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Salazar, Sophia, Femi Oyewole, Ted Obi, Rebecca Baron, Dylan Mahony, Anna Kropelnicki, Adrian Cohen, David Putrino, and Adam Fry. "Steady-state visual evoked potentials are unchanged following physical and cognitive exertion paradigms." Journal of Concussion 5 (January 2021): 205970022110553. http://dx.doi.org/10.1177/20597002211055346.
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Background There is a need for objective biomarkers of sports-related concussion that are unaffected by physical and cognitive exertion. Electroencephalography-based biomarkers such as steady-state visually evoked potentials (SSVEPs) have been proposed as one such biomarker. The aim of this study was to investigate the effects of cognitive and physical exertion on SSVEP signal-to-noise ratio (SNR). Methods This study involved two experiments. The first experiment was performed in a controlled laboratory environment and involved a treadmill run designed to induce physical fatigue and a Stroop task designed to induce mental fatigue, completed in a randomized order on two separate visits. SSVEPs were evoked using a 15-Hz strobe using a Nurochek headset before and after each task. Changes in the 15-Hz SSVEP SNR and self-reported fatigue (visual analog scales) were assessed. In the second experiment, SSVEP SNR was measured before and after real-world boxing matches. Paired t-tests compared pre- and post-task SSVEP SNR and fatigue scores. Results Eighteen participants were recruited for experiment 1. Following the treadmill run, participants reported higher physical fatigue, mental fatigue, and overall fatigue ( p ≤ 0.005; d ≥ 0.90). Following the Stroop task, participants reported higher mental fatigue and overall fatigue ( p < 0.001; d ≥ 1.16), but not physical fatigue. SSVEP SNR scores were unchanged following either the Stroop task ( p = 0.059) or the treadmill task ( p = 0.590). Seven participants were recruited for experiment 2. SSVEP SNR scores were unchanged following the boxing matches ( p = 0.967). Conclusions The results of both experiments demonstrate that SSVEP SNR scores were not different following the treadmill run, Stroop task or amateur boxing match. These findings provide preliminary evidence that SSVEP fidelity may not be significantly affected by physical and cognitive exertion paradigms.
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Ikeda, Akira, and Yoshikazu Washizawa. "Steady-State Visual Evoked Potential Classification Using Complex Valued Convolutional Neural Networks." Sensors 21, no. 16 (August 6, 2021): 5309. http://dx.doi.org/10.3390/s21165309.
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The steady-state visual evoked potential (SSVEP), which is a kind of event-related potential in electroencephalograms (EEGs), has been applied to brain–computer interfaces (BCIs). SSVEP-based BCIs currently perform the best in terms of information transfer rate (ITR) among various BCI implementation methods. Canonical component analysis (CCA) or spectrum estimation, such as the Fourier transform, and their extensions have been used to extract features of SSVEPs. However, these signal extraction methods have a limitation in the available stimulation frequency; thus, the number of commands is limited. In this paper, we propose a complex valued convolutional neural network (CVCNN) to overcome the limitation of SSVEP-based BCIs. The experimental results demonstrate that the proposed method overcomes the limitation of the stimulation frequency, and it outperforms conventional SSVEP feature extraction methods.
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Chen, Jing, Matteo Valsecchi, and Karl R. Gegenfurtner. "Saccadic suppression measured by steady-state visual evoked potentials." Journal of Neurophysiology 122, no. 1 (July 1, 2019): 251–58. http://dx.doi.org/10.1152/jn.00712.2018.
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Visual sensitivity is severely impaired during the execution of saccadic eye movements. This phenomenon has been extensively characterized in human psychophysics and nonhuman primate single-neuron studies, but a physiological characterization in humans is less established. Here, we used a method based on steady-state visually evoked potential (SSVEP), an oscillatory brain response to periodic visual stimulation, to examine how saccades affect visual sensitivity. Observers made horizontal saccades back and forth, while horizontal black-and-white gratings flickered at 5–30 Hz in the background. We analyzed EEG epochs with a length of 0.3 s either centered at saccade onset (saccade epochs) or centered at fixations half a second before the saccade (fixation epochs). Compared with fixation epochs, saccade epochs showed a broadband power increase, which most likely resulted from saccade-related EEG activity. The execution of saccades, however, led to an average reduction of 57% in the SSVEP amplitude at the stimulation frequency. This result provides additional evidence for an active saccadic suppression in the early visual cortex in humans. Compared with previous functional MRI and EEG studies, an advantage of this approach lies in its capability to trace the temporal dynamics of neural activity throughout the time course of a saccade. In contrast to previous electrophysiological studies in nonhuman primates, we did not find any evidence for postsaccadic enhancement, even though simulation results show that our method would have been able to detect it. We conclude that SSVEP is a useful technique to investigate the neural correlates of visual perception during saccadic eye movements in humans. NEW & NOTEWORTHY We make fast ballistic saccadic eye movements a few times every second. At the time of saccades, visual sensitivity is severely impaired. The present study uses steady-state visually evoked potentials to reveal a neural correlate of the fine temporal dynamics of these modulations at the time of saccades in humans. We observed a strong reduction (57%) of visually driven neural activity associated with saccades but did not find any evidence for postsaccadic enhancement.
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Olze, Katharina, Christof Jan Wehrmann, Luyang Mu, and Meinhard Schilling. "Obstacles in using a computer screen for steady-state visually evoked potential stimulation." Biomedical Engineering / Biomedizinische Technik 63, no. 4 (July 26, 2018): 377–82. http://dx.doi.org/10.1515/bmt-2016-0243.
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Abstract In brain computer interface (BCI) applications, the use of steady-state visually evoked potentials (SSVEPs) is common. Therefore, a visual stimulation with a constant repetition frequency is necessary. However, using a computer monitor, the set of frequencies that can be used is restricted by the refresh rate of the screen. Frequencies that are not an integer divisor of the refresh rate cannot be displayed correctly. Furthermore, the programming language the stimulation software is written in and the operating system influence the actually generated and presented frequencies. The aim of this paper is to identify the main challenges in generating SSVEP stimulation using a computer screen with and without using DirectX in Windows-based PC systems and to provide solutions for these issues.
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Zhang, Shangen, and Xiaogang Chen. "Effect of background luminance of visual stimulus on elicited steady-state visual evoked potentials." Brain Science Advances 8, no. 1 (March 2022): 50–56. http://dx.doi.org/10.26599/bsa.2022.9050006.
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Steady-state visual evoked potential (SSVEP)-based brain– computer interfaces (BCIs) have been widely studied. Considerable progress has been made in the aspects of stimulus coding, electroencephalogram processing, and recognition algorithms to enhance system performance. The properties of SSVEP have been demonstrated to be highly sensitive to stimulus luminance. However, thus far, there have been very few reports on the impact of background luminance on the system performance of SSVEP-based BCIs. This study investigated the impact of stimulus background luminance on SSVEPs. Specifically, this study compared two types of background luminance, i.e., (1) black luminance [red, green, blue (rgb): (0, 0, 0)] and (2) gray luminance [rgb: (128, 128, 128)], and determined their effect on the classification performance of SSVEPs at the stimulus frequencies of 9, 11, 13, and 15 Hz. The offline results from nine healthy subjects showed that compared with the gray background luminance, the black background luminance induced larger SSVEP amplitude and larger signal-to-noise ratio, resulting in a better classification accuracy. These results suggest that the background luminance of visual stimulus has a considerable effect on the SSVEP and therefore has a potential to improve the BCI performance.
