Relevant bibliographies by topics / Steady-State Visually Evoked Potential (SSVEP)

Academic literature on the topic 'Steady-State Visually Evoked Potential (SSVEP)'

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Published: 6 September 2023

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Journal articles on the topic "Steady-State Visually Evoked Potential (SSVEP)"

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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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Dissertations / Theses on the topic "Steady-State Visually Evoked Potential (SSVEP)"

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Calore, E. "TOWARDS STEADY-STATE VISUALLY EVOKED POTENTIALS BRAIN-COMPUTER INTERFACES FOR VIRTUAL REALITY ENVIRONMENTS EXPLICIT AND IMPLICIT INTERACTION." Doctoral thesis, Università degli Studi di Milano, 2014. http://hdl.handle.net/2434/233319.

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In the last two decades, Brain-Computer Interfaces (BCIs) have been investigated mainly for the purpose of implementing assistive technologies able to provide new channels for communication and control for people with severe disabilities. Nevertheless, more recently, thanks to technical and scientific advances in the different research fields involved, BCIs are gaining greater attention also for their adoption by healthy users, as new interaction devices. This thesis is dedicated to to the latter goal and in particular will deal with BCIs based on the Steady State Visual Evoked Potential (SSVEP), which in previous works demonstrated to be one of the most flexible and reliable approaches. SSVEP based BCIs could find applications in different contexts, but one which is particularly interesting for healthy users, is their adoption as new interaction devices for Virtual Reality (VR) environments and Computer Games. Although being investigated since several years, BCIs still poses several limitations in terms of speed, reliability and usability with respect to ordinary interaction devices. Despite of this, they may provide additional, more direct and intuitive, explicit interaction modalities, as well as implicit interaction modalities otherwise impossible with ordinary devices. This thesis, after a comprehensive review of the different research fields being the basis of a BCI exploiting the SSVEP modality, present a state-of-the-art open source implementation using a mix of pre-existing and custom software tools. The proposed implementation, mainly aimed to the interaction with VR environments and Computer Games, has then been used to perform several experiments which are hereby described as well. Initially performed experiments aim to stress the validity of the provided implementation, as well as to show its usability with a commodity bio-signal acquisition device, orders of magnitude less expensive than commonly used ones, representing a step forward in the direction of practical BCIs for end users applications. The proposed implementation, thanks to its flexibility, is used also to perform novel experiments aimed to investigate the exploitation of stereoscopic displays to overcome a known limitation of ordinary displays in the context of SSVEP based BCIs. Eventually, novel experiments are presented investigating the use of the SSVEP modality to provide also implicit interaction. In this context, a first proof of concept Passive BCI based on the SSVEP response is presented and demonstrated to provide information exploitable for prospective applications.
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Raza, Asim. "SSVEP based EEG Interface for Google Street View Navigation." Thesis, Linköpings universitet, Medie- och Informationsteknik, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-104276.

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Brain-computer interface (BCI) or Brain Machine Interface (BMI) provides direct communication channel between user’s brain and an external device without any requirement of user’s physical movement. Primarily BCI has been employed in medical sciences to facilitate the patients with severe motor, visual and aural impairments. More recently many BCI are also being used as a part of entertainment. BCI differs from Neuroprosthetics, a study within Neuroscience, in terms of its usage; former connects the brain with a computer or external device while the later connects the nervous system to an implanted device. A BCI receives the modulated input from user either invasively or non-invasively. The modulated input, concealed in the huge amount of noise, contains distinct brain patterns based on the type of activity user is performing at that point in time. Primary task of a typical BCI is to find out those distinct brain patterns and translates them to meaningful communication command set. Cursor controllers, Spellers, Wheel Chair and robot Controllers are classic examples of BCI applications. This study aims to investigate an Electroencephalography (EEG) based non-invasive BCI in general and its interaction with a web interface in particular. Different aspects related to BCI are covered in this work including feedback techniques, BCI frameworks, commercial BCI hardware, and different BCI applications. BCI paradigm Steady State Visually Evoked Potentials (SSVEP) is being focused during this study. A hybrid solution is developed during this study, employing a general purpose BCI framework OpenViBE, which comprised of a low-level stimulus management and control module and a web based Google Street View client application. This study shows that a BCI can not only provide a way of communication for the impaired subjects but it can also be a multipurpose tool for a healthy person. During this study, it is being established that the major hurdles that hamper the performance of a BCI system are training protocols, BCI hardware and signal processing techniques. It is also observed that a controlled environment and expert assistance is required to operate a BCI system.
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Line, Per, and pline@swin edu au. "Cognition and the steady state visually evoked potential." Swinburne University of Technology, 1993. http://adt.lib.swin.edu.au./public/adt-VSWT20060118.170228.

