Emotion Recognition Positively Correlates with Steady-state Visual Evoked Potential Amplitude and Alpha Entrainment

Deng, Can; Tong, Jiasen; Zhang, Zhiyong; Qin, Yurong

doi:10.1016/j.neuroscience.2020.01.045

Cited by 5 publications

(3 citation statements)

References 56 publications

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“…Using time-frequency analyses, this study replicated previous finding that RVS could induce frequency-tagged SSVEP responses at the stimulation frequency of RVS and its harmonics (Herrmann, 2001). In previous studies, SSVEPs have been widely used for studying dynamic neural processes, such as attention control (Wittenhagen and Mattingley, 2019;Kritzman et al, 2022), working memory (Ellis et al, 2006), emotion recognition (Deng et al, 2020), visual information processing (Hansen et al, 2019;Montani et al, 2019) and visual perception (Chicherov and Herzog, 2015). In their views, the frequency-tagged SSVEP can reflect the dynamic neural processes but not influence them, thus making it a good candidate biomarker of brain functioning.…”

Section: Ssvep Entrainment From the View Of Functional Cerebral Asymm...supporting

confidence: 86%

Modulation of rhythmic visual stimulation on left–right attentional asymmetry

You

et al. 2023

Front. Neurosci.

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The rhythmic visual stimulation (RVS)-induced oscillatory brain responses, namely steady-state visual evoked potentials (SSVEPs), have been widely used as a biomarker in studies of neural processing based on the assumption that they would not affect cognition. However, recent studies have suggested that the generation of SSVEPs might be attributed to neural entrainment and thus could impact brain functions. But their neural and behavioral effects are yet to be explored. No study has reported the SSVEP influence on functional cerebral asymmetry (FCA). We propose a novel lateralized visual discrimination paradigm to test the SSVEP effects on visuospatial selective attention by FCA analyses. Thirty-eight participants covertly shifted their attention to a target triangle appearing in either the lower-left or -right visual field (LVF or RVF), and judged its orientation. Meanwhile, participants were exposed to a series of task-independent RVSs at different frequencies, including 0 (no RVS), 10, 15, and 40-Hz. As a result, it showed that target discrimination accuracy and reaction time (RT) varied significantly across RVS frequency. Furthermore, attentional asymmetries differed for the 40-Hz condition relative to the 10-Hz condition as indexed by enhanced RT bias to the right visual field, and larger Pd EEG component for attentional suppression. Our results demonstrated that RVSs had frequency-specific effects on left–right attentional asymmetries in both behavior and neural activities. These findings provided new insights into the functional role of SSVEP on FCAs.

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Section: Ssvep Entrainment From the View Of Functional Cerebral Asymm...supporting

confidence: 86%

Modulation of rhythmic visual stimulation on left–right attentional asymmetry

You

et al. 2023

Front. Neurosci.

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“…The filter bank analysis is applied to decompose SS-VEP into components of sub-band, therefore the information of independent component present at the component of harmonics will be removed more  ISSN: 2252-8938 effectively [24]. In accordance with the paper [24], the upper and lower cut-off frequencies of − ℎ sumband is set to 90 Hz and × 8 Hz, respectively. Thereafter, − ℎ pass-band ripple filter with zero phase is applied that depends on infinite impulse response (IIR) system.…”

Section: = ( ) ∈ × × ×mentioning

confidence: 99%

“…The emotions are psychophysiological changes in the moods of the person that will emerge by the interactions with the bio-chemical and the environmental effects. There are various on-going researches on the emotion recognition [24]. Some of the researches include the voicerecognition, and the recognition with the mimics.…”

Section: Introductionmentioning

confidence: 99%

Classification of EEG signal using EACA based approach at SSVEP-BCI

Ashwini¹,

Nagaraj

2021

IJ-AI

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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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SSVEP-Based Emotion Recognition for IoT via Multiobjective Neural Architecture Search

Liu

Wang

et al. 2022

IEEE Internet Things J.

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Emotion Recognition Positively Correlates with Steady-state Visual Evoked Potential Amplitude and Alpha Entrainment

Cited by 5 publications

References 56 publications

Modulation of rhythmic visual stimulation on left–right attentional asymmetry

Modulation of rhythmic visual stimulation on left–right attentional asymmetry

Classification of EEG signal using EACA based approach at SSVEP-BCI

SSVEP-Based Emotion Recognition for IoT via Multiobjective Neural Architecture Search

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