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Krishnappa, Manjula, and Madaveeranahally Boregowda Anandaraju. "Adaptive filters based efficient EEG classification for steady state visually evoked potential based BCI system." International Journal of Reconfigurable and Embedded Systems (IJRES) 12, no. 2 (July 1, 2023): 215. http://dx.doi.org/10.11591/ijres.v12.i2.pp215-221.
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Brain-computer interfaces (BCIs) system is a link to generate a communication between disable people and physical devices. Thus, steady state visually evoked potential (SSVEP) is analysed to improve performance efficiency of BCIs system using multi-class classification process. Thus, an adaptive filtering-based component analysis (AFCA) method is adopted to examine SSVEP from multiple-channel electroencephalography (EEG) signals for BCIs system efficiency enhancement. Further, flickering at varied frequencies is used in a visual stimulation process to examine user intentions and brain responses. A detailed solution for optimization problem and efficient feature extraction is also presented. Here, a large SSVEP dataset is utilized which contains 256 channel EEG data. Experimental results are evaluated in terms of classification accuracy and information transfer rate to measure efficiency of proposed SSVEP extraction method against varied traditional SSVEP-based BCIs. The average information transfer rate (ITR) results are 308.23 bits per minute and classification accuracy is 93.48% using proposed AFCA method. Thus, proposed AFCA method shows decent performance in comparison with state-of-art-SSVEP extraction methods.
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Gao, Shouwei, Kang Zhou, Jun Zhang, Yi Cheng, and Shujun Mao. "Effects of Background Music on Mental Fatigue in Steady-State Visually Evoked Potential-Based BCIs." Healthcare 11, no. 7 (April 2, 2023): 1014. http://dx.doi.org/10.3390/healthcare11071014.
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As a widely used brain–computer interface (BCI) paradigm, steady-state visually evoked potential (SSVEP)-based BCIs have the advantages of high information transfer rates, high tolerance for artifacts, and robust performance across diverse users. However, the incidence of mental fatigue from prolonged, repetitive stimulation is a critical issue for SSVEP-based BCIs. Music is often used as a convenient, non-invasive means of relieving mental fatigue. This study investigates the compensatory effect of music on mental fatigue through the introduction of different modes of background music in long-duration, SSVEP-BCI tasks. Changes in electroencephalography power index, SSVEP amplitude, and signal-to-noise ratio were used to assess participants’ mental fatigue. The study’s results show that the introduction of exciting background music to the SSVEP-BCI task was effective in relieving participants’ mental fatigue. In addition, for continuous SSVEP-BCI tasks, a combination of musical modes that used soothing background music during the rest interval phase proved more effective in reducing users’ mental fatigue. This suggests that background music can provide a practical solution for long-duration SSVEP-based BCI implementation.
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Adam, Kirsten C. S., Lillian Chang, Nicole Rangan, and John T. Serences. "Steady-State Visually Evoked Potentials and Feature-based Attention: Preregistered Null Results and a Focused Review of Methodological Considerations." Journal of Cognitive Neuroscience 33, no. 4 (April 2021): 695–724. http://dx.doi.org/10.1162/jocn_a_01665.
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Feature-based attention is the ability to selectively attend to a particular feature (e.g., attend to red but not green items while looking for the ketchup bottle in your refrigerator), and steady-state visually evoked potentials (SSVEPs) measured from the human EEG signal have been used to track the neural deployment of feature-based attention. Although many published studies suggest that we can use trial-by-trial cues to enhance relevant feature information (i.e., greater SSVEP response to the cued color), there is ongoing debate about whether participants may likewise use trial-by-trial cues to voluntarily ignore a particular feature. Here, we report the results of a preregistered study in which participants either were cued to attend or to ignore a color. Counter to prior work, we found no attention-related modulation of the SSVEP response in either cue condition. However, positive control analyses revealed that participants paid some degree of attention to the cued color (i.e., we observed a greater P300 component to targets in the attended vs. the unattended color). In light of these unexpected null results, we conducted a focused review of methodological considerations for studies of feature-based attention using SSVEPs. In the review, we quantify potentially important stimulus parameters that have been used in the past (e.g., stimulation frequency, trial counts) and we discuss the potential importance of these and other task factors (e.g., feature-based priming) for SSVEP studies.
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Liu, Siyu, Deyu Zhang, Ziyu Liu, Mengzhen Liu, Zhiyuan Ming, Tiantian Liu, Dingjie Suo, Shintaro Funahashi, and Tianyi Yan. "Review of brain–computer interface based on steady‐state visual evoked potential." Brain Science Advances 8, no. 4 (November 30, 2022): 258–75. http://dx.doi.org/10.26599/bsa.2022.9050022.
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The brain–computer interface (BCI) technology has received lots of attention in the field of scientific research because it can help disabled people improve their quality of life. Steady‐state visual evoked potential (SSVEP) is the most researched BCI experimental paradigm, which offers the advantages of high signal‐to‐noise ratio and short training‐time requirement by users. In a complete BCI system, the two most critical components are the experimental paradigm and decoding algorithm. However, a systematic combination of the SSVEP experimental paradigm and decoding algorithms is missing in existing studies. In the present study, the transient visual evoked potential, SSVEP, and various improved SSVEP paradigms are compared and analyzed, and the problems and development bottlenecks in the experimental paradigm are finally pointed out. Subsequently, the canonical correlation analysis and various improved decoding algorithms are introduced, and the opportunities and challenges of the SSVEP decoding algorithm are discussed.
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Lin, Bor-Shyh, Bor-Shing Lin, Tzu-Hsiang Yen, Chien-Chin Hsu, and Yao-Chin Wang. "Design of Wearable Headset with Steady State Visually Evoked Potential-Based Brain Computer Interface." Micromachines 10, no. 10 (October 10, 2019): 681. http://dx.doi.org/10.3390/mi10100681.
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Brain–computer interface (BCI) is a system that allows people to communicate directly with external machines via recognizing brain activities without manual operation. However, for most current BCI systems, conventional electroencephalography (EEG) machines and computers are usually required to acquire EEG signal and translate them into control commands, respectively. The sizes of the above machines are usually large, and this increases the limitation for daily applications. Moreover, conventional EEG electrodes also require conductive gels to improve the EEG signal quality. This causes discomfort and inconvenience of use, while the conductive gels may also encounter the problem of drying out during prolonged measurements. In order to improve the above issues, a wearable headset with steady-state visually evoked potential (SSVEP)-based BCI is proposed in this study. Active dry electrodes were designed and implemented to acquire a good EEG signal quality without conductive gels from the hairy site. The SSVEP BCI algorithm was also implemented into the designed field-programmable gate array (FPGA)-based BCI module to translate SSVEP signals into control commands in real time. Moreover, a commercial tablet was used as the visual stimulus device to provide graphic control icons. The whole system was designed as a wearable device to improve convenience of use in daily life, and it could acquire and translate EEG signal directly in the front-end headset. Finally, the performance of the proposed system was validated, and the results showed that it had excellent performance (information transfer rate = 36.08 bits/min).
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Owen, Caroline M., John Patterson, and Richard B. Silberstein. "Olfactory Modulation of Steady- State Visual Evoked Potential Topography in Comparison with Differences in Odor Sensitivity." Journal of Psychophysiology 16, no. 2 (January 2002): 71–81. http://dx.doi.org/10.1027//0269-8803.16.2.71.