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This masters thesis examines the hemispheric activation pattern of the cognitive processes involved in a complex mental rotations test (MRT) (Vandenberg and Kuse, 1978) using Steady-State Probe Topography (SSPT) (Silberstein et al, 1990) as a method to index brain activity. The Steady State Visually Evoked Potential (SSVEP) was recorded from 64 electrode sites using a multichannel electrode helmet, and elicited by a 13 Hz sinusoidal visual flicker, whilst the subjects were performing a visual vigilance Baseline task and the MRT. Forty-one right handed subjects (twenty male and twenty-one female) were used. In the MRT the subjects were required to choose the two figures which correctly matched the criterion figure in the centre. The figures were three-dimensional objects represented in two-dimensions on a computer screen. A significant finding of this study was that when all the subjects were considered as one group, no noticeable lateralization in cerebral activation associated with mental rotation was evident. When analyzing the results for the subjects, partitioned into two groups according to gender, evidence was found suggesting that the cortical processing associated with mental rotation may be more localized bilaterally in the males than the females. However, no noticeable lateralization effects for mental rotation were found in the males or females, and hence no gender differences in hemispheric lateralization was evident. An important finding was the emergence of gender differences in hemispheric lateralization in subsets of subjects performing with higher spatial ability. A left hemisphere lateralization for mental rotation was associated with the Best Performance Male group. The Best Performance Female group showed the opposite effect, where a right hemisphere lateralization was associated with better performance on the task. The lateralization effect appeared to be stronger in the Best Performance Males than the Best Performance Females. An important conclusion from this study is that when examining for hemispheric lateralization effects in mental rotation, and possibly other visual-spatial tasks, not only gender effects need to be considered, but the level of spatial ability in the comparison groups needs also to be taken into account.
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Pipingas, Andrew, and apipingas@bsi swin edu au. "Steady-state visually evoked potential correlates of object recognition memory." Swinburne University of Technology, 2003. http://adt.lib.swin.edu.au./public/adt-VSWT20050322.171342.

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Object recognition memory (ORM) refers to both recognition of an object and the memory of having seen it before. In humans, ORM has been investigated using functional neuroimaging and electrophysiological techniques with tests of episodic memory retrieval involving recollection of previously studied items. Processes involved in the maintenance of a mental state adopted for the performance of a retrieval task (retrieval mode) appear to involve right frontal neural regions. More transient processes occurring at the time of item recollection (retrieval success) have shown scalp activity over parietal and right frontal regions. This activity is thought to originate in the medial temporal lobes and the underlying right frontal cortex respectively. The aforementioned findings have been derived mainly from studies using verbal stimuli. It is uncertain whether the same neural regions are involved in object recollection. It is also uncertain whether sustained modal and transient item-related activity involve the same or different right frontal regions. In this study, steady-state probe topography (SSPT) was used to investigate both sustained and transient processes involved in the retrieval of abstract pictorial objects from memory. The ability to vary the evaluation period of the steady-state visually evoked potential (SSVEP) allows investigation of cognitive processes occurring over different time scales. Neural regions involved in sustained modal processes were identified by examining the SSVEP values averaged over the duration of a memory retrieval task. Sustained SSVEP effects were observed over right fronto-temporal regions. Neural regions involved in transient retrieval success processes were identified by comparing the transient SSVEP responses to tasks with different memory loads. Comparison of a higher with a lower memory load condition showed SSVEP effects over parieto-temporal and right inferior frontal regions. Larger differences between memory loads gave effects that were larger and more right lateralized. Retrieval mode and retrieval success processes showed SSVEP effects over different right frontal regions. It was also found that, in contrast to the left lateralized parietal ERP response to recollected verbal stimuli, the SSVEP effects produced with abstract pictorial shapes showed a more bilateral pattern. This was considered to reflect the relatively non-verbalizable pictorial nature of the stimuli.
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Steedman, David John. "Simultaneous measurement of human brain activity using near infra-red spectroscopy, electroencephalogram and the steady state visually evoked potential." Swinburne Research Bank, 2008. http://hdl.handle.net/1959.3/48535.

