Human implicit intent recognition based on the phase synchrony of EEG signals

Kang, Jun-Su; Park, Ukeob; Gonuguntla, Venkateswarlu; Veluvolu, Kalyana C.; Lee, Minho

doi:10.1016/j.patrec.2015.06.013

Cited by 28 publications

(14 citation statements)

References 20 publications

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“…EEG phase differences are often used to investigate “inter-sites phase synchronization”, which is an estimation of the connectivity between two EEG electrode sites (van Mierlo et al, 2014 ; Kang et al, 2015 ; Tokariev et al, 2016 ). Phase locking value (PLV; Lachaux et al, 1999 ) is a possible means to represent the synchronization phenomena in EEG signals (Lachaux et al, 1999 ).…”

Section: Methodsmentioning

confidence: 99%

Theta and Alpha Oscillations during the Retention Period of Working Memory by rTMS Stimulating the Parietal Lobe

Jin

Wang

et al. 2017

Front. Behav. Neurosci.

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Studies on repetitive transcranial magnetic stimulation (rTMS) have shown that stimulating the parietal lobe, which plays a role in memory storage, can enhance performance during the “retention” process of working memory (WM). However, the mechanism of rTMS effect during this phase is still unclear. In this study, we stimulated the superior parietal lobe (SPL) using 5-Hz rTMS in 26 participants and recorded electroencephalography (EEG) while they performed a delayed-recognition WM task. The analyses included the comparisons of event-related spectral perturbation (ERSP) value variations in theta (4–7 Hz) and alpha (8–14 Hz) band frequencies between conditions (rTMS vs. sham), as well as the correlations between different brain areas. Following rTMS, the ERSP values of theta-band oscillations were significantly increased in the parietal and occipital-parietal brain areas (P < 0.05*), whereas the ERSP values of alpha-band oscillations were significantly decreased in the parietal area (P < 0.05*). The ERSP value variations of theta-band oscillations between the two conditions in the left parietal and left prefrontal areas were positively correlated with the response time (RT) variations (by using rTMS, the more subject RT decreased, the more ERSP value of theta oscillation increased). The ERSP value variations of alpha-band oscillations in the left parietal and bilateral prefrontal areas were negatively correlated with RT variations (by using rTMS, the more RT of the subject decreased, the more ERSP value of alpha oscillation decreased). Inter-sites phase synchronization of theta-band EEG between the left parietal and left prefrontal areas, as well as alpha-band EEG between the left parietal and bilateral prefrontal areas were enhanced by rTMS. These results indicated that activities of both parietal and prefrontal areas were required for information storage, and these activities were related to the behavioral responses. Moreover, the connectivity between these two regions was intensified following rTMS. Thus, rTMS may affect the frontal area indirectly via the frontal parietal pathway.

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Section: Methodsmentioning

confidence: 99%

Theta and Alpha Oscillations during the Retention Period of Working Memory by rTMS Stimulating the Parietal Lobe

Jin

Wang

et al. 2017

Front. Behav. Neurosci.

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“…In this study, interactions between brain regions are investigated through phase locking value for positive and negative emotions as it is demonstrated that this metric is successful in inferring the functional connectivity between brain regions using EEG signals (Dimitriadis et al 2015;Kang et al 2015;Sakkalis 2011;Sun et al 2012). For this purpose, we constructed a multimodal emotional database from 25 voluntary subjects using 15 stimuli.…”

Section: Introductionmentioning

confidence: 99%

Analysis of functional brain connections for positive–negative emotions using phase locking value

Daşdemir

Yıldırım

2017

Cogn Neurodyn

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In this study, we investigate the brain networks during positive and negative emotions for different types of stimulus (audio only, video only and audio + video) in [Formula: see text], and [Formula: see text] bands in terms of phase locking value, a nonlinear method to study functional connectivity. Results show notable hemispheric lateralization as phase synchronization values between channels are significant and high in right hemisphere for all emotions. Left frontal electrodes are also found to have control over emotion in terms of functional connectivity. Besides significant inter-hemisphere phase locking values are observed between left and right frontal regions, specifically between left anterior frontal and right mid-frontal, inferior-frontal and anterior frontal regions; and also between left and right mid frontal regions. ANOVA analysis for stimulus types show that stimulus types are not separable for emotions having high valence. PLV values are significantly different only for negative emotions or neutral emotions between audio only/video only and audio only/audio + video stimuli. Finding no significant difference between video only and audio + video stimuli is interesting and might be interpreted as that video content is the most effective part of a stimulus.

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“…Distinguishing different types of intentions by using biosignals was investigated in a study by Kang et al (2015) [28]. The authors classified viewing a picture with a simple navigational purpose and searching for an object within the picture with the intention of searching for information.…”

Section: Intention Detection Based On the Classification Of Eeg Signalsmentioning

confidence: 99%

An Investigation of Insider Threat Mitigation Based on EEG Signal Classification

Kim

Yim

2020

Sensors

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This study proposes a scheme to identify insider threats in nuclear facilities through the detection of malicious intentions of potential insiders using subject-wise classification. Based on electroencephalography (EEG) signals, a classification model was developed to identify whether a subject has a malicious intention under scenarios of being forced to become an insider threat. The model also distinguishes insider threat scenarios from everyday conflict scenarios. To support model development, 21-channel EEG signals were measured on 25 healthy subjects, and sets of features were extracted from the time, time–frequency, frequency and nonlinear domains. To select the best use of the available features, automatic selection was performed by random-forest-based algorithms. The k-nearest neighbor, support vector machine with radial kernel, naïve Bayes, and multilayer perceptron algorithms were applied for the classification. By using EEG signals obtained while contemplating becoming an insider threat, the subject-wise model identified malicious intentions with 78.57% accuracy. The model also distinguished insider threat scenarios from everyday conflict scenarios with 93.47% accuracy. These findings could be utilized to support the development of insider threat mitigation systems along with existing trustworthiness assessments in the nuclear industry.

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Human implicit intent recognition based on the phase synchrony of EEG signals

Cited by 28 publications

References 20 publications

Theta and Alpha Oscillations during the Retention Period of Working Memory by rTMS Stimulating the Parietal Lobe

Theta and Alpha Oscillations during the Retention Period of Working Memory by rTMS Stimulating the Parietal Lobe

Analysis of functional brain connections for positive–negative emotions using phase locking value

An Investigation of Insider Threat Mitigation Based on EEG Signal Classification

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