Network oscillations imply the highest cognitive workload and lowest cognitive control during idea generation in open-ended creation tasks

EEG microstate transition cost correlates with task demands

The ability to solve complex tasks relies on the adaptive changes occurring in the spatio-temporal organization of brain activity under different conditions. Altered flexibility in these dynamics can lead to impaired cognitive performance, manifesting for instance as difficulties in attention regulation, distraction inhibition, and behavioral adaptation. Such impairments result in decreased efficiency and increased effort in accomplishing goal-directed tasks. Therefore, developing quantitative measures that can directly assess the effort involved in these transitions using neural data is of paramount importance. In this study, we propose a framework to associate cognitive effort during the performance of tasks with electroencephalography (EEG) activation patterns. The methodology relies on the identification of discrete dynamical states (EEG microstates) and optimal transport theory. To validate the effectiveness of this framework, we apply it to a dataset collected during a spatial version of the Stroop task, a cognitive test in which participants respond to one aspect of a stimulus while ignoring another, often conflicting, aspect. The Stroop task is a cognitive test where participants must respond to one aspect of a stimulus while ignoring another, often conflicting, aspect. Our findings reveal an increased cost linked to cognitive effort, thus confirming the framework's effectiveness in capturing and quantifying cognitive transitions. By utilizing a fully data-driven method, this research opens up fresh perspectives for physiologically describing cognitive effort within the brain.

EEG-based study of design creativity: a review on research design, experiments, and analysis

Brain dynamics associated with design creativity tasks are largely unexplored. Despite significant strides, there is a limited understanding of the brain-behavior during design creation tasks. The objective of this paper is to review the concepts of creativity and design creativity as well as their differences, and to explore the brain dynamics associated with design creativity tasks using electroencephalography (EEG) as a neuroimaging tool. The paper aims to provide essential insights for future researchers in the field of design creativity neurocognition. It seeks to examine fundamental studies, present key findings, and initiate a discussion on associated brain dynamics. The review employs thematic analysis and a forward and backward snowball search methodology with specific inclusion and exclusion criteria to select relevant studies. This search strategy ensured a comprehensive review focused on EEG-based creativity and design creativity experiments. Different components of those experiments such as participants, psychometrics, experiment design, and creativity tasks, are reviewed and then discussed. The review identifies that while some studies have converged on specific findings regarding EEG alpha band activity in creativity experiments, there remain inconsistencies in the literature. The paper underscores the need for further research to unravel the interplays between these cognitive processes. This comprehensive review serves as a valuable resource for readers seeking an understanding of current literature, principal discoveries, and areas where knowledge remains incomplete. It highlights both positive and foundational aspects, identifies gaps, and poses lingering questions to guide future research endeavors.

Complexity Measures for EEG Microstate Sequences: Concepts and Algorithms

EEG microstate sequence analysis quantifies properties of ongoing brain electrical activity which is known to exhibit complex dynamics across many time scales. In this report we review recent developments in quantifying microstate sequence complexity, we classify these approaches with regard to different complexity concepts, and we evaluate excess entropy as a yet unexplored quantity in microstate research. We determined the quantities entropy rate, excess entropy, Lempel-Ziv complexity (LZC), and Hurst exponents on Potts model data, a discrete statistical mechanics model with a temperature-controlled phase transition. We then applied the same techniques to EEG microstate sequences from wakefulness and non-REM sleep stages and used first-order Markov surrogate data to determine which time scales contributed to the different complexity measures. We demonstrate that entropy rate and LZC measure the Kolmogorov complexity (randomness) of microstate sequences, whereas excess entropy and Hurst exponents describe statistical complexity which attains its maximum at intermediate levels of randomness. We confirmed the equivalence of entropy rate and LZC when the LZ-76 algorithm is used, a result previously reported for neural spike train analysis (Amigó et al., Neural Comput 16:717-736, https://doi.org/10.1162/089976604322860677 , 2004). Surrogate data analyses prove that entropy-based quantities and LZC focus on short-range temporal correlations, whereas Hurst exponents include short and long time scales. Sleep data analysis reveals that deeper sleep stages are accompanied by a decrease in Kolmogorov complexity and an increase in statistical complexity. Microstate jump sequences, where duplicate states have been removed, show higher randomness, lower statistical complexity, and no long-range correlations. Regarding the practical use of these methods, we suggest that LZC can be used as an efficient entropy rate estimator that avoids the estimation of joint entropies, whereas entropy rate estimation via joint entropies has the advantage of providing excess entropy as the second parameter of the same linear fit. We conclude that metrics of statistical complexity are a useful addition to microstate analysis and address a complexity concept that is not yet covered by existing microstate algorithms while being actively explored in other areas of brain research.