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Thesis (M.Sc.) - Swinburne University of Technology, Brain Sciences Institute, 2008.
A thesis submitted for M.Sc by Research, Brain Sciences Institute, Swinburne University of Technology - 2008. Typescript. Includes bibliographical references (p. 117-153)
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Talevi, Luca. "Sviluppo e test di un sistema BCI SSVEP-based." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2016. http://amslaurea.unibo.it/11636/.

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Una Brain Computer Interface (BCI) è un dispositivo che permette la misura e l’utilizzo di segnali cerebrali al fine di comandare software e/o periferiche di vario tipo, da semplici videogiochi a complesse protesi robotizzate. Tra i segnali attualmente più utilizzati vi sono i Potenziali Evocati Visivi Steady State (SSVEP), variazioni ritmiche di potenziale elettrico registrabili sulla corteccia visiva primaria con un elettroencefalogramma (EEG) non invasivo; essi sono evocabili attraverso una stimolazione luminosa periodica, e sono caratterizzati da una frequenza di oscillazione pari a quella di stimolazione. Avendo un rapporto segnale rumore (SNR) particolarmente favorevole ed una caratteristica facilmente studiabile, gli SSVEP sono alla base delle più veloci ed immediate BCI attualmente disponibili. All’utente vengono proposte una serie di scelte ciascuna associata ad una stimolazione visiva a diversa frequenza, fra le quali la selezionata si ripresenterà nelle caratteristiche del suo tracciato EEG estratto in tempo reale. L’obiettivo della tesi svolta è stato realizzare un sistema integrato, sviluppato in LabView che implementasse il paradigma BCI SSVEP-based appena descritto, consentendo di: 1. Configurare la generazione di due stimoli luminosi attraverso l’utilizzo di LED esterni; 2. Sincronizzare l’acquisizione del segnale EEG con tale stimolazione; 3. Estrarre features (attributi caratteristici di ciascuna classe) dal suddetto segnale ed utilizzarle per addestrare un classificatore SVM; 4. Utilizzare il classificatore per realizzare un’interfaccia BCI realtime con feedback per l’utente. Il sistema è stato progettato con alcune delle tecniche più avanzate per l’elaborazione spaziale e temporale del segnale ed il suo funzionamento è stato testato su 4 soggetti sani e comparato alle più moderne BCI SSVEP-based confrontabili rinvenute in letteratura.
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Si, Mohammed Hakim. "Design and Study of Interactive Systems based on Brain- Computer Interfaces and Augmented Reality." Thesis, Rennes, INSA, 2019. http://www.theses.fr/2019ISAR0024.

