A novel method for the determination of the EEG individual alpha frequency

Goljahani, Anahita; D’Avanzo, Costanza; Schiff, Sami; Amodio, Piero; Bisiacchi, Patrizia; Sparacino, Giovanni

doi:10.1016/j.neuroimage.2011.12.001

Cited by 48 publications

(41 citation statements)

References 41 publications

(59 reference statements)

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“…Fink et al (2005) found a link between decreased alpha and increased mental effort which was similar to the finding of a negative correlation between EEG relative alpha power and blood flow velocity during cognitive effort by Szirmai et al (2005). In this paper, we use EEG high beta band to measure mental effort because EEG beta is usually associated with active thinking (Dietrich and Kanso 2010) and high beta from 20 to 30 Hz is chosen to avoid individual alpha frequency which may extend beyond 13 Hz (Goljahani et al 2012).…”

Section: Electroencephalography (Eeg) and Cognitive Effortsupporting

confidence: 52%

Effects of stress and effort on self-rated reports in experimental study of design activities

Nguyen

Zeng

2016

J Intell Manuf

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This paper investigates the relationships between subjective rating measure and physiological measure of mental stress and mental effort during design activities. Mental stress and mental effort were captured by skin conductance and EEG beta2 power (20-30 Hz) whereas self-rated mental stress and self-rated mental effort were obtained using the NASA Task Load Index. A strong association between selfrated effort and EEG beta2 power was found in several design tasks. The analysis shows that self-rating is by itself a mental activity which may be affected by psychological stress and may be influenced by the amount of cognitive effort allocated. Researchers who rely on subjective rating should take into account the stress and effort of respondents during the rating activities to ensure the validity of the self-report measures. The study also demonstrated that design tasks induced mental stress that continued to stay above the baseline even after the tasks were completed.

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Section: Electroencephalography (Eeg) and Cognitive Effortsupporting

confidence: 52%

Effects of stress and effort on self-rated reports in experimental study of design activities

Nguyen

Zeng

2016

J Intell Manuf

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“…This calls for alpha activity models that would take personalized alpha features into account, along with modeling the interplay of alpha activity with activity of other frequency bands. Using models that represent better alpha activity, i.e., not as an ideal bell shaped curve, might offer better estimation [20].…”

Section: Discussionmentioning

confidence: 99%

The need for adequate EEG modeling and personalization in daily life cognitive engagement monitoring

Mihajlović

Patki

Grundlehner

2015

2015 7th International IEEE/EMBS Conference on Neural Engineering (NER)

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Monitoring cognitive engagement during daily activities can offer new insights in understanding our brain at work. It requires the usage of a convenient electroencephalograph (EEG) system and an adequate model that can characterize cognitive states and state changes reflected in the electrical activity of the brain. This paper studies EEG during cognitive relaxation and cognitive engagement in eyes open and eyes closed conditions. It explores the effects cognitive states have on alpha brain activity captured by different alpha activity models. The analysis revealed that models that characterize not only the spectral power but also the alpha peak frequency and the width and the power of the alpha peak provide better insights into the cognitive engagement of a person. Furthermore, personal differences are observed in the alpha peak parameters with respect to different cognitive states. We argue that using alpha peak modeling, instead of traditionally used power in the fixed alpha frequency band is required for proper characterization of human brain activity in cognitive studies.

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“…The stimuli were presented on a 19″ flat screen with a distance from the subject varying from 50–60 cm. Before the exposure to the experimental stimuli, participants were asked to look at a black screen for 60 s, and the EEG activity recorded in correspondence of this open eyes condition was then used for the IAF calculation (Goljahani et al, 2012). …”

Section: Methodsmentioning

confidence: 99%

“…Then, the Independent Component Analysis (ICA) has been applied to EEG data in order to identify and remove other artifacts-related components, such as blinks and eye movements, since their contribution is overlapped to the EEG bands of interest in the present study (Di Flumeri et al, 2016a). In order to take into account any subjective difference in terms of brain rhythms, for each subject the Individual Alpha Frequency (IAF) was computed on the 60-s-long Open Eyes segment (Goljahani et al, 2012), recorded at the beginning of the experimental task, in order to define the EEG bands of interest according to the method suggested in the current scientific literature, i.e., each band is defined as “IAF ± x,” where IAF is the Individual Alpha Frequency, in Hertz, and × an integer in the frequency domain (Klimesch, 1999). Thus, the EEG activity was divided, by filtering the EEG signals in the time-domain, in two main frequency bands: theta [IAF-6 ÷ IAF-2 Hz] and alpha [IAF-2 ÷ IAF+2 Hz].…”

Section: Methodsmentioning

confidence: 99%

Neurophysiological Measures of the Perception of Antismoking Public Service Announcements Among Young Population

Cartocci

Modica

Rossi

et al. 2018

Front. Hum. Neurosci.

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Tobacco constitutes a global emergency with totally preventable millions of deaths per year and smoking-related illnesses. Public service announcements (PSAs) are the main tool against smoking and by now their efficacy is still assessed through questionnaires and metrics, only months after their circulation. The present study focused on the young population, because at higher risk of developing tobacco addiction, investigating the reaction to the vision of Effective, Ineffective and Awarded antismoking PSAs through: electroencephalography (EEG), autonomic activity variation (Galvanic skin response—GSR- and Heart Rate—HR-) and Eye-Tracking (ET). The employed indices were: the EEG frontal alpha band asymmetry and the frontal theta; the Emotional Index (EI), deriving from the GSR and HR signals matching; the ET Visual Attention (VA) index, based on the ratio between the total time spent fixating an area of interest (AOI) and its area. Smokers expressed higher frontal alpha asymmetry values in comparison to non-smokers. Concerning frontal theta, Awarded PSAs reported the highest values in comparison to both Effective and Ineffective PSAs. EI results highlighted that lowest values were expressed by Heavy Smokers (HS), and Effective PSAs obtained the highest EI values. Finally, concerning the Effective PSAs, regression analysis highlighted a correlation between the number of cigarettes smoked by participants (independent variable) and frontal alpha asymmetry, frontal theta and EI values. ET results suggested that for the Ineffective PSAs the main focus were texts, while for the Effective and Awarded PSAs were the visual elements. Results support the use of methods aimed at assessing the physiological reaction for the evaluation of PSAs images, in particular when considering the smoking habits of target populations.

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A novel method for the determination of the EEG individual alpha frequency

Cited by 48 publications

References 41 publications

Effects of stress and effort on self-rated reports in experimental study of design activities

Effects of stress and effort on self-rated reports in experimental study of design activities

The need for adequate EEG modeling and personalization in daily life cognitive engagement monitoring

Neurophysiological Measures of the Perception of Antismoking Public Service Announcements Among Young Population

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