVEP were measured, and targets were recognized based on these phase lags. The current design used eight LEDs to flicker at 31.25 Hz with 45 degrees phase margin between any two adjacent SSVEP flickers. The SSVEP responses were filtered within 29.25-33.25 Hz and then averaged over 60 epochs. Owing to the utilization of high-frequency flickers, the induced SSVEPs were away from low-frequency noises, 60 Hz electricity noise, and eye movement artifacts. As a consequence, we achieved a simple architecture that did not require eye movement monitoring or other artifact detection and removal. The high-frequency design also achieved a flicker fusion effect for better visualization. Seven subjects were recruited in this study to sequentially input a command sequence, consisting of a sequence of eight cursor functions, repeated three times. The accuracy and information transfer rate (mean +/- SD) over the seven subjects were 93.14 +/- 5.73% and 28.29 +/- 12.19 bits/min, respectively. The proposed system can provide a reliable channel for severely disabled patients to communicate with external environments.

Steady-State Visual Evoked Potential Response - Impact of the Time Segment Length

When a visual stimulus with a constant frequency is presented to a person, it is possible to observe a continuous brain response in the visual cortical area. This response is called a steady-state visual evoked potential (SSVEP). The detection of this brain response can be analyzed as a command for tailoring a Brain-Computer interface (BCI). Its goal is to provide a new output channel for the brain that requires voluntary control. The efficiency of a BCI depends on the commands accuracy and their latency: the time between the order from the subject and the moment when the command is detected. Given one particular SSVEP response classification method, the BCI control is variable among the users. This variance is translated into different performance between subjects. We propose a robust classification method of SSVEP responses, which does not require any training. A performance comparison on 8 different time segment lengths over 10 subjects is proposed. The average time segment length for obtaining a SSVEP response recognition of 95% is determined to be 2.8s.

Optimization of SSVEP brain responses with application to eight-command Brain–Computer Interface

This study pursues the optimization of the brain responses to small reversing patterns in a Steady-State Visual Evoked Potentials (SSVEP) paradigm, which could be used to maximize the efficiency of applications such as Brain–Computer Interfaces (BCI). We investigated the SSVEP frequency response for 32 frequencies (5–84Hz), and the time dynamics of the brain response at 8, 14 and 28Hz, to aid the definition of the optimal neurophysiological parameters and to outline the onset-delay and other limitations of SSVEP stimuli in applications such as our previously described four-command BCI system. Our results showed that the 5.6–15.3Hz pattern reversal stimulation evoked the strongest responses, peaking at 12Hz, and exhibiting weaker local maxima at 28 and 42Hz. After stimulation onset, the long-term SSVEP response was highly non-stationary and the dynamics, including the first peak, was frequency-dependent. The evaluation of the performance of a frequency-optimized eight-command BCI system with dynamic neurofeedback showed a mean success rate of 98%, and a time delay of 3.4s. Robust BCI performance was achieved by all subjects even when using numerous small patterns clustered very close to each other and moving rapidly in 2D space. These results emphasize the need for SSVEP applications to optimize not only the analysis algorithms but also the stimuli in order to maximize the brain responses they rely on.