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Summary Research was undertaken to determine whether olfactory stimulation can alter steady-state visual evoked potential (SSVEP) topography. Odor-air and air-only stimuli were used to determine whether the SSVEP would be altered when odor was present. Comparisons were also made of the topographic activation associated with air and odor stimulation, with the view toward determining whether the revealed topographic activity would differentiate levels of olfactory sensitivity by clearly identifying supra- and subthreshold odor responses. Using a continuous respiration olfactometer (CRO) to precisely deliver an odor or air stimulus synchronously with the natural respiration, air or odor (n-butanol) was randomly delivered into the inspiratory airstream during the simultaneous recording of SSVEPs and subjective behavioral responses. Subjects were placed in groups based on subjective odor detection response: “yes” and “no” detection groups. In comparison to air, SSVEP topography revealed cortical changes in response to odor stimulation for both response groups, with topographic changes evident for those unable to perceive the odor, showing the presence of a subconscious physiological odor detection response. Differences in regional SSVEP topography were shown for those who reported smelling the odor compared with those who remained unaware of the odor. These changes revealed olfactory modulation of SSVEP topography related to odor awareness and sensitivity and therefore odor concentration relative to thresholds.
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Cohen, Adrian, Daryl Fong, David Putrino, Philip Boughton, Joseph Herrera, Neil G. Simon, Paul Raftos, and Dylan Mahony. "Steady-State Visual-Evoked Potentials as a Biomarker for Concussion: A Pilot Study." Neurology 95, no. 20 Supplement 1 (November 16, 2020): S6.2—S7. http://dx.doi.org/10.1212/01.wnl.0000719920.91849.25.
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ObjectiveThis study aims to utilize a portable system capable of measuring steady-state visual evoked potentials (SSVEP) to investigate their use as an objective electrophysiologic biomarker for concussion.BackgroundThe most pressing issues in relation to sports related concussion (SRC) involves accurate and timely diagnosis, for a safe return to play criteria. Despite the vast range of tools available to help clinicians assess concussion, most are subjective, non-portable, and therefore non-ideal for unbiased application at the site and time of a suspected injury.Design/MethodsThis system applied a smartphone housed in a VR-frame delivering a 15-Hz flickering stimulus while a wireless electroencephalography (EEG) headset recorded EEG signals. Sixty-five male amateur rugby athletes (20.9 ± 2.3 years-old) were tested throughout a season and were stratified into healthy, concussed, and recovered groups based on clinical examinations pre- and post-competitive games. Players SSVEP responses was quantified into a signal-to-noise ratio (SNR) and summarized into respective study-groups for comparison of medians with 25th–75th interquartile range.ResultsAll sixty-five participants completed a baseline evaluation preseason. Twelve participants sustained a diagnosed concussion during the season and were retested within 72 h of injury. Eight concussed players received additional SSVEP testing following a 2-week recovery period. Concussed participants had a significantly lower SNR [2.20 (2.04–2.38)] when compared to their baseline [4.54 (3.79–5.10)]. When clinically recovered, participant SNR [4.82 (4.13–5.18)] was not significantly different to their baseline. Baseline SNR of concussed and non-concussed participants [4.80 (4.07–5.68)] did not significantly differ.ConclusionsThis is the first study to show that SSVEPs are significantly attenuated in the presence of concussion in male athletes. Concussed individuals' ability to generate SSVEP appear to recover following clinical recovery. The observations of this study indicate SSVEP have the potential to be a supplemental aid for the assessment and management of concussion at point-of-care.
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Siribunyaphat, Nannaphat, and Yunyong Punsawad. "Brain–Computer Interface Based on Steady-State Visual Evoked Potential Using Quick-Response Code Pattern for Wheelchair Control." Sensors 23, no. 4 (February 12, 2023): 2069. http://dx.doi.org/10.3390/s23042069.
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Brain–computer interfaces (BCIs) are widely utilized in control applications for people with severe physical disabilities. Several researchers have aimed to develop practical brain-controlled wheelchairs. An existing electroencephalogram (EEG)-based BCI based on steady-state visually evoked potential (SSVEP) was developed for device control. This study utilized a quick-response (QR) code visual stimulus pattern for a robust existing system. Four commands were generated using the proposed visual stimulation pattern with four flickering frequencies. Moreover, we employed a relative power spectrum density (PSD) method for the SSVEP feature extraction and compared it with an absolute PSD method. We designed experiments to verify the efficiency of the proposed system. The results revealed that the proposed SSVEP method and algorithm yielded an average classification accuracy of approximately 92% in real-time processing. For the wheelchair simulated via independent-based control, the proposed BCI control required approximately five-fold more time than the keyboard control for real-time control. The proposed SSVEP method using a QR code pattern can be used for BCI-based wheelchair control. However, it suffers from visual fatigue owing to long-time continuous control. We will verify and enhance the proposed system for wheelchair control in people with severe physical disabilities.
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NAKANISHI, MASAKI, YIJUN WANG, YU-TE WANG, YASUE MITSUKURA, and TZYY-PING JUNG. "A HIGH-SPEED BRAIN SPELLER USING STEADY-STATE VISUAL EVOKED POTENTIALS." International Journal of Neural Systems 24, no. 06 (July 31, 2014): 1450019. http://dx.doi.org/10.1142/s0129065714500191.
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Implementing a complex spelling program using a steady-state visual evoked potential (SSVEP)-based brain–computer interface (BCI) remains a challenge due to difficulties in stimulus presentation and target identification. This study aims to explore the feasibility of mixed frequency and phase coding in building a high-speed SSVEP speller with a computer monitor. A frequency and phase approximation approach was developed to eliminate the limitation of the number of targets caused by the monitor refresh rate, resulting in a speller comprising 32 flickers specified by eight frequencies (8–15 Hz with a 1 Hz interval) and four phases (0°, 90°, 180°, and 270°). A multi-channel approach incorporating Canonical Correlation Analysis (CCA) and SSVEP training data was proposed for target identification. In a simulated online experiment, at a spelling rate of 40 characters per minute, the system obtained an averaged information transfer rate (ITR) of 166.91 bits/min across 13 subjects with a maximum individual ITR of 192.26 bits/min, the highest ITR ever reported in electroencephalogram (EEG)-based BCIs. The results of this study demonstrate great potential of a high-speed SSVEP-based BCI in real-life applications.
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Silberstein, Richard B., Paul L. Nunez, Andrew Pipingas, Philip Harris, and Frank Danieli. "Steady state visually evoked potential (SSVEP) topography in a graded working memory task." International Journal of Psychophysiology 42, no. 2 (October 2001): 219–32. http://dx.doi.org/10.1016/s0167-8760(01)00167-2.
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Camfield, D. A., A. Scholey, A. Pipingas, R. Silberstein, M. Kras, K. Nolidin, K. Wesnes, M. Pase, and C. Stough. "Steady state visually evoked potential (SSVEP) topography changes associated with cocoa flavanol consumption." Physiology & Behavior 105, no. 4 (February 2012): 948–57. http://dx.doi.org/10.1016/j.physbeh.2011.11.013.
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Liu, Bingchuan, Xinyi Yan, Xiaogang Chen, Yijun Wang, and Xiaorong Gao. "tACS facilitates flickering driving by boosting steady-state visual evoked potentials." Journal of Neural Engineering 18, no. 6 (December 1, 2021): 066042. http://dx.doi.org/10.1088/1741-2552/ac3ef3.