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Les Interfaces Cerveau Ordinateur (ICO) permettent l’interaction à partir de l’activité cérébrale. La Réalité Augmentée (RA) elle, permet d’intégrer des éléments virtuels dans un environnement réel. Dans cette thèse, nous avons cherché à concevoir des systèmes interactifs exploitant des ICO dans des environnements RA, afin de proposer de nouveaux moyens d’interagir avec des éléments réels et virtuels. Dans la première partie de cette thèse, nous avons étudié la possibilité d’extraire différents signaux cérébraux dans un contexte de RA. Nous avons ainsi montré qu’il était possible d’exploiter les Potentiels Evoqués Visuels Stationnaires (SSVEP) en RA. Puis, nous avons montré la possibilité d’extraire des Potentiels d’Erreur des signaux cérébraux, lorsqu’un utilisateur est soumis à des types d’erreurs fréquents en RA. Dans la seconde partie, nous avons approfondi nos recherches sur l’utilisation des SSVEP pour l’interaction en RA. Nous avons notamment proposé HCCA, un nouvel algorithme permettant la reconnaissance asynchrone de réponses SSVEP. Nous avons ensuite étudié la conception d’interfaces de RA, pour des systèmes interactifs, intuitifs performants. Enfin nous avons illustré nos résultats à travers le développement d’un système de domotique utilisant les SSVEP et la RA, qui s’intègre à une plateforme de maison intelligente industrielle
Brain-Computer Interfaces (BCI) enable interaction directly from brain activity. Augmented Reality (AR) on the other hand, enables the integration of virtual elements in the real world. In this thesis, we aimed at designing interactive systems associating BCIs and AR, to offer new means of hands-free interaction with real and virtual elements. In the first part, we have studied the possibility to extract different BCI paradigms in AR. We have shown that it was possible to use Steady-State Visual Evoked Potentials (SSVEP) in AR. Then, we have studied the possibility to extract Error-Related Potentials (ErrPs) in AR, showing that ErrPs were elicited in users facing errors, often occurring in AR. In the second part, we have deepened our research in the use of SSVEP for direct interaction in AR. We have proposed HCCA, a new algorithm for self-paced detection of SSVEP responses. Then, we have studied the design of AR interfaces, for the development of intuitive and efficient interactive systems. Lastly, we have illustrated our results, through the development of a smart-home system combining SSVEP and AR, which integrates in a commercially available smart-home system
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Gregori, Federica. "Sistemi di comunicazione alternativa basati su Brain Computer Interface: stato dell’arte e prospettive future." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2020. http://amslaurea.unibo.it/19912/.

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Con il termine Brain-Computer Interface (BCI) si indica un sistema hardware e software che permette, a partire da segnali di origine cerebrale, di tradurre le intenzioni dell’utente in comandi per il controllo di un dispositivo di output, come computer, sintetizzatori vocali, apparecchi di assistenza e neuroprotesi. Questa tipologia di applicazioni non richiede l’impiego di muscoli periferici, poiché sfrutta solamente specifici segnali generati dall’attività cerebrale. In particolare, il presente elaborato tratta le BCI-speller, ovvero sistemi che permettono la scrittura di un testo sfruttando le variazioni del segnale elettroencefalografico (EEG) suscitate attraverso un’interfaccia grafica (GUI). La GUI è costituita da lettere, simboli e numeri opportunamente presentati, così che, se il soggetto presta attenzione ad uno di essi, particolari potenziali cerebrali vengono elicitati nell’EEG e sfruttati per identificare e quindi selezionare tale simbolo. L’obiettivo dell’elaborato è introdurre l’emergente e promettente campo di ricerca delle BCI, facendo luce sulle caratteristiche dei componenti che le caratterizzano e sulle varie applicazioni a cui si prestano, concentrandosi sulle BCI-speller. A tal fine, sono state presentate BCI-speller basate su due particolari potenziali cerebrali (P300 e SSVEP), ponendo particolare attenzione sugli aspetti che possono portare ad un loro miglioramento. Maggiore enfasi è stata quindi posta sulle diverse GUI delle BCI-speller basate su questi potenziali, in quanto modifiche associate alla presentazione dello stimolo e alla facilità d’uso di queste interfacce possono migliorare in prima battuta il potenziale elicitato, e di conseguenza la prestazione generale della BCI. Infine, sono stati evidenziati i vantaggi e i limiti associati a queste tecnologie, nonché gli sviluppi futuri nella prospettiva di un impiego quotidiano di queste tecnologie da parte di pazienti affetti da disturbi neuromuscolari.
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Ibáñez, Soria David 1983. "Analysis of brain dynamics using echo-state networks." Doctoral thesis, Universitat Pompeu Fabra, 2018. http://hdl.handle.net/10803/663491.