Propofol Reversibly Attenuates Short-Range Microstate Ordering and 20 Hz Microstate Oscillations

Microstate sequences summarize the changing voltage patterns measured by electroencephalography, using a clustering approach to reduce the high dimensionality of the underlying data. A common approach is to restrict the pattern matching step to local maxima of the global field power (GFP) and to interpolate the microstate fit in between. In this study, we investigate how the anesthetic propofol affects microstate sequence periodicity and predictability, and how these metrics are changed by interpolation. We performed two frequency analyses on microstate sequences, one based on time-lagged mutual information, the other based on Fourier transform methodology, and quantified the effects of interpolation. Resting-state microstate sequences had a 20 Hz frequency peak related to dominant 10 Hz (alpha) rhythms, and the Fourier approach demonstrated that all five microstate classes followed this frequency. The 20 Hz periodicity was reversibly attenuated under moderate propofol sedation, as shown by mutual information and Fourier analysis. Characteristic microstate frequencies could only be observed in non-interpolated microstate sequences and were masked by smoothing effects of interpolation. Information-theoretic analysis revealed faster microstate dynamics and larger entropy rates under propofol, whereas Shannon entropy did not change significantly. In moderate sedation, active information storage decreased for non-interpolated sequences. Signatures of non-equilibrium dynamics were observed in non-interpolated sequences, but no changes were observed between sedation levels. All changes occurred while subjects were able to perform an auditory perception task. In summary, we show that low dose propofol reversibly increases the randomness of microstate sequences and attenuates microstate oscillations without correlation to cognitive task performance. Microstate dynamics between GFP peaks reflect physiological processes that are not accessible in interpolated sequences.

Frequency Analysis of EEG Microstate Sequences in Wakefulness and NREM Sleep

The majority of EEG microstate analyses concern wakefulness, and the existing sleep studies have focused on changes in spatial microstate properties and on microstate transitions between adjacent time points, the shortest available time scale. We present a more extensive time series analysis of unsmoothed EEG microstate sequences in wakefulness and non-REM sleep stages across many time scales. Very short time scales are assessed with Markov tests, intermediate time scales by the entropy rate and long time scales by a spectral analysis which identifies characteristic microstate frequencies. During the descent from wakefulness to sleep stage N3, we find that the increasing mean microstate duration is a gradual phenomenon explained by a continuous slowing of microstate dynamics as described by the relaxation time of the transition probability matrix. The finite entropy rate, which considers longer microstate histories, shows that microstate sequences become more predictable (less random) with decreasing vigilance level. Accordingly, the Markov property is absent in wakefulness but in sleep stage N3, 10/19 subjects have microstate sequences compatible with a second-order Markov process. A spectral microstate analysis is performed by comparing the time-lagged mutual information coefficients of microstate sequences with the autocorrelation function of the underlying EEG. We find periodic microstate behavior in all vigilance states, linked to alpha frequencies in wakefulness, theta activity in N1, sleep spindle frequencies in N2, and in the delta frequency band in N3. In summary, we show that EEG microstates are a dynamic phenomenon with oscillatory properties that slow down in sleep and are coupled to specific EEG frequencies across several sleep stages.