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Abstract Objective. There has become of increasing interest in transcranial alternating current stimulation (tACS) since its inception nearly a decade ago. tACS in modulating brain state is an active area of research and has been demonstrated effective in various neuropsychological and clinical domains. In the visual domain, much effort has been dedicated to brain rhythms and rhythmic stimulation, i.e. tACS. However, less is known about the interplay between the rhythmic stimulation and visual stimulation. Approach. Here, we used steady-state visual evoked potential (SSVEP), induced by flickering driving as a widely used technique for frequency-tagging, to investigate the aftereffect of tACS in healthy human subjects. Seven blocks of 64-channel electroencephalogram were recorded before and after the administration of 20min 10Hz tACS, while subjects performed several blocks of SSVEP tasks. We characterized the physiological properties of tACS aftereffect by comparing and validating the temporal, spatial, spatiotemporal and signal-to-noise ratio (SNR) patterns between and within blocks in real tACS and sham tACS. Main results. Our result revealed that tACS boosted the 10Hz SSVEP significantly. Besides, the aftereffect on SSVEP was mitigated with time and lasted up to 5 min. Significance. Our results demonstrate the feasibility of facilitating the flickering driving by external rhythmic stimulation and open a new possibility to alter the brain state in a direction by noninvasive transcranial brain stimulation.
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Kim, Y. J., R. Shapley, and N. Rubin. "Coherent global percepts increase steady-state visual evoked potential (SSVEP)." Journal of Vision 10, no. 7 (August 3, 2010): 334. http://dx.doi.org/10.1167/10.7.334.
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Wang, Lu, Dan Han, Binbin Qian, Zhenhao Zhang, Zhijun Zhang, and Zhifang Liu. "The Validity of Steady-State Visual Evoked Potentials as Attention Tags and Input Signals: A Critical Perspective of Frequency Allocation and Number of Stimuli." Brain Sciences 10, no. 9 (September 7, 2020): 616. http://dx.doi.org/10.3390/brainsci10090616.
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Steady-state visual evoked potential (SSVEP) is a periodic response to a repetitive visual stimulus at a specific frequency. Currently, SSVEP is widely treated as an attention tag in cognitive activities and is used as an input signal for brain–computer interfaces (BCIs). However, whether SSVEP can be used as a reliable indicator has been a controversial issue. We focused on the independence of SSVEP from frequency allocation and number of stimuli. First, a cue–target paradigm was adopted to examine the interaction between SSVEPs evoked by two stimuli with different frequency allocations under different attention conditions. Second, we explored whether signal strength and the performance of SSVEP-based BCIs were affected by the number of stimuli. The results revealed that no significant interaction of SSVEP responses appeared between attended and unattended stimuli under various frequency allocations, regardless of their appearance in the fundamental or second-order harmonic. The amplitude of SSVEP suffered no significant gain or loss under different numbers of stimuli, but the performance of SSVEP-based BCIs varied along with duration of stimuli; that is, the recognition rate was not affected by the number of stimuli when the duration of stimuli was long enough, while the information transfer rate (ITR) presented the opposite trend. It can be concluded that SSVEP is a reliable tool for marking and monitoring multiple stimuli simultaneously in cognitive studies, but much caution should be taken when choosing a suitable duration and the number of stimuli, in order to achieve optimal utility of BCIs in the future.
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Zhang, Sitao, Kainan Ma, Yibo Yin, Binbin Ren, and Ming Liu. "A Personalized Compression Method for Steady-State Visual Evoked Potential EEG Signals." Information 13, no. 4 (April 6, 2022): 186. http://dx.doi.org/10.3390/info13040186.
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As an informative electroencephalogram (EEG) signal, steady-state visual evoked potential (SSVEP) stands out from many paradigms for application in wireless wearable devices. However, its data are usually enormous, occupy too many bandwidth sources and require immense power when transmitted in the raw data form, so it is necessary to compress the signal. This paper proposes a personalized EEG compression and reconstruction algorithm for the SSVEP application. In the algorithm, to realize personalization, a primary artificial neural network (ANN) model is first pre-trained with the open benchmark database towards BCI application (BETA). Then, an adaptive ANN model is generated with incremental learning for each subject to compress their individual data. Additionally, a personalized, non-uniform quantization method is proposed to reduce the errors caused by compression. The recognition accuracy only decreases by 3.79% when the compression rate is 12.7 times, and is tested on BETA. The proposed algorithm can reduce signal loss by from 50.43% to 81.08% in the accuracy test compared to the case without ANN and uniform quantization.
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Touyama, Hideaki, and Mitsuru Sakuda. "Online Control of a Virtual Object with Collaborative SSVEP." Journal of Advanced Computational Intelligence and Intelligent Informatics 21, no. 7 (November 20, 2017): 1291–97. http://dx.doi.org/10.20965/jaciii.2017.p1291.
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In this paper, we propose a brain-computer interface (BCI) based on collaborative steady-state visually evoked potential (SSVEP). A technique for estimating the common direction of the gaze of multiple subjects is studied with a view to controlling a virtual object in a virtual environment. The electro-encephalograms (EEG) of eight volunteers are simultaneously recorded with two virtual cubes as visual stimuli. These two virtual cubes flicker at different rates, 6 Hz and 8 Hz, and the corresponding SSVEP is observed around the occipital area. The amplitude spectra of the EEG activity of individual subjects are analyzed, averaged, and synthesized to obtain the collaborative SSVEP. Machine learning is applied to estimate the common gaze direction of the eight subjects with the supervised data from fewer than eight subjects. The estimation accuracy is perfect only in the case of the collaborative SSVEP. One-dimensional control of a virtual ball is performed by controlling the common eye gaze direction, which induces the collaborative SSVEP.
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Sutjiadi, Raymond, Timothy John Pattiasina, and Resmana Lim. "SSVEP-based brain-computer interface for computer control application using SVM classifier." International Journal of Engineering & Technology 7, no. 4 (September 26, 2018): 2722. http://dx.doi.org/10.14419/ijet.v7i4.16139.
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In this research, a Brain Computer Interface (BCI) based on Steady State Visually Evoked Potential (SSVEP) for computer control applications using Support Vector Machine (SVM) is presented. For many years, people have speculated that electroencephalographic activities or other electrophysiological measures of brain function might provide a new non-muscular channel that can be used for sending messages or commands to the external world. BCI is a fast-growing emergent technology in which researchers aim to build a direct channel between the human brain and the computer. BCI systems provide a new communication channel for disabled people. Among many different types of the BCI systems, the SSVEP based has attracted more attention due to its ease of use and signal processing. SSVEPs are usually detected from the occipital lobe of the brain when the subject is looking at a twinkling light source. In this paper, SVM is used to classify SSVEP based on electroencephalogram data with proper features. Based on the experiment utilizing a 14-channel Electroencephalography (EEG) device, 80 percent of accuracy can be reached by our SSVEP-based BCI system using Linear SVM Kernel as classification engine.
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Nam, Chang S., Matthew Moore, Inchul Choi, and Yueqing Li. "Designing Better, Cost-Effective Brain–Computer Interfaces." Ergonomics in Design: The Quarterly of Human Factors Applications 23, no. 4 (October 2015): 13–19. http://dx.doi.org/10.1177/1064804615572625.
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Despite the increase in research interest in the brain–computer interface (BCI), there remains a general lack of understanding of, and even inattention to, human factors/ergonomics (HF/E) issues in BCI research and development. The goal of this article is to raise awareness of the importance of HF/E involvement in the emerging field of BCI technology by providing HF/E researchers with a brief guide on how to design and implement a cost-effective, steady-state visually evoked potential (SSVEP)–based BCI system. We also discuss how SSVEP BCI systems can be improved to accommodate users with special needs.
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R., Ashwini S., and H. C. Nagaraj. "Classification of EEG signal using EACA based approach at SSVEP-BCI." IAES International Journal of Artificial Intelligence (IJ-AI) 10, no. 3 (September 1, 2021): 717. http://dx.doi.org/10.11591/ijai.v10.i3.pp717-726.
