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Behavioral results by task. Bars show the behavioral difference (MW minus OT) between conditions. Error bars indicate the 95% confidence interval. ACC = accuracy; RT = response time; MW =
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Mind-wandering refers to the process of thinking task-unrelated thoughts while performing a task. The dynamics of mind-wandering remain elusive because it is difficult to track when someone’s mind is wandering based only on behavior. The goal of this study is to develop a machine-learning classifier that can determine someone’s mind-wandering state...
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... = 0.85). Figure 3 shows the behavioral performance difference between mind-wandering and on-task state (mind-wandering minus on-task). Negative values in the accuracy plot indicate worse performance in mind-wandering than in on-task. ...
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Citations
... Earlier studies have primarily focused on a single thought dimension, with most research examining thoughts that are unrelated to the task-at-hand. While these off-task thoughts have important theoretical implications (Smallwood & Schooler, 2015), real-world applications (Dong et al., 2021;Jin et al., 2019;Mills et al., 2021) as well as clinical implications (Arch et al., 2021;Hoffmann et al., 2016;Kucyi et al., 2023), it leaves out other important characteristics about our thoughts. As an example, it does not capture the dynamics of our thoughts (Christoff et al., 2016;Irving, 2016). ...
... Similarly, studies that have combined machine or deep learning approaches with electrophysiological data have solely focused on detecting off-task thoughts. These studies have reported several electrophysiological measures, including the P3 ERP component and alpha activity, that successfully predicted off-task thought occurrence in experimental (Dong et al., 2021;Groot et al., 2021;Jin et al., 2019Jin et al., , 2023 and naturalistic settings (Dhindsa et al., 2019). What remains unknown are the electrophysiological markers of other thought dimensions during naturalistic tasks or behavior, and whether they can be combined with deep learning to detect the occurrence of thought dimensions. ...
... There were up to 49 datasets (7 sessions x 7 participants) included in a cluster-based permutation test for each thought dimension. This is comparable to or exceeds the number of data sets reported in recent studies using EEG to examine different types of thoughts (Dhindsa et al., 2019;Jin et al., 2019;Kam et al., 2021;Polychroni et al., 2022;Simola et al., 2023). ...
Humans engage in a continuous stream of ongoing mental experience. Recent work examining the neural correlates of several dimensions of thoughts has revealed their functional connectivity patterns using fMRI during constrained experimental tasks. Less is known about the electrophysiological basis of various thoughts dimensions in more naturalistic settings. To address this, we first examined the electrophysiological signatures of ongoing thoughts during naturalistic tasks in seven participants across seven recording sessions. We then combined deep learning algorithms with electrophysiological data to determine the utility of these signals in predicting thought dimensions. Based on a total of 49 data sets, our results revealed distinct oscillatory markers of 7 dimensions of ongoing thought as participants completed any computer-based activities they wished to perform. In addition to identifying electrophysiological markers consistent with those observed in experimental settings for internally oriented thoughts and freely moving thoughts, we found novel patterns not previously reported for off-task thoughts, goal-oriented thoughts, and sticky thoughts, primarily characterized by spectral activity in canonical theta, alpha, and beta bands. Importantly, applying deep learning algorithms on electrophysiological data reliably detected all seven thought dimensions at above chance levels for both within-participant (MCC = 0.22–0.43) and across-participant (MCC = 0.14–0.31) approaches. Together, these results established the electrophysiological signatures of seven dimensions of ongoing thought, assembling a comprehensive set of brain-to-experience mapping of the phenomenological features of thoughts. Our findings provide an important step toward predicting thought patterns in the real world with clinical implications for establishing biomarkers of typical and atypical thought patterns.
... Accordingly, this metric might be expected to be reduced in individuals with AD(H)D and/or in conditions that contain external disturbances; (2) Neural event-related potentials (ERPs), transient changes in SC, and overt gaze-shifts following unexpected sound-events in the background of the classroom. These metrics are thought to reflect exogenous capture of attention, increases in arousal and potentially distraction by salient irrelevant stimuli (Posner, 1980;Bidet-Caulet et al., 2015;Masson and Bidet-Caulet, 2019); (3) The frequency of gaze-shifts away from the teacher, and time spent looking around the classroom, metrics associated with attention-shifts and distractibility (Grosbras et al., 2005;Schomaker et al., 2017), and are often heightened in individuals with AD(H)D (Mauriello et al., 2022;Stokes et al., 2022;Selaskowski et al., 2023); (4) The power of alpha-and beta-oscillations, which are often associated with increased mind-wandering or boredom (Clarke et al., 2001;Boudewyn and Carter, 2018;Jin et al., 2019), and some have suggested may be altered in individuals with AD(H)D (although use of spectral signatures as biomarkers for AD(H)D is highly controversial; Gloss et al., 2016); (5) Continuous levels of arousal, as measured by SC, which some propose are either heightened or reduced in individuals with AD(H) D relative to their control peers (Sergeant, 2000;Bellato et al., 2020). ...
