EEG frequency PCA in EEG‐ERP dynamics

Barry, Robert J.; Blasio, Frances M. De

doi:10.1111/psyp.13042

Cited by 29 publications

(41 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Nonsignificant relationships between no‐go error rates and ERP components were expected here. For resting state intrinsic EEG, similar f‐PCA outcomes as those identified by Barry and De Blasio () were anticipated. These consisted of a frontocentral delta component, a second factor overlapping the delta/theta band ranges, three posterior alphas, and two parietal‐midline beta components.…”

Section: Introductionsupporting

confidence: 77%

“…Although this finding is comparable to prior studies indicating a direct relationship between P3b amplitude and broad-range (8-13 Hz) alpha (De Blasio & Barry, 2013b;De Blasio et al, 2013) and also subdivided alpha power (Intriligator & Polich, 1995;Polich, 1997), it is the first report involving a resting state f-PCA component. Barry and De Blasio (2018) did not examine resting state EEG components in relation to ERPs, focusing only on the prestimulus EEG components. In their study, distinct P3b effects were found: Prestimulus alpha-1 and alpha-3 were inversely related to P3b amplitude, while alpha-2 directly predicted P3b enhancement.…”

Section: Discussionmentioning

confidence: 99%

“…This method was applied in subsequent studies examining the EEG in antidepressant treatment response (Tenke et al, ) and spirituality (Tenke et al, ). In a recent application of f‐PCA to both resting state and prestimulus task data, Barry and De Blasio () found the varimax rotation suboptimal, when compared with promax rotated factors, due to the uninterpretable negative loadings obtained at some frequencies. Although varimax is preferred for ERP decompositions (as it maintains orthogonality in components), the promax rotation revealed EEG components to be highly correlated and argued that the underlying data were better estimated using this solution.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Using principal components analysis to examine resting state EEG in relation to task performance

2019

Self Cite

View full text Add to dashboard Cite

Brain dynamics research has highlighted the significance of the ongoing EEG in ERP genesis and cognitive functioning. Few studies, however, have assessed the contributions of the intrinsic resting state EEG to these stimulus‐response processes and behavioral outcomes. Principal components analysis (PCA) has increasingly been used to obtain more objective, data‐driven estimates of the EEG and ERPs. PCA was used here to reassess resting state EEG and go/no‐go task ERP data from a previous study (Karamacoska et al., 2017) and the relationships between these measures. Twenty adults had EEG recorded with eyes closed (EC) and eyes open (EO), and as they completed an auditory go/no‐go task. Separate EEG and ERP PCAs were conducted on each resting condition and stimulus type. For each state, seven EEG components were identified within the delta‐beta frequency range, and six ERP components were obtained for go and no‐go stimuli. Within the task, mean reaction time (RT) correlated positively with go P2 amplitude and negatively with P3b positivity. Regressions revealed that greater EC delta‐1 amplitude predicted shorter mean RT, and larger alpha‐3 amplitude predicted go P3b enhancement. These findings demonstrate the immediate P2 and P3b involvement in decision making and response control and the intrinsic EC delta‐1 and alpha‐3 amplitudes that underpin these processes.

show abstract

Section: Introductionsupporting

confidence: 77%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Using principal components analysis to examine resting state EEG in relation to task performance

2019

Self Cite

View full text Add to dashboard Cite

show abstract

“…In comparison to resting theta oscillations, the alpha rhythm dominates the resting EEG, with most individuals showing a distinct alpha peak at about 10 Hz having a robust posterior topography (Aurlien et al, 2004;Chiang, Rennie, Robinson, van Albada, & Kerr, 2011). Moreover, the alpha rhythm is prominent (e.g., visible in raw EEG traces) and reliably quantified by different research groups using different methodologies (e.g., Barry & De Blasio, 2018;Labounek et al, 2018;Schmidt et al, 2017;Shackman et al, 2010;Sockeel, Schwartz, Pélégrini-issac, & Benali, 2016;Tenke et al, 2017). Importantly, greater posterior alpha oscillations at rest predicted a favorable clinical outcome for individuals diagnosed with MDD (Baskaran et al, 2017;Bruder et al, 2008;Jaworska, de la Salle, Ibrahim, Blier, & Knott, 2019;Kandilarova et al, 2017;Knott et al, 1996;Tenke et al, 2011;Ulrich, Renfordt, Zeller, & Frick, 1984;Ulrich, Renfordt, & Frick, 1986; although see Arns et al, 2016, andKnott, Mahoney, Kennedy, &Evans, 2000, for unsuccessful attempts to replicate these findings).…”