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The brain-computer-interfaces (BCI) can also be referred towards a mindmachine interface that can provide a non-muscular communication channel in between the computer device and human brain. To measure the brain activity, electroencephalography (EEG) has been widely utilized in the applications of BCI to work system in real-time. It has been analyzed that the identification probability performed with other methodologies do not provide optimal classification accuracy. Therefore, it is required to focus on the process of feature extraction to achieve maximum classification accuracy. In this paper, a novel process of data-driven spatial has been proposed to improve the detection of steady state visually evoked potentials (SSVEPs) at BCI. Here, EACA has been proposed, which can develop the reproducibility of SSVEP across many trails. Further this can be utilized to improve the SSVEP from a noisy data signal by eliminating the activities of EEG background. In the simulation process, the SSVEP dataset recorded from given 11 subjects are considered. To validate the performance, the state-of-art method is considered to compare with the EDCA based proposed approach.
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Siribunyaphat, Nannaphat, and Yunyong Punsawad. "Steady-State Visual Evoked Potential-Based Brain–Computer Interface Using a Novel Visual Stimulus with Quick Response (QR) Code Pattern." Sensors 22, no. 4 (February 13, 2022): 1439. http://dx.doi.org/10.3390/s22041439.
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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.
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Zhang, Feng, Chengcheng Han, Lili Li, Xin Zhang, Jun Xie, and Yeping Li. "Research on High-Frequency Combination Coding-Based SSVEP-BCIs and Its Signal Processing Algorithms." Shock and Vibration 2015 (2015): 1–12. http://dx.doi.org/10.1155/2015/194230.
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This study presents a new steady-state visual evoked potential (SSVEP) paradigm for brain computer interface (BCI) systems. The new paradigm is High-Frequency Combination Coding-Based SSVEP (HFCC-SSVEP). The goal of this study is to increase the number of targets using fewer stimulation frequencies, with diminishing subject’s fatigue and reducing the risk of photosensitive epileptic seizures. This paper investigated the HFCC-SSVEP high-frequency response (beyond 25 Hz) for 3 frequencies (25 Hz, 33.33 Hz, and 40 Hz). HFCC-SSVEP producesnnwithnhigh stimulation frequencies through Time Series Combination Code. Furthermore, The Improved Hilbert-Huang Transform (IHHT) is adopted to extract time-frequency feature of the proposed SSVEP response. Lastly, the differentiation combination (DC) method is proposed to select the combination coding sequence in order to increase the recognition rate; as a result, IHHT algorithm and DC method for the proposed SSVEP paradigm in this study increase recognition efficiency so as to improve ITR and increase the stability of the BCI system. Furthermore, SSVEPs evoked by high-frequency stimuli (beyond 25 Hz) minimally diminish subject’s fatigue and prevent safety hazards linked to photo-induced epileptic seizures. This study tests five subjects in order to verify the feasibility of the proposed method.
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Li, Minglun, Dianning He, Chen Li, and Shouliang Qi. "Brain–Computer Interface Speller Based on Steady-State Visual Evoked Potential: A Review Focusing on the Stimulus Paradigm and Performance." Brain Sciences 11, no. 4 (April 1, 2021): 450. http://dx.doi.org/10.3390/brainsci11040450.
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The steady-state visual evoked potential (SSVEP), measured by the electroencephalograph (EEG), has high rates of information transfer and signal-to-noise ratio, and has been used to construct brain–computer interface (BCI) spellers. In BCI spellers, the targets of alphanumeric characters are assigned different visual stimuli and the fixation of each target generates a unique SSVEP. Matching the SSVEP to the stimulus allows users to select target letters and numbers. Many BCI spellers that harness the SSVEP have been proposed over the past two decades. Various paradigms of visual stimuli, including the procedure of target selection, layout of targets, stimulus encoding, and the combination with other triggering methods are used and considered to influence on the BCI speller performance significantly. This paper reviews these stimulus paradigms and analyzes factors influencing their performance. The fundamentals of BCI spellers are first briefly described. SSVEP-based BCI spellers, where only the SSVEP is used, are classified by stimulus paradigms and described in chronological order. Furthermore, hybrid spellers that involve the use of the SSVEP are presented in parallel. Factors influencing the performance and visual fatigue of BCI spellers are provided. Finally, prevailing challenges and prospective research directions are discussed to promote the development of BCI spellers.
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Farrow, Maree, Richard B. Silberstein, Florence Levy, Andrew Pipingas, Katie Wood, David A. Hay, and Frederick C. Jarman. "Prefrontal and Parietal Deficits in ADHD Suggested by Brain Electrical Activity Mapping During Children's Performance of the AX CPT." Australian Educational and Developmental Psychologist 13, no. 1 (May 1996): 59–68. http://dx.doi.org/10.1017/s0816512200027413.
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AbstractNine children meeting DSM-III-R criteria for Attention Deficit Hyperactivity Disorder (ADHD) and eighteen normal children participated in this study. A screening assessment revealed significantly more behavioural and academic problems in the ADHD group. Subjects performed a low demand visual vigilance task (the reference task) and the AX version of the continuous perfonnance task (CPT), while the steady-state visually evoked potential (SSVEP) was continuously recorded from 64 scalp electrode sites. The topography of the SSVEP amplitude difference between the reference and AX tasks was examined. In the 3.5 second interval between the appearances of the “A” and the “X” normal children showed transient reductions in right prefrontal SSVEP amplitude and a sustained reduction in right parieto-occipital SSVEP amplitude. These reductions in SSVEP amplitude were not seen in ADHD subjects. These results suggest that the presentation of a priming stimulus is associated with increased activation of right prefrontal and parieto-occipital regions in normal children, whereas the absence of this pattern of activation suggests a deficit in these processes in ADHD.
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Chuang, Chia-Chun, Chien-Ching Lee, Edmund-Cheung So, Chia-Hong Yeng, and Yeou-Jiunn Chen. "Multi-Task Learning-Based Deep Neural Network for Steady-State Visual Evoked Potential-Based Brain–Computer Interfaces." Sensors 22, no. 21 (October 29, 2022): 8303. http://dx.doi.org/10.3390/s22218303.
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Amyotrophic lateral sclerosis (ALS) causes people to have difficulty communicating with others or devices. In this paper, multi-task learning with denoising and classification tasks is used to develop a robust steady-state visual evoked potential-based brain–computer interface (SSVEP-based BCI), which can help people communicate with others. To ease the operation of the input interface, a single channel-based SSVEP-based BCI is selected. To increase the practicality of SSVEP-based BCI, multi-task learning is adopted to develop the neural network-based intelligent system, which can suppress the noise components and obtain a high level of accuracy of classification. Thus, denoising and classification tasks are selected in multi-task learning. The experimental results show that the proposed multi-task learning can effectively integrate the advantages of denoising and discriminative characteristics and outperform other approaches. Therefore, multi-task learning with denoising and classification tasks is very suitable for developing an SSVEP-based BCI for practical applications. In the future, an augmentative and alternative communication interface can be implemented and examined for helping people with ALS communicate with others in their daily lives.
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PATTERSON, JOHN, CAROLINE M. OWEN, RICHARD B. SILBERSTEIN, DAVID G. SIMPSON, ANDREW PIPINGAS, and GEOFFREY NIELD. "Steady State Visual Evoked Potential (SSVEP) Changes in Response to Olfactory Stimulation." Annals of the New York Academy of Sciences 855, no. 1 OLFACTION AND (November 1998): 625–27. http://dx.doi.org/10.1111/j.1749-6632.1998.tb10633.x.
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Zhu, Shixuan, Jingcheng Yang, Peng Ding, Fan Wang, Anmin Gong, and Yunfa Fu. "Optimization of SSVEP-BCI Virtual Reality Stereo Stimulation Parameters Based on Knowledge Graph." Brain Sciences 13, no. 5 (April 24, 2023): 710. http://dx.doi.org/10.3390/brainsci13050710.
