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Review of the Human Brain and EEG Signals
Published in Teodiano Freire Bastos-Filho, Introduction to Non-Invasive EEG-Based Brain–Computer Interfaces for Assistive Technologies, 2020
Alessandro Botti Benevides, Alan Silva da Paz Floriano, Mario Sarcinelli-Filho, Teodiano Freire Bastos-Filho
Thus, when a subject gazes at one of the stimuli, an SSVEP is evoked in their brain [22,60,61], which can be detected in the EEG signal. These measurements can then be used as control commands to the BCI with precision [48]. Figure 1.39 shows a diagram of an SSVEP-based BCI.
Eye Gaze Collaboration with Brain–Computer Interfaces
Published in Chang S. Nam, Anton Nijholt, Fabien Lotte, Brain–Computer Interfaces Handbook, 2018
Gaye Lightbody, Chris P. Brennan, Paul J. McCullagh, Leo Galway
The visual evoked potential (VEP) can be detected in the EEG in response to external visual stimuli. The stimuli can be pattern reversal (e.g., a checkerboard on computer screen), flashing icons on a computer screen, or externally modulated flashing lights, usually LEDs (Zhu et al. 2010). The VEP component is prominent in the visual region of the occipital cortex (Regan 1988). If the visual stimulus is presented at a rate greater than 6 Hz, an oscillatory response is evoked. This response is termed steady-state visual evoked potential (SSVEP). If users pay attention to one or more stimuli that oscillate between 6 and 50 Hz, corresponding frequencies may be measured over visual areas of the brain. Users can thus communicate by focusing on one stimulus while ignoring others. Different frequencies elicit different SSVEP activity across different subjects (Gao et al. 2003; Kelly et al. 2005) Allison et al. (2008) showed that SSVEP sufficient for BCI control may be elicited by selective attention to one of two overlapping stimuli. Thus, some SSVEP-based BCI approaches may not depend on gaze control and could function in severely disabled users (Allison et al. 2010; Pfurtscheller et al. 2010b; Volosyak et al. 2011).
An update on EEG in migraine
Published in Expert Review of Neurotherapeutics, 2019
Both migraine with and without aura present with this SSVEP pattern; however, Nyrke et al [46] described some differences between the two migraine phenotypes. Shibata et al [47] used a pattern reversal stimulus paradigm, confirmed the subtle differences between the two types of migraine in visual stimuli processing, because migraine with aura exhibited an increased amplitude in the fourth harmonic at 10 Hz and high contrast, compared with migraine without aura and controls (Table 2). This different functions of the occipital cortex may predispose to aura symptom perception. Recent studies investigating basal EEG rhythm perturbation related to the same modality of stimulation have highlighted the functional differences between the two types of migraine [48]. An example of EEG spectral analysis under checkerboard pattern stimulation at 5 Hz temporal and 2 cpd spatial frequency is presented in Figure 1.
A novel method for the detection of VEP signals from frontal region
Published in International Journal of Neuroscience, 2018
Chih-Tsung Chang, Christina Huang
These brain signals are collected from the scalp overlying the visual cortex as EEG signals, which is an electric potential triggered by visual stimuli. Among all the common signals used in EEG-based BCI, flash visual evoked potential (FVEP) and the steady-state VEP (SSVEP) are most widely used due to many advantages such as easy training and high information transfer rate [8]. VEP is an evoked response, which follows the pathway from the retina via the optic nerves to the visual cortex of the brain, in response to light stimuli. VEP spectra are obtained from the electroencephalogram by signal averaging [9].
Investigating the effects of stimulus duration and inter-stimulus interval parameters on P300 based BCI application performance
Published in Computer Methods in Biomechanics and Biomedical Engineering, 2022
Onder Aydemir, Kubra Saka, Mehmet Ozturk
One of the fundamental aims of EEG based BCI research is to develop the performance of the system in terms of the decision-making speed (Aydemir 2016; Torres and Daly 2021). Stimulus duration (SD), inter-stimulus interval (ISI) and stimulus onset asynchrony (SOA, which indicates the duration of SD + ISI) are parameters which directly influence the decision-making speed of the BCI system. Traditionally, P300 and steady-state visual evoked potential (SSVEP) based studies used constant SD, ISI and SOA values (Krusienski et al. 2008; Yin et al. 2013, 2014). In literature, there are a few studies which have investigated the effects of these parameters on the success of P300 based studies. Allison and Pineda examined only three different SOAs (125 ms, 250 ms, and 500 ms) on classification accuracy (CA) rates and found longer SOA provided higher CA (Allison and Pineda 2006). Like the previous study, Lee et al. also explored the effect of SOA for 150 ms, 300 ms, and 500 ms. Their results showed that the amplitude of P300 waves decreased when the SOA decreased from 500 ms to 150 ms. Although such kind of studies investigated the effect of SOA, they did not investigate it detail for the impact of other stimulus timing parameters such as SD and ISI (Lee et al. 2016). On contrary to these studies, Bulanov et al. used longer SD and ISI, which were 500 ms, and 1000 ms, respectively (Bulanov et al. 2020). On the other hand, there are also some work in which researchers examined the effect of various SD and ISI values on the performance of P300 approach. In such a study, Lu et al. examined the impacts of distinct stimulus presentation parameters including, SD, ISI, and SOA on the CA and performance of the P300 speller application (Lu et al. 2013). They recorded EEG signals from six males in a single two-hour session with a 32-electrode cap. While every row and column were flashed 12 times per trial, they conducted 15 trials per letter selection. They concluded that while the CA and speed of the P300 speller increased as ISI and SOA increased, the SD was minimally affected to the performance. It should be note that they proposed optimum ISI and SOA values as 93.75 ms and 125 ms, respectively. In another P300 based approach, Belhaouari et al. collected the EEG data from five subjects with an 8-electrode cap (Belhaouari et al. 2015). They compared the performance of the BCI application with a constant SD (50 ms) and two distinct ISIs (100 ms and 200 ms). The best CA performance was obtained as 73.33% by 100 ms ISI value. Moreover, there are conflicting results in literature regarding the effect of timing parameters on P300 accuracy. Additionally, studies vary in terms of experimental methodology, outcomes and limited kind of approach which makes it difficult to draw any firm conclusions. Hence, there is a need to further investigate the effects of these parameters on P300 performance.