Revisiting consciousness: Distinguishing between states of conscious focused attention and mind wandering with EEG

Silva, M R Dias da; Gonçalves, Óscar F.; Branco, Diogo; Postma, Marie

doi:10.1016/j.concog.2022.103332

Cited by 14 publications

(15 citation statements)

References 57 publications

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“…Under the heaviest memory load with digit target form, node degrees of Cz and F8 are most obviously enhanced when memorization compared with the control stage, and node degrees of FP1 and F7 are most obviously weakened. Considering that Cz has been widely applied to attention feedback/training to track attention level in real time [ 42 ], it is reasonable that Cz functions more actively in memory than in control, resulting in node degree of Cz increasing. In addition, according to Okamoto and colleague's work on correlation between the international 10–20 system and Brodmann Area (BA) [ 43 ]; FP1, F7, and F8 correspond to BA9 and BA10, which are believed responsible for working memory, attention, and task management and planning [ 44 , 45 ].…”

Section: Resultsmentioning

confidence: 99%

Classification for Memory Activities: Experiments and EEG Analysis Based on Networks Constructed via Phase-Locking Value

Huang

Dang

et al. 2022

Computational and Mathematical Methods in Medicine

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Electroencephalogram (EEG) plays a crucial role in the study of working memory, which involves the complex coordination of brain regions. In this research, we designed and conducted series of experiments of memory with various memory loads or target forms and collected behavioral data as well as 32-lead EEG simultaneously. Combined with behavioral data analysis, we segmented EEG into slices; then, we calculated phase-locking value (PLV) of Gamma rhythms between every two leads, conducted binarization, constructed brain function network, and extracted three network characteristics of node degree, local clustering coefficient, and betweenness centrality. Finally, we inputted these network characteristics of all leads into support vector machines (SVM) for classification and obtained decent performances; i.e., all classification accuracies are greater than 0.78 on an independent test set. Particularly, PLV application was restricted to the narrow-band signals, and rare successful application to EEG Gamma rhythm, defined as wide as 30-100 Hz, had been reported. In order to address this limitation, we adopted simulation on band-pass filtered noise with the same frequency band as Gamma to help determine the PLV binarizing threshold. It turns out that network characteristics based on binarized PLV have the ability to distinguish the presence or absence of memory, as well as the intensity of the mental workload at the moment of memory. This work sheds a light upon phase-locking investigation between relatively wide-band signals, as well as memory research via EEG.

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Section: Resultsmentioning

confidence: 99%

Classification for Memory Activities: Experiments and EEG Analysis Based on Networks Constructed via Phase-Locking Value

Huang

Dang

et al. 2022

Computational and Mathematical Methods in Medicine

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“…Previous EEG studies aimed, inter alia, at establishing links between MW and the theta/beta ratio 33 , or generally a given frequency peak 34 , and identifying EEG signature of off-task though using thought-probe method and ERP analysis 35,36 . The frequency-focused investigations have produced quite divergent results suggesting that MW is associated with alpha 36,37 delta and gamma 38 or by delta, theta and alpha bands 39 . In this aspect, functional connections which differentiated our groups have occurred mainly in the theta, delta and gamma bands.…”

Section: Discussionmentioning

confidence: 99%

Relationships between resting-state EEG functional networks organization and individual differences in mind wandering

Krukow

Jonak

2022

Sci Rep

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When performing cognitively demanding tasks, people tend to experience momentary distractions or personal associations that intercept their stream of consciousness. This phenomenon is known as Mind Wandering (MW) and it has become a subject of neuroscientific investigations. Off-task thoughts can be analyzed during task performance, but currently, MW is also understood as a dimension of individual differences in cognitive processing. We wanted to recognize the intrinsically-organized functional networks that could be considered the neuronal basis for MW dispositional variability. To achieve this goal we recruited a group of normal adults, and eventually divided the group in half, based on participants’ scores on the scale measuring dispositional MW. Next, these groups were compared regarding the arrangement of preselected intrinsic functional networks, which were reconstructed based on multi-channel signal-source resting-state EEG. It appeared that subjects who tend to mind wander often exhibited decreased synchronization within the default mode network, and, simultaneously, strengthened connectivity between ‘on-task’ networks of diverse functional specificity. Such within- and between networks integrity patterns might suggest that greater Mind Wanderers present an atypical organization of resting-state brain activity, which may translate into attenuated resources needed to maintain attentional control in task-related conditions.