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The steady-state visually evoked potential (SSVEP) is an important type of BCI that has various potential applications, including in virtual environments using virtual reality (VR). However, compared to VR research, the majority of visual stimuli used in the SSVEP-BCI are plane stimulation targets (PSTs), with only a few studies using stereo stimulation targets (SSTs). To explore the parameter optimization of the SSVEP-BCI virtual SSTs, this paper presents a parameter knowledge graph. First, an online VR stereoscopic stimulation SSVEP-BCI system is built, and a parameter dictionary for VR stereoscopic stimulation parameters (shape, color, and frequency) is established. The online experimental results of 10 subjects under different parameter combinations were collected, and a knowledge graph was constructed to optimize the SST parameters. The best classification performances of the shape, color, and frequency parameters were sphere (91.85%), blue (94.26%), and 13Hz (95.93%). With various combinations of virtual reality stereo stimulation parameters, the performance of the SSVEP-BCI varies. Using the knowledge graph of the stimulus parameters can help intuitively and effectively select appropriate SST parameters. The knowledge graph of the stereo target stimulation parameters presented in this work is expected to offer a way to convert the application of the SSVEP-BCI and VR.
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Kubacki, Arkadiusz, and Arkadiusz Jakubowski. "Classifier testing for the brain-machine interface (BCI) based on Steady State Visually Evoked Potential (SSVEP)." ITM Web of Conferences 15 (2017): 02003. http://dx.doi.org/10.1051/itmconf/20171502003.
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Sridhar, Saraswati, and Vidya Manian. "Assessment of Cognitive Aging Using an SSVEP-Based Brain–Computer Interface System." Big Data and Cognitive Computing 3, no. 2 (May 24, 2019): 29. http://dx.doi.org/10.3390/bdcc3020029.
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Cognitive deterioration caused by illness or aging often occurs before symptoms arise, and its timely diagnosis is crucial to reducing its medical, personal, and societal impacts. Brain–computer interfaces (BCIs) stimulate and analyze key cerebral rhythms, enabling reliable cognitive assessment that can accelerate diagnosis. The BCI system presented analyzes steady-state visually evoked potentials (SSVEPs) elicited in subjects of varying age to detect cognitive aging, predict its magnitude, and identify its relationship with SSVEP features (band power and frequency detection accuracy), which were hypothesized to indicate cognitive decline due to aging. The BCI system was tested with subjects of varying age to assess its ability to detect aging-induced cognitive deterioration. Rectangular stimuli flickering at theta, alpha, and beta frequencies were presented to subjects, and frontal and occipital Electroencephalographic (EEG) responses were recorded. These were processed to calculate detection accuracy for each subject and calculate SSVEP band power. A neural network was trained using the features to predict cognitive age. The results showed potential cognitive deterioration through age-related variations in SSVEP features. Frequency detection accuracy declined after age group 20–40, and band power declined throughout all age groups. SSVEPs generated at theta and alpha frequencies, especially 7.5 Hz, were the best indicators of cognitive deterioration. Here, frequency detection accuracy consistently declined after age group 20–40 from an average of 96.64% to 69.23%. The presented system can be used as an effective diagnosis tool for age-related cognitive decline.
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Yu, Je-Hun, and Kwee-Bo Sim. "Robot Control based on Steady-State Visual Evoked Potential using Arduino and Emotiv Epoc." Journal of Korean Institute of Intelligent Systems 25, no. 3 (June 25, 2015): 254–59. http://dx.doi.org/10.5391/jkiis.2015.25.3.254.
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Albahri, A. S., Z. T. Al-qaysi, Laith Alzubaidi, Alhamzah Alnoor, O. S. Albahri, A. H. Alamoodi, and Anizah Abu Bakar. "A Systematic Review of Using Deep Learning Technology in the Steady-State Visually Evoked Potential-Based Brain-Computer Interface Applications: Current Trends and Future Trust Methodology." International Journal of Telemedicine and Applications 2023 (April 30, 2023): 1–24. http://dx.doi.org/10.1155/2023/7741735.
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The significance of deep learning techniques in relation to steady-state visually evoked potential- (SSVEP-) based brain-computer interface (BCI) applications is assessed through a systematic review. Three reliable databases, PubMed, ScienceDirect, and IEEE, were considered to gather relevant scientific and theoretical articles. Initially, 125 papers were found between 2010 and 2021 related to this integrated research field. After the filtering process, only 30 articles were identified and classified into five categories based on their type of deep learning methods. The first category, convolutional neural network (CNN), accounts for 70% ( n = 21 / 30 ). The second category, recurrent neural network (RNN), accounts for 10% ( n = 3 / 30 ). The third and fourth categories, deep neural network (DNN) and long short-term memory (LSTM), account for 6% ( n = 30 ). The fifth category, restricted Boltzmann machine (RBM), accounts for 3% ( n = 1 / 30 ). The literature’s findings in terms of the main aspects identified in existing applications of deep learning pattern recognition techniques in SSVEP-based BCI, such as feature extraction, classification, activation functions, validation methods, and achieved classification accuracies, are examined. A comprehensive mapping analysis was also conducted, which identified six categories. Current challenges of ensuring trustworthy deep learning in SSVEP-based BCI applications were discussed, and recommendations were provided to researchers and developers. The study critically reviews the current unsolved issues of SSVEP-based BCI applications in terms of development challenges based on deep learning techniques and selection challenges based on multicriteria decision-making (MCDM). A trust proposal solution is presented with three methodology phases for evaluating and benchmarking SSVEP-based BCI applications using fuzzy decision-making techniques. Valuable insights and recommendations for researchers and developers in the SSVEP-based BCI and deep learning are provided.
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Müller, Matthias M., and Ronald Hübner. "Can the Spotlight of Attention Be Shaped Like a Doughnut? Evidence From Steady-State Visual Evoked Potentials." Psychological Science 13, no. 2 (March 2002): 119–24. http://dx.doi.org/10.1111/1467-9280.00422.
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Visual attention enables observers to extract and process high-priority information in the visual field. Controversy remains as to whether or not observers can ignore information that falls within the spatial beam of attention. We used an objective physiological measure, the steady-state visual evoked potential (SSVEP), to investigate this question. A stream of flickering small, uppercase letters was embedded in the center of a stream of large, uppercase letters. A unitary beam would result in no difference of the SSVEP amplitude elicited by the small letter stream when it was attended versus ignored (i.e., when subjects attended the large letter stream). Contrary to this prediction, SSVEP amplitude increased by almost 100% when the small letter stream was attended compared with when it was ignored. The results support the notion that the attentional spotlight can be formed like a doughnut, processing central information differentially depending on whether it is attended or ignored.
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Silberstein, R. B., A. Pipingas, J. Song, D. A. Camfield, P. J. Nathan, and C. Stough. "Examining Brain-Cognition Effects of Ginkgo Biloba Extract: Brain Activation in the Left Temporal and Left Prefrontal Cortex in an Object Working Memory Task." Evidence-Based Complementary and Alternative Medicine 2011 (2011): 1–10. http://dx.doi.org/10.1155/2011/164139.