Section: Research Findings On Posterior Alphaband Activity As a Canmentioning

confidence: 99%

Frontal theta and posterior alpha in resting EEG: A critical examination of convergent and discriminant validity

et al. 2019

View full text Add to dashboard Cite

Prior research has identified two resting EEG biomarkers with potential for predicting functional outcomes in depression: theta current density in frontal brain regions (especially rostral anterior cingulate cortex) and alpha power over posterior scalp regions. As little is known about the discriminant and convergent validity of these putative biomarkers, a thorough evaluation of these psychometric properties was conducted toward the goal of improving clinical utility of these markers. Resting 71-channel EEG recorded from 35 healthy adults at two sessions (1-week retest) were used to systematically compare different quantification techniques for theta and alpha sources at scalp (surface Laplacian or current source density [CSD]) and brain (distributed inverse; exact low resolution electromagnetic tomography [eLORETA]) level. Signal quality was evaluated with signal-to-noise ratio, participant-level spectra, and frequency PCA covariance decomposition. Convergent and discriminant validity were assessed within a multitrait-multimethod framework. Posterior alpha was reliably identified as two spectral components, each with unique spatial patterns and condition effects (eyes open/closed), high signal quality, and good convergent and discriminant validity. In contrast, frontal theta was characterized by one lowvariance component, low signal quality, lack of a distinct spectral peak, and mixed validity. Correlations between candidate biomarkers suggest that posterior alpha components constitute reliable, convergent, and discriminant biometrics in healthy adults. Component-based identification of spectral activity (CSD/eLORETA-fPCA) was superior to fixed, a priori frequency bands. Improved quantification and conceptualization of frontal theta is necessary to determine clinical utility.

show abstract

“…Since transforming from time domain to frequency domain requires EEG data lasting for some duration being processed, it means some unusual variance is discarded at the same time. Based on these global and stable features more sophisticated biomarkers can be achieved [16].…”

Section: Controller Networkmentioning

confidence: 99%

EEG data analysis with stacked differentiable neural computers

Ming

Pelusi

Fang

et al. 2018

Neural Comput & Applic

View full text Add to dashboard Cite

Differentiable Neural Computer (DNC) has demonstrated remarkable capabilities in solving complex problems. In this paper we propose to stack an enhanced version of differentiable neural computer together to extend its learning capabilities. Firstly, we give an intuitive interpretation of DNC to explain the architectural essence and demonstrate the stacking feasibility by contrasting it with the conventional Recurrent Neural Network (RNN). Secondly, the architecture of stacked DNCs is proposed and modified for electroencephalogram (EEG) data analysis. We substitute the original Long Short-Term Memory (LSTM) network controller by a recurrent convolutional network controller and adjust the memory accessing structures for processing EEG topographic data. Thirdly, the practicability of our proposed model is verified by an open-sourced EEG dataset with the highest average accuracy achieved; then after fine-tuning the parameters, we show the minimal mean error obtained on a proprietary EEG dataset. Finally, by analyzing the behavioral characteristics of the trained stacked DNCs model, we highlight the suitableness and potential of utilizing stacked DNCs in EEG signal processing.

show abstract

EEG frequency PCA in EEG‐ERP dynamics

Cited by 29 publications

References 39 publications

Using principal components analysis to examine resting state EEG in relation to task performance

Using principal components analysis to examine resting state EEG in relation to task performance

Frontal theta and posterior alpha in resting EEG: A critical examination of convergent and discriminant validity

EEG data analysis with stacked differentiable neural computers

Contact Info

Product

Resources

About