... This finding is consistent with the hypothesized role of these metrics in attention. Higher alpha-power is associated with reduced levels of attention/arousal, and is consistently found to increase in conditions of boredom, tiredness or prolonged time on tasks (Clarke et al., 2001;Dockree et al., 2007;Palva and Palva, 2007;Wöstmann et al., 2017;Boudewyn and Carter, 2018;Jin et al., 2019;Haro et al., 2022). Several studies have also found higher baseline alpha-power in individuals with AD(H)D versus controls (Barry et al., 2003;Johnstone et al., 2013;Bozhilova et al., 2022;Michelini et al., 2022), although results are not always consistent (Loo and Makeig, 2012;Johnstone et al., 2013). ...
Many people, and particularly individuals with attention deficit (hyperactivity) disorder (AD(H)D), find it difficult to maintain attention during classroom learning. However, traditional paradigms used to evaluate attention do not capture the complexity and dynamic nature of real-life classrooms. Using a novel virtual reality platform, coupled with measurement of neural activity, eye-gaze, and skin conductance, here we studied the neurophysiological manifestations of attention and distractibility, under realistic learning conditions. Individuals with AD(H)D exhibited higher neural responses to irrelevant sounds and reduced speech tracking of the teacher, relative to controls. Additional neurophysiological measures, such the power of alpha-oscillations and frequency of gaze-shifts away from the teacher, contributed to explaining variance in self-reported AD(H)D symptoms across the sample. These ecologically valid findings provide critical insight into the neurophysiological mechanisms underlying individual differences in the capacity for sustained attention and the proneness to distraction and mind-wandering, experienced in real-life situations.
... Using machine learning approaches, previous studies have tried to define the cognitive state of users with various bio-markers. One of the most popular methods is using EEG [3,13,26,66]. These methods have successfully distinguished between the attention lapsed state and sustained state with above chance level accuracy (> 60%). ...
Identifying objective markers of attentional states is critical, particularly in real-world scenarios where attentional lapses have serious consequences. In this study, we identified gaze-based indices of attentional lapses and validated them by examining their impact on the performance of classification models. We designed a virtual reality visual search task that encouraged active eye movements to define dynamic gaze-based metrics of different attentional states (zone in/out). The results revealed significant differences in both reactive ocular features, such as first fixation and saccade onset latency, and global ocular features, such as saccade amplitude, depending on the attentional state. Moreover, the performance of the classification models improved significantly when trained only on the proven gaze-based and behavioral indices rather than all available features, with the highest prediction accuracy of 79.3%. We highlight the importance of the preliminary studies before model training and provide generalizable gaze-based indices of attentional states for practical applications.
... Past studies of EEG-based machine learning detectors for task-unrelated thoughts have had success and used techniques that may be applicable to freely moving thought. For example, Jin and colleagues [26] used support vector machine (SVM) with a Gaussian radial basis function (RBF) to classify task-unrelated thoughts and task-related thoughts using ERP measures and spectral measures as features. They created a SVM model for each participant to predict task-unrelated thoughts, and created a classification model that is generalizable across two tasks with an average accuracy of 60%. ...
... Although past studies have used supervised classifiers such as decision tree [22], naïve bayes [32], k-nearest neighbor [32], artificial neural network [39], and random forest [32], we used SVM to evaluate the proposed method for several reasons. First, the majority of past research attained the best performance using SVM for classification of task-related and task-unrelated thoughts [24,26,30]. Second, SVM is best suited for well-structured small to medium sized datasets with low dimensional data as in the current study and is ideal for obtaining clear decision boundaries between two classes [24,26,30]. ...