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Ginkgo Biloba extract (GBE) is increasingly used to alleviate symptoms of age related cognitive impairment, with preclinical evidence pointing to a pro-cholinergic effect. While a number of behavioral studies have reported improvements to working memory (WM) associated with GBE, electrophysiological studies of GBE have typically been limited to recordings during a resting state. The current study investigated the chronic effects of GBE on steady state visually evoked potential (SSVEP) topography in nineteen healthy middle-aged (50-61 year old) male participants whilst completing an object WM task. A randomized double-blind crossover design was employed in which participants were allocated to receive 14 days GBE and 14 days placebo in random order. For both groups, SSVEP was recorded from 64 scalp electrode sites during the completion of an object WM task both pre- and 14 days post-treatment. GBE was found to improve behavioural performance on the WM task. GBE was also found to increase the SSVEP amplitude at occipital and frontal sites and increase SSVEP latency at left temporal and left frontal sites during the hold component of the WM task. These SSVEP changes associated with GBE may represent more efficient processing during WM task completion.
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Chen, Yeou-Jiunn, Pei-Chung Chen, Shih-Chung Chen, and Chung-Min Wu. "Denoising Autoencoder-Based Feature Extraction to Robust SSVEP-Based BCIs." Sensors 21, no. 15 (July 23, 2021): 5019. http://dx.doi.org/10.3390/s21155019.
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For subjects with amyotrophic lateral sclerosis (ALS), the verbal and nonverbal communication is greatly impaired. Steady state visually evoked potential (SSVEP)-based brain computer interfaces (BCIs) is one of successful alternative augmentative communications to help subjects with ALS communicate with others or devices. For practical applications, the performance of SSVEP-based BCIs is severely reduced by the effects of noises. Therefore, developing robust SSVEP-based BCIs is very important to help subjects communicate with others or devices. In this study, a noise suppression-based feature extraction and deep neural network are proposed to develop a robust SSVEP-based BCI. To suppress the effects of noises, a denoising autoencoder is proposed to extract the denoising features. To obtain an acceptable recognition result for practical applications, the deep neural network is used to find the decision results of SSVEP-based BCIs. The experimental results showed that the proposed approaches can effectively suppress the effects of noises and the performance of SSVEP-based BCIs can be greatly improved. Besides, the deep neural network outperforms other approaches. Therefore, the proposed robust SSVEP-based BCI is very useful for practical applications.
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Deepak D. Kapgate and Krishna Prasad K. "An improved model for the use of facial stimulation in hybrid SSVEP+P300 brain-computer interfaces." World Journal of Advanced Engineering Technology and Sciences 8, no. 1 (February 28, 2023): 330–39. http://dx.doi.org/10.30574/wjaets.2023.8.1.0046.
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Purpose: This research proposes a hybrid BCI that integrates Steady State Visual Evoked Potentials (SSVEP) and Event Related Potentials (P300) simultaneously. We included human facial structure into a visual stimulus to elicit stronger cortical responses in a hybrid SSVEP+P300 BCI. We also discussed the possibilities of triggering one potential with facial stimuli and another with non-facial stimuli. Methods: To elicit SSVEP and P300 responses, non-face and neutral-face stimulus paradigms are presented. We also tested the neutral-face and flicker paradigm, in which non-face stimuli would elicit SSVEP and neutral-face stimuli would elicit P300. Results: The results showed that the latter paradigm evoked more robust cortical potentials, leading to enhanced system accuracy and ITR. The neutral-face and flicker paradigm has an average accuracy of 91.62%, with an average system communication rate of 22.04 bits per second. Conclusions: The author talked about visual stimulus characteristics that might change the way that multiple brain potentials are activated simultaneously and how that affects the individual potentials.
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Teng, Fei, Yixin Chen, Aik Min Choong, Scott Gustafson, Christopher Reichley, Pamela Lawhead, and Dwight Waddell. "Square or Sine: Finding a Waveform with High Success Rate of Eliciting SSVEP." Computational Intelligence and Neuroscience 2011 (2011): 1–5. http://dx.doi.org/10.1155/2011/364385.
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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.
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Yang, Dalin, Trung-Hau Nguyen, and Wan-Young Chung. "A Bipolar-Channel Hybrid Brain-Computer Interface System for Home Automation Control Utilizing Steady-State Visually Evoked Potential and Eye-Blink Signals." Sensors 20, no. 19 (September 24, 2020): 5474. http://dx.doi.org/10.3390/s20195474.
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The goal of this study was to develop and validate a hybrid brain-computer interface (BCI) system for home automation control. Over the past decade, BCIs represent a promising possibility in the field of medical (e.g., neuronal rehabilitation), educational, mind reading, and remote communication. However, BCI is still difficult to use in daily life because of the challenges of the unfriendly head device, lower classification accuracy, high cost, and complex operation. In this study, we propose a hybrid BCI system for home automation control with two brain signals acquiring electrodes and simple tasks, which only requires the subject to focus on the stimulus and eye blink. The stimulus is utilized to select commands by generating steady-state visually evoked potential (SSVEP). The single eye blinks (i.e., confirm the selection) and double eye blinks (i.e., deny and re-selection) are employed to calibrate the SSVEP command. Besides that, the short-time Fourier transform and convolution neural network algorithms are utilized for feature extraction and classification, respectively. The results show that the proposed system could provide 38 control commands with a 2 s time window and a good accuracy (i.e., 96.92%) using one bipolar electroencephalogram (EEG) channel. This work presents a novel BCI approach for the home automation application based on SSVEP and eye blink signals, which could be useful for the disabled. In addition, the provided strategy of this study—a friendly channel configuration (i.e., one bipolar EEG channel), high accuracy, multiple commands, and short response time—might also offer a reference for the other BCI controlled applications.
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Lee, Hyeon Kyu, and Young-Seok Choi. "Enhancing SSVEP-Based Brain-Computer Interface with Two-Step Task-Related Component Analysis." Sensors 21, no. 4 (February 12, 2021): 1315. http://dx.doi.org/10.3390/s21041315.
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Among various methods for frequency recognition of the steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) study, a task-related component analysis (TRCA), which extracts discriminative spatial filters for classifying electroencephalogram (EEG) signals, has gathered much interest. The TRCA-based SSVEP method yields lower computational cost and higher classification performance compared to existing SSVEP methods. In spite of its utility, the TRCA-based SSVEP method still suffers from the degradation of the frequency recognition rate in cases where EEG signals with a short length window are used. To address this issue, here, we propose an improved strategy for decoding SSVEPs, which is insensitive to a window length by carrying out two-step TRCA. The proposed method reuses the spatial filters corresponding to target frequencies generated by the TRCA. Followingly, the proposed method accentuates features for target frequencies by correlating individual template and test data. For the evaluation of the performance of the proposed method, we used a benchmark dataset with 35 subjects and confirmed significantly improved performance comparing with other existing SSVEP methods. These results imply the suitability as an efficient frequency recognition strategy for SSVEP-based BCI applications.
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Müller-Putz, Gernot R., Reinhold Scherer, Christian Brauneis, and Gert Pfurtscheller. "Steady-state visual evoked potential (SSVEP)-based communication: impact of harmonic frequency components." Journal of Neural Engineering 2, no. 4 (October 25, 2005): 123–30. http://dx.doi.org/10.1088/1741-2560/2/4/008.
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Oikonomou, Vangelis P. "Human Recognition Using Deep Neural Networks and Spatial Patterns of SSVEP Signals." Sensors 23, no. 5 (February 22, 2023): 2425. http://dx.doi.org/10.3390/s23052425.