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In the last decade recurrent neural networks have revolutionized the field of artificial intelligence. Their cyclic connections provide them with memory and thus with the capability of modeling processes with temporal context. Echo-state networks are a framework for recurrent neural networks that enormously simplifies their design and training. In this thesis we explore the capabilities of echo-state networks and their application in EEG feature extraction and classification problems. In a first study, we proved that such networks are capable of detecting generalized synchronization changes between two chaotic time-series. In a second study, we used echo-state networks to characterize the non-stationary nature of what has been considered so far to be a stationary brain response, namely steady-state visual evoked potentials (SSVEPs). Finally, in a third study, we successfully proposed a novel biomarker for attention deficit hyperactivity disorder (ADHD), which is capable of quantifying EEG dynamical changes between low and normal attention-arousal conditions. The results presented here demonstrate the excellent non-stationary detection capabilities of these networks, and their applicability to electrophysiological data analysis.
En la última decada las redes neuronales recurrentes han revolucionado el campo de la inteligencia artificial. Sus conexiones cíclicas les proporcionan memoria y por tanto la capacidad de modelar problemas con contexto temporal. Las redes echo-state simplifican enormemente el diseño y entrenamiento de las redes recurrentes. En esta tesis exploramos el uso de redes echo-state y su aplicación en problemas de clasificación y detección de patrones en señales EEG. En un primer estudio demostramos que son capaces de detectar cambios de sincronización generalizada entre dos series temporales caóticas. En un segundo utilizamos redes echo-state para caracterizar la no estacionaridad de un fenómeno considerado de estado estable, potenciales visuales evocados steady-sate (SSVEP). Finalmente en un tercer estudio proponemos un nuevo biomarcardor para TDAH capaz de cuantificar cambios en la dinámica de la señal EEG entre condiciones bajas y normales de excitación. Los resultados aquí presentados demuestran la excelente capacidad de detección de patrones no estacionarios de estas redes, así como su aplicabilidad en el análisis de datos electrofisiológicos.
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Sie, Jyun-jie, and 謝竣傑. "Implementation of a high-performance steady-state visual evoked potential (SSVEP)-based brain computer interface using frequency and phase encoded flash lights." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/35804818538671355209.

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碩士
國立中央大學
電機工程研究所
95
The present study proposes a new visual evoked potential (VEP)-based brain computer interface (BCI). Users gaze at different spatially separated flash channels (FCs) in order to induce visual evoked signals that have temporal sequences corresponding to the gazed FCs, so that the gazed FC can be recognized and the command mapping to the gazed FC can be sent out to achieve control purposes. To achieve distinct flickering sequences among different FCs, we utilized different frequencies and phases to encode the flashing sequences of different FCs. The proposed system provides the high flexibility in expansion of FC number and high information transfer rate (ITR) which are superior to the traditional SSVEP-based and FVEP-based BCIs. In this thesis, we have built an eight-FC system. The command transfer rate and detected accuracy are 0.52 sec/command and 100%, respectively.
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Books on the topic "Steady-State Visually Evoked Potential (SSVEP)"

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Prasad, Girijesh. Brain–machine interfaces. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780199674923.003.0049.