... First, the majority of past research attained the best performance using SVM for classification of task-related and task-unrelated thoughts [24,26,30]. Second, SVM is best suited for well-structured small to medium sized datasets with low dimensional data as in the current study and is ideal for obtaining clear decision boundaries between two classes [24,26,30]. Finally, we also compared the performance of other classification algorithms like k-nearest neighbor, artificial neural network, and random forest with the performance of SVM algorithm, and found out that the SVM algorithm achieved highest performance, hence in this study we reported only the results corresponding to the SVM algorithm We performed exhaustive testing on SVM to select the best parameters of SVM algorithm for classifying freely moving and nonfreely moving thoughts. ...
Objective. Freely moving thought is a type of thinking that shifts from one topic to another without any overarching direction or aim. The ability to detect when freely moving thought occurs may help us promote its beneficial outcomes, such as for creative thinking and positive mood. Thus far, no studies have used machine learning to detect freely moving thought on the basis of ‘objective’ (e.g. neural or behavioral) data. Approach. Our study addresses this gap, using event-related potential (ERP) and spectral features of electroencephalogram (EEG) signals as well as behavioral measures during a simple attention task and machine learning to detect freely moving thought. EEG features were first examined with both inter-subject and intra-subject strategies. Specifically, the statistical and entropy features of the P3 ERP and alpha spectral measures were entered as inputs to the support vector machine. The best combination of EEG features achieving higher classification performance in both strategies were then selected to combine with behavioral features to further enhance classification performance. Main results. Our best performing model has a Matthew’s correlation coefficient and area under the curve of 0.3105 and 0.6665 for inter-subject models and 0.2815 and 0.6407 for intra-subject models respectively. Significance. The above chance level performance in both strategies using EEG and behavioral features shows great promise for machine learning approaches to detect freely moving thought and highlights their potential for real-time prediction in the real world. This has important implications for enhancing creative processes and mood associated with freely moving thought.
... In these studies, task difficulty remains constant, and participants subjectively report being in a mindwandering or concentrated state, which is used to label neural signals for classification. For instance, Groot et al. [45] and Jin et al. [46] utilize EEG data to classify between on-task and off-task states with maximum accuracies of 65% and 60%, respectively. While their works may serve as better benchmarks for this study, it remains difficult to assert that their identified mental states are associated with flow, as its necessary conditions, such as skill-challenge balance and immediate feedback, are not achieved in their tasks. ...
Maintaining a high mental engagement is critical for motor rehabilitation interventions. Achieving a flow experience, often conceptualized as a highly engaged mental state, is an ideal goal for motor rehabilitation tasks. This paper proposes a virtual reality-based fine fingertip motor task in which the difficulty is maintained to match individual abilities. The aim of this study is to decode the intrinsic fluctuations of flow experience from electroencephalogram (EEG) signals during the execution of a motor task, addressing a gap in flow research that overlooks these fluctuations. To resolve the conflict between sparse self-reported flow sampling and the high dimensionality of neural signals, we use motor behavioral measures to represent flow and label the EEG data, thereby increasing the number of samples. A machine learning-based neural decoder is then established to classify each trial into high-flow or low-flow using spectral power and coherence features extracted from the EEG signals. Cross-validation reveals that the classification accuracy of the neural decoder can exceed 80%. Notably, we highlight the contributions of high-frequency bands in EEG activities to flow decoding. Additionally, EEG feature analyses reveal significant increases in the power of parietal-occipital electrodes and global coherence values, specifically in the alpha and beta bands, during high-flow durations. This study validates the feasibility of decoding the intrinsic flow fluctuations during fine motor task execution with a high accuracy. The methodology and findings in this work lay a foundation for future applications in manipulating flow experience and enhancing engagement levels in motor rehabilitation practice.
... These studies have shown promising results for certain neural "signatures" of mind wandering such as activation within the default mode network (DMN) in functional magnetic resonance imaging (fMRI) and power at the α frequency in electroencephalography (EEG; Kam et al., 2022). These results have encouraged researchers to develop machine learning-based neural models that enable the prediction of mind-wandering episodes from brain data (Dong et al., 2021;Groot et al., 2021;Jin et al., 2019;Kucyi et al., 2021;Mittner et al., 2014). These strides have moved the field a step closer to achieving brain-based models that may have clinical utility, as well as deepen understanding of the neural processes driving involuntary thought. ...
