Subject, session and task effects on power, connectivity and network centrality: a source-based EEG study

Pani, Sara Maria; Ciuffi, Marta; Demuru, Matteo; Bazzano, Giovanni; d’Aloja, Ernesto; Fraschini, Matteo

doi:10.1101/673343

Cited by 3 publications

(7 citation statements)

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“…Despite its noisy characteristics, several scalp EEG features still contain relevant subject-specific traits that have been tested under any conceivable scenario. In particular, EEG fingerprints have been successfully observed and reported in resting-state [ 2 , 3 , 4 ], motor, visual, auditory, imagined speech or even multi-functional systems [ 5 , 6 , 7 ]. More recently, the aperiodic component of the power spectrum [ 8 ], as defined by the offset and the exponent, which reflect its 1/f-like characteristic, was shown to outperform other commonly used EEG features [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

EEG Fingerprints under Naturalistic Viewing Using a Portable Device

Fraschini

Meli

Demuru

et al. 2020

Sensors

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The electroencephalogram (EEG) has been proven to be a promising technique for personal identification and verification. Recently, the aperiodic component of the power spectrum was shown to outperform other commonly used EEG features. Beyond that, EEG characteristics may capture relevant features related to emotional states. In this work, we aim to understand if the aperiodic component of the power spectrum, as shown for resting-state experimental paradigms, is able to capture EEG-based subject-specific features in a naturalistic stimuli scenario. In order to answer this question, we performed an analysis using two freely available datasets containing EEG recordings from participants during viewing of film clips that aim to trigger different emotional states. Our study confirms that the aperiodic components of the power spectrum, as evaluated in terms of offset and exponent parameters, are able to detect subject-specific features extracted from the scalp EEG. In particular, our results show that the performance of the system was significantly higher for the film clip scenario if compared with resting-state, thus suggesting that under naturalistic stimuli it is even easier to identify a subject. As a consequence, we suggest a paradigm shift, from task-based or resting-state to naturalistic stimuli, when assessing the performance of EEG-based biometric systems.

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Section: Introductionmentioning

confidence: 99%

EEG Fingerprints under Naturalistic Viewing Using a Portable Device

Fraschini

Meli

Demuru

et al. 2020

Sensors

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“…The systematic inter-day differences were evident from the dissimilarity between samples from all participants and all days (90 samples per participant per day) (Figure 4B). The dissimilarity between any two samples was described by their correlation distance (= 1 - r , where r is the Pearson’s correlation coefficient)(Diedrichsen and Kriegeskorte 2017; Dimsdale-Zucker and Ranganath 2019; Pani et al 2020). For all 24 participants, the mean dissimilarity between samples from the same day was lower than between samples from different days ( cross-day ) [ t 23 = −6.74, p < 0.0001].…”

Section: Resultsmentioning

confidence: 99%

“…However, our approach and findings here are agnostic as to whether the inter-sample differences indicate variability around a characteristic mean value (i.e., static connectivity) or characteristic transitions between distinct states (i.e., dynamic connectivity). The relationship between the classifier-based multivariate representations to connectivity and distances measures (e.g., Valizadeh et al 2019; Pani et al 2020) is a key issue to be resolved by future studies.…”

Section: Discussionmentioning

confidence: 99%

“…By adopting the above strategy to obtain suitable NP- and NP+ examples, the demands for NP+ / NP- classification shared similarities to the demands for person identification, namely, obtaining a decision-rule to distinguish S from other individuals based on RS-activity (Figure 1B). Numerous prior studies demonstrate that RS-activity can serve as a “fingerprint” for person identification (Huang et al 2012; Campisi and Rocca 2014; Finn et al 2015; Valizadeh et al 2019; Pani et al 2020). Although our focus was not on the neural basis of individual differences and trait-identification (Smit et al 2005, 2006; Demuru et al 2017; Finn et al 2017; Gratton et al 2018), this person identification approach provided a convenient technical platform for our test of individual-specific longitudinal inference.…”

Section: Introductionmentioning

confidence: 99%

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Robustness of individualized inferences from longitudinal resting state dynamics