Loosely controlled experimental EEG datasets for higher-order cognitions in design and creativity tasks

Understanding neural mechanisms in design and creativity processes remains a challenging endeavor. To address this gap, we present two electroencephalography (EEG) datasets recorded in design and creativity experiments. We have discussed the details, similarities, differences, and corresponding cognitive tasks of the two datasets in the following sections. The design dataset (Dataset A) comprises EEG recordings of 27 participants during loosely controlled design creation experiments. Each experiment included six design problems. In each design problem, participants performed five cognitive tasks, including problem understanding, idea generation, rating idea generation, idea evaluation, and rating idea evaluation. The NASA Task Load Index was used in rating tasks. The creativity dataset (Dataset B) includes EEG signals recorded from 28 participants in creativity experiments which were based on a modified variant of the Torrance Test of Creative Thinking (TTCT-F). Participants were presented with three incomplete sketches and were asked to perform three creativity tasks for each sketch: idea generation, idea evolution, and idea evaluation. In both datasets, we structured the experiments into predefined steps, primarily to ensure participants' comfort and task clarity. This was the only control applied to the experiments. All the tasks were loosely controlled: open-ended (up to 3 min) and self-paced. 64-channel EEG signals were recorded at 500 Hz based on the international 10-10 system by the Brain Vision EEG recording system while the participants were performing their assigned tasks. EEG channels were pre-processed and finally referenced to the Cz channel to remove artifacts. EEGs were pre-processed using popular pipelines widely used in previous studies. Preprocessed EEG signals were finally segmented according to the tasks to facilitate future analyses. The EEG signals are stored in the .mat format. While the present paper mainly addresses pre-processed datasets, it also cites raw EEG recordings in the following sections. We aim to promote research and facilitate the development of experimental protocols and methodologies in design and creativity cognition by sharing these resources. There exist important points regarding the datasets which are worth mentioning. These datasets represent a novel contribution to the field, offering insights into design and creativity neurocognition. To our knowledge, publicly accessible datasets of this nature are scarce, and, to the best of our knowledge, our datasets are the first publicly available ones in design and creativity. Researchers can utilize these datasets directly or draw upon the considerations and technical insights provided to inform their studies. Furthermore, we introduce the concept of loosely controlled cognitive experiments in design and creativity cognition. These experiments strike a balance between flexibility and control, allowing participants to incubate creative ideas over extended response times while maintaining structured experimental sections. Such an approach fosters more natural data recording procedures and holds the potential to enhance the accuracy and reliability of future studies. The loosely controlled approach can be employed in future cognitive studies. This paper also conducts a comparative analysis of the two datasets, offering a holistic view of design and creativity tasks. By exploring various aspects of these cognitive processes, we provide an understanding for future researchers.

Differences in engineers' brain activity when CAD modelling from isometric and orthographic projections

A way of presenting information in visual representations of technical systems influences the progress and the outcome of the engineering design process. Consequently, improving the means by and through which information is utilised during the process is one suggested approach to advancing engineering design. Engineers' interaction with visual representations of technical systems is mainly visual and virtual. Although such interactions are cognitively complex, little is known about cognition (mental information processing) underlying the utilisation of design information during engineering design. To narrow the research gap, this study explores how visual representations of technical systems affect engineers' brain activity while generating computer-aided design (CAD) models based on them. More precisely, the brain activity of 20 engineers is captured and analysed using electroencephalography (EEG) during the visuospatially-intensive design tasks of CAD modelling in two conditions; when technical systems are presented with orthographic and isometric projections in technical drawings. The results imply the sensitivity of engineers' brain activity in CAD modelling to the visual representation from which a technical system is interpreted. In particular, significant differences are found in theta, alpha, and beta task-related power (TRP) over the cortex when interpreting the technical drawings and CAD modelling from them. Furthermore, the results reveal significant differences in theta and alpha TRP when considering the individual electrodes, the cortical hemispheres, and the cortical areas. In particular, theta TRP over the right hemisphere and the frontal area seems essential in distinguishing neurocognitive responses to the orthographic and isometric projections. As such, the conducted exploratory study sets the foundations for exploring engineers' brain activity while performing visuospatially-intensive design tasks, whose segments are relatable to the aspects of visuospatial thinking. Future work will explore brain activity in other design activities that are highly visuospatial, with a larger sample size and an EEG device of a higher spatial resolution.