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A brain–machine interface (BMI) is a biohybrid system intended as an alternative communication channel for people suffering from severe motor impairments. A BMI can involve either invasively implanted electrodes or non-invasive imaging systems. The focus in this chapter is on non-invasive approaches; EEG-based BMI is the most widely investigated. Event-related de-synchronization/ synchronization (ERD/ERS) of sensorimotor rhythms (SMRs), P300, and steady-state visual evoked potential (SSVEP) are the three main cortical activation patterns used for designing an EEG-based BMI. A BMI involves multiple stages: brain data acquisition, pre-processing, feature extraction, and feature classification, along with a device to communicate or control with or without neurofeedback. Despite extensive research worldwide, there are still several challenges to be overcome in making BMI practical for daily use. One such is to account for non-stationary brainwaves dynamics. Also, some people may initially find it difficult to establish a reliable BMI with sufficient accuracy. BMI research, however, is progressing in two broad areas: replacing neuromuscular pathways and neurorehabilitation.
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Book chapters on the topic "Steady-State Visually Evoked Potential (SSVEP)"

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Xu, Zhuo, Jie Li, Rong Gu, and Bin Xia. "Steady-State Visually Evoked Potential (SSVEP)-Based Brain-Computer Interface (BCI): A Low-Delayed Asynchronous Wheelchair Control System." In Neural Information Processing, 305–14. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-34475-6_37.

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Kubacki, Arkadiusz, and Andrzej Milecki. "Control of the 6-Axis Robot Using a Brain-Computer Interface Based on Steady State Visually Evoked Potential (SSVEP)." In Lecture Notes in Mechanical Engineering, 213–22. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-18715-6_18.

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Segers, H., A. Combaz, N. V. Manyakov, N. Chumerin, K. Vanderperren, S. Van Huffel, and M. M. Van Hulle. "Steady State Visual Evoked Potential (SSVEP) - Based Brain Spelling System with Synchronous and Asynchronous Typing Modes." In IFMBE Proceedings, 164–67. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21683-1_41.

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Sayilgan, Ebru, Yilmaz Kemal Yuce, and Yalcin Isler. "Evaluating Steady-State Visually Evoked Potentials-Based Brain-Computer Interface System Using Wavelet Features and Various Machine Learning Methods." In Artificial Intelligence. IntechOpen, 2021. http://dx.doi.org/10.5772/intechopen.98335.

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Steady-state visual evoked potentials (SSVEPs) have been designated to be appropriate and are in use in many areas such as clinical neuroscience, cognitive science, and engineering. SSVEPs have become popular recently, due to their advantages including high bit rate, simple system structure and short training time. To design SSVEP-based BCI system, signal processing methods appropriate to the signal structure should be applied. One of the most appropriate signal processing methods of these non-stationary signals is the Wavelet Transform. In this study, we investigated both the effect of choosing a mother wavelet function and the most successful combination of classifier algorithm, wavelet features, and frequency pairs assigned to BCI commands. SSVEP signals that were recorded at seven different stimulus frequencies (6–6.5 – 7 – 7.5 – 8.2 – 9.3 – 10 Hz) were used in this study. A total of 115 features were extracted from time, frequency, and time-frequency domains. These features were classified by a total of seven different classification processes. Classification evaluation was presented with the 5-fold cross-validation method and accuracy values. According to the results, (I) the most successful wavelet function was Haar wavelet, (II) the most successful classifier was Ensemble Learning, (III) using the feature vector consisting of energy, entropy, and variance features yielded higher accuracy than using one of these features alone, and (IV) the highest performances were obtained in the frequency pairs with “6–10”, “6.5–10”, “7–10”, and “7.5–10” Hz.
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Verma, Akshat, Praveen Kumar Shukla, Shrish Verma, and Rahul Kumar Chaurasiya. "A Frequency Discrimination Technique for SSVEP-Based BCIs Using Common Feature Analysis and Support Vector Machine." In Advances in Bioinformatics and Biomedical Engineering, 158–78. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-6684-3947-0.ch009.