Hommelsen

Viswanathan

Daun

2020

Preprint

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Individual human brains plastically reorganize over extended timescales ranging from days to years, which makes these changes difficult to track. One promising tracking indicator is the neural activity at rest. However, the rest state (RS) is vulnerable to incidental inter-day cognitive variability that can confound the detection of changes to underlying neurophysiology. Here we show that this confounding is minimized by tracking changes to the distinctiveness of an individual’s RS activity, which is shaped by individual neurophysiology. Using longitudinal (5-day) RS acquired with EEG, we devised empirical simulations of distinctiveness changes confounded by cognitive variation. These inter-day changes were correctly classified with over 96% accuracy from 2-second snapshots of instantaneous oscillatory power from RS and confounded-RS. Surprisingly, the individual indicators of distinctiveness were concentrated at characteristic fronto-central and occipital zones. These findings support the suitability of longitudinal RS for individualized inferences about neurophysiological change in health and disease.

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“…Our proposed strategy is based on the observation that RS change classification is qualitatively isomorphic to the well‐studied problem of RS‐based person identification . Numerous studies demonstrate that individual RS activity is highly distinctive to the extent that a person can be identified relative to others solely from their RS activity (Campisi & Rocca, 2014; Finn et al, 2015; see, e.g., Huang et al, 2012; Pani et al, 2020; Valizadeh et al, 2019). In that framework, identification is a form of population inference with a focus on multivariate relationships in a person's RS activity that generalize to samples of the person's own activity but not to the activity of others.…”

Section: Introductionmentioning

confidence: 99%

Robustness of individualized inferences from longitudinal resting state EEG dynamics

Hommelsen¹,

Viswanathan²,

Daun

2022

Eur J of Neuroscience

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Tracking how individual human brains change over extended timescales is crucial to clinical scenarios ranging from stroke recovery to healthy aging. The use of resting state (RS) activity for tracking is a promising possibility. However, it is unresolved how a person's RS activity over time can be decoded to distinguish neurophysiological changes from confounding cognitive variability. Here, we develop a method to screen RS activity changes for these confounding effects by formulating it as a problem of change classification.We demonstrate a novel solution to change classification by linking individual-specific change to inter-individual differences. Individual RS-electroencephalography (EEG) was acquired over 5 consecutive days including task states devised to simulate the effects of inter-day cognitive variation. As inter-individual differences are shaped by neurophysiological differences, the inter-individual differences in RS activity on 1 day were analysed (using machine learning) to identify distinctive configurations in each individual's RS activity. Using this configuration as a decision rule, an individual could be re-identified from 2-s samples of the instantaneous oscillatory power spectrum acquired on a different day both from RS and confounded RS with a limited loss in accuracy. Importantly, the low loss in accuracy in cross-day versus same-day classification was achieved with classifiers that combined information from multiple frequency bands at channels across the scalp (with a concentration at characteristic fronto-central and occipital zones). Taken together, these findings support the technical feasibility of screening RS activity for confounding effects and the suitability of Abbreviations: A I p ! B I q , train decision rule on state A from day p ( A I p ); test on state B from day q ( B I q ); A I p ∘ A I q ! B I r , train decision rule on state A from days p and q (aggregation); test on state B from day r; B δ , B θ, B α , B β1 , B β2 , mono-band feature sets for the delta (δ); theta (θ); alpha (α); low-beta (β 1 ); and high-beta (β 2 ) frequency bands; EEG, electroencephalography; L F , L FC , L CP , L PO , mono-location feature sets for the frontal; frontocentral, centro-parietal and parieto-occipital zones; NPÀ, absence (negative instance) of inter-day neurophysiological change; NP+, presence (positive instance) of inter-day neurophysiological change; RS, resting state; RS1, RS2, resting state Session 1, Session 2; X I d , combined sample distribution of individuals in state X from day d.

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Subject, session and task effects on power, connectivity and network centrality: a source-based EEG study

Cited by 3 publications

References 25 publications

EEG Fingerprints under Naturalistic Viewing Using a Portable Device

EEG Fingerprints under Naturalistic Viewing Using a Portable Device

Robustness of individualized inferences from longitudinal resting state dynamics

Robustness of individualized inferences from longitudinal resting state EEG dynamics

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