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“…Overall, there is substantial evidence for the key role alpha oscillations play in gating information flow throughout the brain (Jensen & Mazaheri, 2010;Klimesch, 2012), and as a top-down inhibitory control mechanism to maintain task performance by suppressing task-irrelevant information (Händel et al, 2011). Numerous studies have reported greater alpha power over frontal, central, parietal and occipital scalp areas during periods of TUT (Arnau et al, 2020;Baldwin et al, 2017;Ceh et al, 2020;Compton et al, 2019;Dias da Silva et al, 2022;Groot et al, 2021;Hanslmayr et al, 2011;Jin et al, 2019;Macdonald et al, 2011;Martel et al, 2019), positioning it as a promising EEG signature of mind-wandering despite some evidence to the opposite (Baird et al, 2014;Braboszcz & Delorme, 2011). Together with the attenuation in ERP amplitudes, greater alpha-band activity suggests a drop in the cortical processing of the external environment as attention is redirected internally during TUT.…”

Section: Electrophysiological Markers Of Mind Wanderingmentioning

confidence: 99%

“…Kam et al, 2010; Martel et al, 2019). The amplitude of the P3, which indexes the general level of cognitive processing or allocation of attentional resources (Polich, 2007), has also been observed to be reduced during TUTs in various studies (Baldwin et al, 2017; Dias da Silva et al, 2022; Gouraud et al, 2021; Groot et al, 2021; Kam et al, 2010; Smallwood et al, 2008). In line with the perceptual decoupling hypothesis of mind-wandering (Smallwood & Schooler, 2015), these findings suggest a strong link with a general attenuation in cortical processing of external stimuli causing a drop in short-term performance (Mooneyham & Schooler, 2013; Randall et al, 2014) and overall vigilance (Braboszcz & Delorme, 2011).…”

Section: Introductionmentioning

confidence: 99%

“…The approach used has been developed and successfully employed to robustly extract electrophysiological markers of consciousness across contexts and protocols identifying features in the alpha and theta band as indexes of consciousness (Engemann et al, 2018; Sitt et al, 2014). Given that mind-wandering states are closely linked to conscious content (Schooler, 2002; Smallwood & Schooler, 2015) and to conscious access (Dias da Silva et al, 2022), we are extending this feature-extraction tool to TUT. Applying this approach to an EEG dataset recorded during a standard test of sustained attention represents the first attempt at systematically investigating the discriminative features of intentional and unintentional TUT.…”

Section: Introductionmentioning

confidence: 99%

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Distinct Electrophysiological Signatures of Intentional and Unintentional Mind-Wandering Revealed by Low-Frequency EEG Markers

Martel

Bruno

Robertson

et al. 2023

Preprint

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Mind-wandering is typically characterized by the common experience wherein attention veers off into thoughts unrelated to the task at hand. Recent research highlights the intentionality dimension of mind-wandering as a key predictor of adverse functional outcomes with intentional and unintentional task-unrelated thought (TUT) differentially linked to neural, behavioral, clinical, and functional correlates. We here aimed to elucidate the electrophysiological underpinnings of intentional and unintentional TUT by systematically examining the individual and collective discriminative power of a large set of EEG markers to distinguish between attentional states. Univariate and multivariate analyses were conducted on 54 predefined markers belonging to four conceptual families: ERP, spectral, information theory and connectivity measures, extracted from scalp EEG recordings prior to multidimensional reports of ongoing thought from participants performing a sustained attention task. We report here that on-task, intentional and unintentional TUT exhibit distinct electrophysiological signatures in the low frequency range. More specifically, increased features of the theta frequency range were found to be most discriminative between on-task and off-task states, while features within the alpha band were characteristic of intentional TUT when compared to unintentional TUT. This result is theoretically well aligned with contemporary accounts describing alpha activity as an index of internally oriented attention and a potential mechanism to shield internal processes from sensory input. Our study verifies the validity of the intentionality dimension of mind-wandering and represents a step forward towards real-time detection and mitigation of maladaptive mind-wandering.

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Revisiting consciousness: Distinguishing between states of conscious focused attention and mind wandering with EEG

Cited by 14 publications

References 57 publications

Classification for Memory Activities: Experiments and EEG Analysis Based on Networks Constructed via Phase-Locking Value

Classification for Memory Activities: Experiments and EEG Analysis Based on Networks Constructed via Phase-Locking Value

Relationships between resting-state EEG functional networks organization and individual differences in mind wandering

Distinct Electrophysiological Signatures of Intentional and Unintentional Mind-Wandering Revealed by Low-Frequency EEG Markers

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