A substantial portion of waking life consists of mental experiences that arise involuntarily, including mind wandering, spontaneous thought, and rumination. Given that these experiences are fundamental to cognitive function and mental health, there has been growing scientific and clinical interest in using measures of brain activity to model and predict the momentary occurrence of these various forms of involuntary thought. In the most common machine learning approach in neuroimaging and electrophysiology research, model training data include neural features derived from a population of individuals, and testing is performed on held-out data in one or more individuals. However, sources of idiosyncrasy, such as individual differences in the nature of thought and brain functional organization, raise critical questions regarding whether population-derived neural models can generalize to individuals. In this article, we describe how an idiographic (person-specific) approach has the potential to improve theory and practice in predictive neural modeling of involuntary thought. This approach emphasizes dense sampling of individuals so that a diverse set of brain states and mental experiences can be modeled within a single individual, and rigorous tests of cross-subject generalizability can be performed. We review the advantages and shortcomings of both nomothetic (population-based) and idiographic predictive modeling approaches, including recent insights from dense-sampling neuroimaging studies that demonstrate person-specific brain-based predictions of mind wandering. We discuss implications for developing personalized clinical biomarkers, strategies to overcome the practical challenges of an idiographic approach and neuroethical concerns that must be considered as person-specific models may enhance the potential for accurate brain-based predictions of involuntary thought.
... It employed short-term windowing to extract statistical features and applied feature selection algorithms to identify relevant features. Testing various classifiers, the study found that the random forest classifier, based on an attribute selected by the OneR rule set, achieved a prediction accuracy of 87.16%.Another study [8] identified mind-wandering states using EEG markers from sustained attention and visual search tasks. The study uses a support vector machine classifier and single-trial ERP methodology, with alpha power as the most predictive marker. ...
... It characterizes the average power or amplitude of a signal. Refer Equn.(8). Hyjorth Parameters Hjorth parameters are indicators of three properties in EEG signal processing: Activity, Mobility, and Complexity. ...
This paper introduces an innovative EEG-based Brain-Computer Interface (BCI), aiming to discern two cognitive states experienced by students during learning sessions. Focusing on "Relaxation" and "Engagement in learning tasks", the study identifies attentive students and students exhibiting disengagement. Utilizing single EEG channel and signals from the fronto-polar region, it aims to develop a real-time engagement detection system compatible with portable devices. Employing a basic machine learning pipeline, the research focuses on time-domain feature extraction, and capturing heterogeneous high-level features. Through feature analysis, selection, and support vector machine(SVM) classification, the BCI system differentiates between relaxed and learning states, achieving 60.36% accuracy with 10-fold cross-validation. The subject-wise analysis yields impressive results, reaching up to 93% accuracy. Despite challenges in EEG signal non-stationarity, the model's accuracy underscores the efficacy of the time-domain parameters.
... 18 9 The average state-spesific alpha power for every channel from the externally oriented task data. . 13 Temporal dynamics of group-averaged alpha power during externally oriented tasks (SART and visual search), derived from the Jin et al. dataset [14]. Epochs are presented in sequential order but are not temporally aligned across subjects due to pseudo-random probes. ...
... Modern supervised machine-learning approaches have increasingly been applied to classify mindwandering states using EEG data. Techniques such as support vector machines (SVM), convolutional neural networks (CNN), and nonlinear regression models have demonstrated promising improvements in identifying mind-wandering episodes [14,1,15], leveraging EEG features like alpha-band power, coherence, and event-related potentials. However, despite these advancements, current models often fail to achieve sufficient accuracy or generalizability across diverse tasks and participant groups. ...
... However, despite these advancements, current models often fail to achieve sufficient accuracy or generalizability across diverse tasks and participant groups. Recent studies [14,1] highlight both the potential and the limitations of these approaches, pointing to the need for deeper exploration of the neural mechanisms underlying attentional shifts. ...
... EEG data measures oscillatory electrical brain activity at the macroscopic scale with high time resolution (Speckmann et al., 2011;Yu et al., 2016). EEG has been shown to have a strong potential to provide biomarkers for diagnoses in many neuropsychiatric disorders (da Silva, 2013;Yu et al., 2016), including attention deficit hyperactivity disorder (ADHD; Lubar, 1991;Loo and Barkley, 2005;Liu et al., 2015;Janssen et al., 2017;Kiiski et al., 2020), but also as indicators of attention during different visual and cognitive tasks (Mulholland, 1969;Ray and Cole, 1985;Harmony et al., 1996;Klimesch et al., 1998;Sauseng et al., 2005;Busch and VanRullen, 2010;Liu et al., 2013;Abiri et al., 2019;Jin et al., 2019). ...
