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![The standard VEP waveform in response to flash stimuli [17].](profile/Reza-Boostani-2/publication/288191452/figure/fig2/AS:353738247163906@1461349178275/The-standard-VEP-waveform-in-response-to-flash-stimuli-17.png)
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![Fig. 2. The standard VEP waveform in response to flash stimuli [17].](profile/Reza-Boostani-2/publication/288191452/figure/fig2/AS:353738247163906@1461349178275/The-standard-VEP-waveform-in-response-to-flash-stimuli-17_Q320.jpg)
Although several studies have been conducted toward quantitative measuring depth of anesthesia (DOA), the state of art DOA indexes sometimes fail in practice. Hence, specialists are looking to find a new source of information, rather than modifying the former indexes, to introduce an accurate DOA index. In this regard, here, a new horizon to this f...
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Context 1
... to the ISCEV standard for VEPs [17], the visual evoked potential in response to flash stimulation consists of a series of neg- ative and positive components. The earliest detectable response has a peak latency of approximately 30 ms post-stimulus and com- ponents are recordable with peak the latencies up to 300 ms as illustrated in Fig. 2. For the flash VEP, the most robust components are the N2 and P2 ...
Citations
... Second, in case of BCIs, different colors may be used as additional discriminating possibility or as suggested by [10], the performance in terms of VEP detection can be increased by selecting the best suited color. Third, studies have revealed that anesthesia influences latency and the peak-to-peak amplitude of the induced VEPs [6,18]. In that context, a further scientific question would be whether the ability to discriminate colors is influenced by anesthesia or even vanishes at a specific state of anesthesia. ...
Visually evoked potentials (VEPs) are widely used for diagnoses of different neurological diseases. Interestingly, there is limited research about the impact of the stimulus color onto the evoked response. Therefore, in our study we investigated the possibility of automatically classifying the stimulus color. The visual stimuli were selected to be red/black and green/black checkerboard patterns with equal light density. Both of these stimuli were presented in a random manner to nine subjects, while the electroencephalogram was recorded at the occipital lobe. After pre-processing and aligning the evoked potentials, an artificial neural network with one hidden layer was used to investigate the general possibility to automatically classify the stimulus color in three different settings. First, color classification with individually trained models, color classification with a common model, and color classification for each individual volunteer with a model trained on the data of the remaining subjects. With an average accuracy (ACC) of 0.83, the best results were achieved for the individually trained model. Also, the second (mean ACC = 0.76) and third experiments (mean ACC = 0.71) indicated a reasonable predictive accuracy across all subjects. Consequently, machine learning tools are able to appropriately classify stimuli colors based on VEPs. Although further studies are needed to improve the classification performance of our approach, this opens new fields of applications for VEPs.
... In order to validate the results, ten times ten-folds cross validation scheme was adopted (Nazhvani, Boostani, Afrasiabi, & Sadatnezhad, 2013). Classification performance was evaluated by the confusion matrix, containing the following criterion (Afrasiabi, Boostani, Zand, & Razavipour, 2012;Van Stralen, et al., 2009): ...
Past research emphasized on revealing the pain in different bands of electroencephalogram (EEG) including alpha band. In this study, we proposed an accurate and robust manner to differentiate pain intensities by deeply characterizing the alpha band in terms of distribution, spectrum and complexity changes in response to five different intensities of pain. Here, 44 subjects executed the Cold Pressor Task (CPT) and experienced five defined levels of pain while their EEGs were recorded via 34 silver channels. After de-noising and filtering the EEGs through the alpha band, 12 informative features were extracted from each channel in successive time frames. Since none of the features could discriminate the five classes, we applied the Kruskal-Wallis test to the features for observing their distribution in differentiating two or more classes. According to this result, we designed a decision tree classifier, where a Bayes optimized support vector machine (BSVM) was selected in each decision node. Sequential forward selection was applied in order to customize a subset of features for each BSVM. Our results provided 93.33% accuracy over the five classes and also generate 99.8% accuracy for separating pain and no-pain classes, which is statistically superior (P<0.05) to state-of-the-art methods over our collected dataset.
... The skewness for a normal distribution is zero, and to some extent, any symmetric data should have a skewness near zero. Negative and positive values for the skewness indicate data that are skewed to the left or right side, respectively [24]. ...
... VEPs are the alterations of the ongoing EEG due to stimulation (e.g. light flash) [10] Usual VEP waveforms characterized by amplitude and latency of the three main peaks including a negative deflection 75 ms (milliseconds) after the stimulus (N75) followed by a positive deflection at 100 ms (P100) followed by another negative peak at 145 ms (N145) [10]. These features also called P100 attributes provide unique time based information about the brain function (especially about visual pathway from the retina to the occipital cortex) [10]. ...
... VEPs are the alterations of the ongoing EEG due to stimulation (e.g. light flash) [10] Usual VEP waveforms characterized by amplitude and latency of the three main peaks including a negative deflection 75 ms (milliseconds) after the stimulus (N75) followed by a positive deflection at 100 ms (P100) followed by another negative peak at 145 ms (N145) [10]. These features also called P100 attributes provide unique time based information about the brain function (especially about visual pathway from the retina to the occipital cortex) [10]. ...
... light flash) [10] Usual VEP waveforms characterized by amplitude and latency of the three main peaks including a negative deflection 75 ms (milliseconds) after the stimulus (N75) followed by a positive deflection at 100 ms (P100) followed by another negative peak at 145 ms (N145) [10]. These features also called P100 attributes provide unique time based information about the brain function (especially about visual pathway from the retina to the occipital cortex) [10]. In another study, Vialette et al. [11] have shown that the steady state visual evoked potential (SSVEP) can be useful and informative in many cognitive and clinical neuroscience disorders such as schizophrenia, anxiety, stress and epilepsy. ...
... VEPs are the alterations of the ongoing EEG due to stimulation (e.g. light flash) [10] Usual VEP waveforms characterized by amplitude and latency of the three main peaks including a negative deflection 75 ms (milliseconds) after the stimulus (N75) followed by a positive deflection at 100 ms (P100) followed by another negative peak at 145 ms (N145) [10]. These features also called P100 attributes provide unique time based information about the brain function (especially about visual pathway from the retina to the occipital cortex) [10]. ...
... VEPs are the alterations of the ongoing EEG due to stimulation (e.g. light flash) [10] Usual VEP waveforms characterized by amplitude and latency of the three main peaks including a negative deflection 75 ms (milliseconds) after the stimulus (N75) followed by a positive deflection at 100 ms (P100) followed by another negative peak at 145 ms (N145) [10]. These features also called P100 attributes provide unique time based information about the brain function (especially about visual pathway from the retina to the occipital cortex) [10]. ...
... light flash) [10] Usual VEP waveforms characterized by amplitude and latency of the three main peaks including a negative deflection 75 ms (milliseconds) after the stimulus (N75) followed by a positive deflection at 100 ms (P100) followed by another negative peak at 145 ms (N145) [10]. These features also called P100 attributes provide unique time based information about the brain function (especially about visual pathway from the retina to the occipital cortex) [10]. In another study, Vialette et al. [11] have shown that the steady state visual evoked potential (SSVEP) can be useful and informative in many cognitive and clinical neuroscience disorders such as schizophrenia, anxiety, stress and epilepsy. ...