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Brain biometrics have received increasing attention from the scientific community due to their unique properties compared to traditional biometric methods. Many studies have shown that EEG features are distinct across individuals. In this study, we propose a novel approach by considering spatial patterns of the brain’s responses due to visual stimulation at specific frequencies. More specifically, we propose, for the identification of the individuals, to combine common spatial patterns with specialized deep-learning neural networks. The adoption of common spatial patterns gives us the ability to design personalized spatial filters. In addition, with the help of deep neural networks, the spatial patterns are mapped into new (deep) representations where the discrimination between individuals is performed with a high correct recognition rate. We conducted a comprehensive comparison between the performance of the proposed method and several classical methods on two steady-state visual evoked potential datasets consisting of thirty-five and eleven subjects, respectively. Furthermore, our analysis includes a large number of flickering frequencies in the steady-state visual evoked potential experiment. Experiments on these two steady-state visual evoked potential datasets showed the usefulness of our approach in terms of person identification and usability. The proposed method achieved an averaged correct recognition rate of 99% over a large number of frequencies for the visual stimulus.
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Leite, Harlei Miguel de Arruda, Sarah Negreiros de Carvalho, Thiago Bulhões da Silva Costa, Romis Attux, Heiko Horst Hornung, and Dalton Soares Arantes. "Analysis of User Interaction with a Brain-Computer Interface Based on Steady-State Visually Evoked Potentials: Case Study of a Game." Computational Intelligence and Neuroscience 2018 (2018): 1–10. http://dx.doi.org/10.1155/2018/4920132.
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This paper presents a systematic analysis of a game controlled by a Brain-Computer Interface (BCI) based on Steady-State Visually Evoked Potentials (SSVEP). The objective is to understand BCI systems from the Human-Computer Interface (HCI) point of view, by observing how the users interact with the game and evaluating how the interface elements influence the system performance. The interactions of 30 volunteers with our computer game, named “Get Coins,” through a BCI based on SSVEP, have generated a database of brain signals and the corresponding responses to a questionnaire about various perceptual parameters, such as visual stimulation, acoustic feedback, background music, visual contrast, and visual fatigue. Each one of the volunteers played one match using the keyboard and four matches using the BCI, for comparison. In all matches using the BCI, the volunteers achieved the goals of the game. Eight of them achieved a perfect score in at least one of the four matches, showing the feasibility of the direct communication between the brain and the computer. Despite this successful experiment, adaptations and improvements should be implemented to make this innovative technology accessible to the end user.
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Shi, Nanlin. "Steady-state visual evoked potential (SSVEP)-based brain–computer interface (BCI) of Chinese speller for a patient with amyotrophic lateral sclerosis: A case report." Journal of Neurorestoratology 08, no. 01 (2020): 40–52. http://dx.doi.org/10.26599/jnr.2020.9040003.
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This study applied a steady-state visual evoked potential (SSVEP) based brain–computer interface (BCI) to a patient in lock-in state with amyotrophic lateral sclerosis (ALS) and validated its feasibility for communication. The developed calibration-free and asynchronous spelling system provided a natural and efficient communication experience for the patient, achieving a maximum free-spelling accuracy above 90% and an information transfer rate of over 22.203 bits/min. A set of standard frequency scanning and task spelling data were also acquired to evaluate the patient’s SSVEP response and to facilitate further personalized BCI design. The results demonstrated that the proposed SSVEP-based BCI system was practical and efficient enough to provide daily life communication for ALS patients.
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Mouli, Surej, Ramaswamy Palaniappan, Emmanuel Molefi, and Ian McLoughlin. "In-Ear Electrode EEG for Practical SSVEP BCI." Technologies 8, no. 4 (November 5, 2020): 63. http://dx.doi.org/10.3390/technologies8040063.
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Steady State Visual Evoked Potential (SSVEP) methods for brain–computer interfaces (BCI) are popular due to higher information transfer rate and easier setup with minimal training, compared to alternative methods. With precisely generated visual stimulus frequency, it is possible to translate brain signals into external actions or signals. Traditionally, SSVEP data is collected from the occipital region using electrodes with or without gel, normally mounted on a head cap. In this experimental study, we develop an in-ear electrode to collect SSVEP data for four different flicker frequencies and compare against occipital scalp electrode data. Data from five participants demonstrates the feasibility of in-ear electrode based SSVEP, significantly enhancing the practicability of wearable BCI applications.
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Ko, Li-Wei, S. S. K. Ranga, Oleksii Komarov, and Chung-Chiang Chen. "Development of Single-Channel Hybrid BCI System Using Motor Imagery and SSVEP." Journal of Healthcare Engineering 2017 (2017): 1–7. http://dx.doi.org/10.1155/2017/3789386.
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Numerous EEG-based brain-computer interface (BCI) systems that are being developed focus on novel feature extraction algorithms, classification methods and combining existing approaches to create hybrid BCIs. Several recent studies demonstrated various advantages of hybrid BCI systems in terms of an improved accuracy or number of commands available for the user. But still, BCI systems are far from realization for daily use. Having high performance with less number of channels is one of the challenging issues that persists, especially with hybrid BCI systems, where multiple channels are necessary to record information from two or more EEG signal components. Therefore, this work proposes a single-channel (C3 or C4) hybrid BCI system that combines motor imagery (MI) and steady-state visually evoked potential (SSVEP) approaches. This study demonstrates that besides MI features, SSVEP features can also be captured from C3 or C4 channel. The results show that due to rich feature information (MI and SSVEP) at these channels, the proposed hybrid BCI system outperforms both MI- and SSVEP-based systems having an average classification accuracy of 85.6 ± 7.7% in a two-class task.
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Singla, Rajesh. "HYBRID BRAIN–COMPUTER INTERFACE PARADIGM — A STUDY." Biomedical Engineering: Applications, Basis and Communications 30, no. 03 (May 30, 2018): 1850022. http://dx.doi.org/10.4015/s1016237218500229.
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The advancements in the field of brain–computer interface (BCI) are driven by the underlying motive of improving quality of life for both healthy as well as locked in subjects. Since BCI’s are based on the response of the human brain to training or external stimuli, the improvement in terms of performance can be achieved by either enhancing the subject training procedure or by improving the external stimuli to produce maximized event related potential (ERP). P300 and steady-state visually evoked potential (SSVEP) approaches have been the most common paradigms used for stimulus-based BCI’s world over. But recently, a large number of researchers are facing a problem of BCI illiteracy in subjects, where some of the subjects showed ineffective results while training with these BCI as independent stimuli. The concept of hybrid brain–computer interface (hBCI) is a step towards eradicating this problem. Our research deals with external stimuli-based ERP generation where we discuss and compare with experimentation, three different options of visual stimulus: conventional SSVEP stimulus, P300-SSVEP hybrid stimulus, distinct target colors for P300-SSVEP-based hybrid stimulus. This paper introduces a novel hBCI paradigm and discusses the validation of improved results by comparing with the already existing stimuli options. The parameters of comparison that were considered to validate our proposal were decision accuracy (Acc), information transfer rate (ITR) and false activation rate (FAR).
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Chen, Kun, Quan Liu, and Qing Song Ai. "Multi-Channel SSVEP Pattern Recognition Based on MUSIC." Applied Mechanics and Materials 539 (July 2014): 84–88. http://dx.doi.org/10.4028/www.scientific.net/amm.539.84.
Full textAbstract:
Brain computer interfaces (BCIs) have become a research hotspot in recent years because of great potentials to help disabled people communicate with the outside world. Among different paradigms, steady state visual evoked potential (SSVEP)-based BCIs are commonly implemented in real applications, because they provide higher signal to noise ratio (SNR) and greater information transfer rate (ITR) than other BCI techniques. Various algorithms have been employed for SSVEP signal processing, like fast Fourier transform (FFT), wavelet analysis and canonical correlation analysis (CCA). In this paper, a new method based on multiple signal classification (MUSIC) was proposed for SSVEP feature extraction. The experimental results proved that it could provide higher frequency resolution and the recognition accuracy was excellent via adjusting some parameters.