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BCI is a communication option that has come up as a very radical tool for those who are suffering from neuromuscular disorders. BCI provide a way for the brain to communicate with the outer world without the use of any outlying nerves. Steady state visually evoked potentials (SSVEP) are frequency-specific responses to visual stimuli. These are extensively used with EEG signals. This research projects an innovative method for recognition of SSVEP-based BCIs. The method establishes a processing pipeline where an IIR Butterworth filter is implemented which filters the signals that are further decomposed into waveforms also known as wavelets. Along with the wavelet decomposition, common feature analysis (CFA), canonical correlation analysis (CCA), and MCCA are used to extract features. The best result is obtained from DWT-CFA. The finest classification results are obtained from the RBF kernel-based SVM classifier. The best overall mean accuracy of 94.78% is obtained using DWT-CFA as the feature extraction technique and employing RBF kernel-based SVM as the classifier.
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Raghuvanshi, Ankita, Mohit Sarin, Praveen Kumar Shukla, Shrish Verma, and Rahul Kumar Chaurasiya. "A Comprehensive Review on a Brain Simulation Tool and Its Applications." In Advances in Bioinformatics and Biomedical Engineering, 26–51. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-6684-3947-0.ch002.

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Brain-computer interface, widely known as BCI, is a relatively new field of research that has emerged as promising field research in the last few decades. It is defined as a combination of software as well as hardware that give us the tool to control external devices by using our brain signals as commands. In this chapter, the authors discuss the various tools that can be used to analyze and perform different functions on the brain signals, create BCI models, simulations, etc. In this study, they compare the tools and tabulate how they might be useful for the user's requirements. Additionally, they have implemented the use of tools for real-time applications. The experimental analysis presented in this work utilizes MAMEM EEG steady-state visually evoked potential (SSVEP) dataset I. Five different frequencies (6.66, 7.50, 8.57, 10.00, and 12.00 Hz) were used for the visual stimulation. The authors have analyzed different parameters like power spectrum density, power spectrum, and inter-trial coherence (ITC) through EEGLAB.
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Conference papers on the topic "Steady-State Visually Evoked Potential (SSVEP)"

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Anil, Divya Geethakumari, Krupal Sureshbhai Mistry, Vaibhav Palande, and Kiran George. "A Novel Steady-State Visually Evoked Potential (SSVEP) Based Brain Computer Interface Paradigm for Disabled Individuals." In 2017 IEEE International Conference on Healthcare Informatics (ICHI). IEEE, 2017. http://dx.doi.org/10.1109/ichi.2017.19.

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Aljshamee, Mustafa, Abbas Malekpour, and Peter Luksch. "Multiple Frequency Effects on Human-Brain Based Steady-State Visual Evoked Potential (SSVEP)." In 2016 IEEE 6th International Conference on Advanced Computing (IACC). IEEE, 2016. http://dx.doi.org/10.1109/iacc.2016.139.

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Mu, Jing, David B. Grayden, Ying Tan, and Denny Oetomo. "Comparison of Steady-State Visual Evoked Potential (SSVEP) with LCD vs. LED Stimulation." In 2020 42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) in conjunction with the 43rd Annual Conference of the Canadian Medical and Biological Engineering Society. IEEE, 2020. http://dx.doi.org/10.1109/embc44109.2020.9175838.

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Hyunmi Lim and Jeonghun Ku. "Mirror neuron system (MNS) activation and steady state visually evoked potential (SSVEP) evocation by flickering exercise video." In 2017 International Conference on Virtual Rehabilitation (ICVR). IEEE, 2017. http://dx.doi.org/10.1109/icvr.2017.8007473.

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Longbotham, Harold G., and Randolph D. Glickman. "Analysis of Visual Evoked Potential Using Generalized Order Statistic Filters." In Noninvasive Assessment of the Visual System. Washington, D.C.: Optica Publishing Group, 1991. http://dx.doi.org/10.1364/navs.1991.md20.