... A review of EEG/ERP applications can be found in Nidal and Malik (2014). In particular, multiple ERPs were found to be associated with mind-wandering (Jin et al., 2019), or different stages of attention (Abiri et al., 2019). Note that the EEG reflects thousands of simultaneously ongoing brain processes, making it challenging to see the brain response to the event of interest in the EEG recording of a single trial (Blankertz et al., 2011). ...
Introduction
The study of attention has been pivotal in advancing our comprehension of cognition. The goal of this study is to investigate which EEG data representations or features are most closely linked to attention, and to what extent they can handle the cross-subject variability.
Methods
We explore the features obtained from the univariate time series from a single EEG channel, such as time domain features and recurrence plots, as well as representations obtained directly from the multivariate time series, such as global field power or functional brain networks. To address the cross-subject variability in EEG data, we also investigate persistent homology features that are robust to different types of noise. The performance of the different EEG representations is evaluated with the Support Vector Machine (SVM) accuracy on the WithMe data derived from a modified digit span experiment, and is benchmarked against baseline EEG-specific models, including a deep learning architecture known for effectively learning task-specific features.
Results
The raw EEG time series outperform each of the considered data representations, but can fall short in comparison with the black-box deep learning approach that learns the best features.
Discussion
The findings are limited to the WithMe experimental paradigm, highlighting the need for further studies on diverse tasks to provide a more comprehensive understanding of their utility in the analysis of EEG data.
... We will further test whether this biomarker of sticky thinking provides information that improves the fitting of the DDM, which would be direct evidence for the interference of sticky thoughts in decision making. A good candidate for such a marker of sticky thinking is alpha-band power in electroencephalogram (EEG) since numerous studies have shown mind wandering to be related to alpha-band power at posterior sites (Arnau et al., 2020;Jann et al., 2010;Jin et al., 2019;Mo et al., 2013). Given that sticky thinking is also a type of mind wandering, but of a more disruptive kind, we hypothesized that sticky thinking may also be associated with alpha power in EEG. ...
... Based on previous literature on mind wandering, we chose P7 and P8 as representative electrodes to track mind wandering (Jin et al., 2019). EEG epochs were filtered using a filter kernel constructed with firls in MATLAB (The MathWorks, Inc.) and separated into the alpha (8-12 Hz) band. ...
... To begin with, we investigated whether increased alpha-band power would occur during sticky thinking, similar to what was observed in previous studies on mind wandering (Kam et al., 2022;Yang et al., 2022). We analyzed the alpha-band power of the selected representative electrodes derived from our previous work (Jin et al., 2019) including the P7 electrode and P8 electrode. There was a main effect of stickiness in the average power of the two electrodes, χ 2 (1) = 5.34, p = .021, ...
Depressed individuals are commonly known to suffer from low mood. Less attention is paid to their decision-making deficiencies, consisting of indecisiveness and biased judgments. Many theories attempt to explain these impairments by focusing on reduced sensitivity to reward and punishment or biased information processing. Beyond these accounts, the present study explores another scenario, namely, whether the occurrence of sticky thinking—the occurrence of thoughts that are difficult to disengage from—could be a cause for the disruption of the decision-making process in individuals with depression. To test this hypothesis, we utilized the drift-diffusion model to investigate the influence of sticky thinking on the accumulation of evidence during a task commonly used to measure spontaneous thinking—the Sustained Attention to Response Task. Results showed that the more vulnerable group—specifically those with higher levels of repetitive negative thinking and depressive symptoms, including rumination—performed less accurately than the less vulnerable group. The more vulnerable group also showed a lower speed of evidence accumulation as evidenced by a decrease in the drift rate according to the drift-diffusion model. Moreover, the more vulnerable group exhibited prolonged nondecision time when more sticky thoughts occurred. At the neural level, we found that stronger alpha-band power marked more sticky thinking. We also demonstrated that the lower drift rate in the more vulnerable group, compared to the less vulnerable group, was exclusive to moments when the alpha-band power was higher than average. In summary, the study supported the idea that sticky thinking could explain the decision-making impairment among individuals who are more vulnerable to depression and worry.