How does experimentally induced pain affect creative ideation and underlying attention-related psychophysiological mechanisms?

While the adverse effect of chronic pain on attention and more complex cognitive abilities is well documented, the findings for experimentally induced pain are inconsistent. These inconsistencies could be attributable to sufficient attentional resources and/or compensatory mechanisms in individuals experiencing experimentally induced pain that are not observable at the behavioral level but could be revealed by psychophysiological measures such as the electroencephalography (EEG). With the current study, we aimed to investigate whether experimentally induced pain affects creative ideation in an adaptation of the Alternate Uses Task (AUT). Performance in the AUT was compared between 39 females in a pain group and 37 females in a pain-free group. While solving the task, EEG was recorded to measure the degree of internally directed attention assessed by means of task-related power (TRP) changes in the upper alpha-frequency band. The results revealed that the pain group and the pain-free group did not differ in AUT performance at the behavioral level. However, TRP increases in the upper alpha band at right (vs. left) temporal, parietal, and occipital electrode sites were significantly more pronounced in the pain group compared to the pain-free group. These results indicate that individuals in the pain group allocated more attention to internal mental processes during creative ideation than individuals in the pain-free group. The necessary inhibition of pain might have caused this additional activation so that the pain group performed similarly well on the behavioral level as the pain-free group.

Functional brain activation patterns of creative metacognitive monitoring

Creative metacognitive monitoring represents the ability to accurately evaluate the quality of own ideas during idea generation. To the best of our knowledge, this study presents the first EEG investigation of creative metacognitive monitoring in the brain, using data, of 100 participants, who generated single, original uses of common objects (alternate uses task). After each response, participants subjectively rated the creative quality of their idea. Additionally, five independent external judges rated the creative quality of all ideas. The correspondence between the subjective and the external performance ratings served as a measure of monitoring accuracy. We applied a generalized linear mixed effects model to investigate effects of creative metacognitive monitoring and creative potential on EEG activity in the alpha band at idea and person level. Participants with both higher monitoring skills and higher creative potential showed stronger alpha power decreases at parietal/occipital sites during creative idea generation and evaluation. Interestingly, only more creative people with lower metacognitive monitoring skills showed the expected alpha power increases at parietal/occipital sites during both phases. Furthermore, metacognitive monitoring skills were associated with lower frontal and temporal/central alpha power during idea evaluation (compared to generation) at the person level. This pattern of findings seems to suggest that less internal attention, less memory load, and increased sensory processing are associated with more effective and accurate monitoring of the creative process. This study sheds first light on the brain mechanisms underlying the interplay of creative metacognitive monitoring skills and creative potential.

More creative ideas are associated with increased right posterior power and frontal-parietal/occipital coupling in the upper alpha band: A within-subjects study

The neurophysiological investigation of creative idea generation is a growing research area. EEG studies congruently reported the sensitivity of upper alpha power (10-12 Hz) for the creative ideation process and its outcome. However, the majority of studies were between-subject design studies and research directly comparing the neurophysiological activation pattern when generating more and less creative ideas within a person are rare. Therefore, the present study was specifically focused on investigating brain activation patterns associated with the generation of more vs. less creative ideas. We applied an alternate uses task (AU-task; i.e., finding original uses for everyday objects such as a brick) in a sample of 74 participants and recorded the brain activation during the AU-task and reference period. A portable EEG system with 21 dry electrodes arranged in the international 10-20 system and linked ear as reference was used. We found a higher increase of upper alpha power during creative ideation (relative to reference period, i.e., task-related power, TRP) over right posterior sites when people generated more compared to less creative ideas. This was accompanied by an increase of functional coupling (i.e., task-related coherence increase) between frontal and parietal/occipital sites, which suggests higher internal attention and more control over sensory processes. Taken together, these findings complement the existing creativity research literature and indicate the importance of alpha power for the creative ideation process also within people.