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Electrophysiology provides a means for obtaining an objective measure of neural function, as well as an indicator of nervous system pathology. In the case of the visual system, noninvasive electrophysiology commonly utilizes recording the potentials evoked by visual stimuli from various points along the optic tract. Although the flash electroretinogram may be well over 150 microvolts in amplitude, the steady-state visual evoked potential (ssVEP), elicited from the visual cortex by counterphased, patterned stimuli, is usually much lower--on the order of 1 to 10 microvolts. In the clinic, the response recorded from patients may be of even lower amplitude due to the underlying disease process. In addition, the pathology may change the nature of the ambient noise, as well as making the patient less amenable to the recording process. All of these factors interfere with the use of the ssVEP in electrodiagnosis. Clearly, techniques which improve the analysis of evoked potential data will be welcomed by the clinical electrophysiologist. We have found that the generalized order statistic (GOS) filters can be used to analyze ssVEP data, reducing the length of recording time required to acquire the signal, and improving the signal to noise ratio by removing transient and impulsive noise from the biological data. The principles and practice for the use of generalized order statistic filters will be outlined below.
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Hwang, Han-Jeong, Jeong-Hwan Lim, Jun-Hak Lee, and Chang-Hwan Im. "Implementation of a mental spelling system based on steady-state visual evoked potential (SSVEP)." In 2013 International Winter Workshop on Brain-Computer Interface (BCI). IEEE, 2013. http://dx.doi.org/10.1109/iww-bci.2013.6506638.

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Kubacki, Arkadiusz, Arkadiusz Jakubowski, and Dominik Rybarczyk. "Research on possibilities of transporter movement using brain-computer interface based on Steady-State Visually Evoked Potential (SSVEP)." In 2017 22nd International Conference on Methods and Models in Automation and Robotics (MMAR). IEEE, 2017. http://dx.doi.org/10.1109/mmar.2017.8046827.

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Leow, R. S., F. Ibrahim, and M. Moghavvemi. "Development of a steady state visual evoked potential (SSVEP)-based brain computer interface (BCI) system." In 2007 International Conference on Intelligent and Advanced Systems (ICIAS). IEEE, 2007. http://dx.doi.org/10.1109/icias.2007.4658399.

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Wang, Shaocheng, Ehsan Tarkesh Esfahani, and V. Sundararajan. "Evaluation of SSVEP as Passive Feedback for Improving the Performance of Brain Machine Interfaces." In ASME 2012 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/detc2012-71068.

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Research in brain-computer interfaces have focused primarily on motor imagery tasks such as those involving movement of a cursor or other objects on a computer screen. In such applications, it is important to detect when the user is interested in moving an object and when the user is not active in this task. This paper evaluates the steady state visual evoked potential (SSVEP) as a feedback mechanism to confirm the mental state of the user during motor imagery. These potentials are evoked when a subject looks at a flashing objects of interest. Four different experiments are conducted in this paper. Subjects are asked to imagine the movement of flashing object in a given direction. If the subject is involved in this task, the SSVEP signal will be detectable in the visual cortex and therefore the motor imagery task is confirmed. During the experiment, EEG signal is recorded at 4 locations near visual cortex. Using a weighting scheme, the best combination of the recorded signal is selected to evaluate the presence of flashing frequency. The experimental result shows that the SSVEP can be detected even in complex motor imagery of flickering objects. The detection rate of 85% is achieved while the refreshing time for SSVEP feedback is set to 0.5 seconds.
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Giuliani, Henrique L. V., Patrick O. de Paula, Diogo C. Soriano, Ricardo Suyama, and Denis G. Fantinato. "Ensemble Learning in BCI-SSVEP Systems for Short Window Lengths." In Escola Regional de Computação Aplicada à Saúde. Sociedade Brasileira de Computação - SBC, 2021. http://dx.doi.org/10.5753/ercas.2021.17438.

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Different approaches have been investigated to implement effective Brain-Computer Interfaces (BCI), translating brain activation patterns into commands to external devices. BCI exploring Steady-State Visually Evoked Potentials usually achieve relatively high accuracy, when considering 2-3 second sample windows, but the performance degrades for smaller windows. So, we investigate the use of an ensemble method, the Adaboost algorithm, combining two different structures, the Logistic Regressor and the Multilayer Perceptron, whose diversity shall bring relevant information for more accurate classification. Simulation results indicate that the proposed method can improve performance for smaller windows, being a promising alternative to reduce model variance.
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