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.

Effects of COVID-19-related psychological distress and anxiety on quality of sleep and life in healthcare workers in Iran and three European countries.

Introduction: The COVID-19 pandemic has considerably affected human beings most of whom are healthcare workers (HCWs) combating the disease in the front line. Methods: This cross-sectional study aims to explore the effects of stress and anxiety caused by COVID-19 on the quality of sleep and life in HCWs, including physicians, nurses, and other healthcare staff. In this global study, we asked 1,210 HCWs (620 and 590 volunteers from Iran and European countries, including Germany, the Netherlands, and Italy, respectively), who age 21-70, to participate in the test. Several measures of COVID-related stress, anxiety, sleep, and life quality, including the 12-item General Health Questionnaire (GHQ-12), Fear of COVID-19 scale (FCV-19S), Beck Anxiety Inventory (BAI), the Pittsburgh Sleep Quality Index (PSQI), and World Health Organization Quality of Life-BREF (WHOQOL-BREF) are recorded. Results: Volunteers reported high rates of stress and anxiety and poor sleep quality as well as lower quality of life. The correlation analysis between the measures is reported. According to the results, regardless of the location, HCWs, predominantly female nurses, developed anxiety and stress symptoms which consequently resulted in lower sleep and life quality. Both for Iranian and the European HCWs, significant differences existed between nurses and the other two groups, with the p-values equal to 0.0357 and 0.0429 for GHQ-12, 0.0368, and 0.714 for BAI measure. Even though nurses reported the most stress, anxiety, fear of COVID-19, lower quality of life and sleep in both countries, and also an increase in other measures as well, there existed no statistically significant difference in FCV-19S, PSQI, and WHOQOL-BREF. Discussion: This study helps to expand our knowledge the effects of pandemics on HCWs and also for healthcare management to predict HCW's mental health conditions in similar situations.

EEG artifact removal using sub-space decomposition, nonlinear dynamics, stationary wavelet transform and machine learning algorithms.

Blind source separation (BSS) methods have received a great deal of attention in electroencephalogram (EEG) artifact elimination as they are routine and standard signal processing tools to remove artifacts and reserve desired neural information. On the other hand, a classifier should follow BSS methods to automatically identify artifactual sources and remove them in the following steps. In addition, removing all detected artifactual components leads to loss of information since some desired information related to neural activity leaks to these sources. So, an approach should be employed to detect and suppress the artifacts and reserve neural activity. This study introduces a novel method based on EEG and Poincare planes in the phase space to detect artifactual components estimated by second-order blind identification (SOBI). Artifacts are detected using a mixture of well-known conventional classifiers and were removed employing stationary wavelet transform (SWT) to reserve neural information. The proposed method is a combination of signal processing techniques and machine learning algorithms, including multi-layer perceptron (MLP), K-nearest neighbor (KNN), naïve Bayes, and support vector machine (SVM) which have significant results while applying our proposed method to different scenarios. Simulated, semi-simulated, and real EEG signals are employed to evaluate the proposed method, and several evaluation criteria are calculated. We achieved acceptable results, for example, 98% average accuracy and 97% average sensitivity in artifactual EEG component detection or about 2% as mean square error in EEG reconstruction after artifact removal. Results showed that the proposed method is effective and can be used in future studies as we have considered different real-world scenarios to evaluate it.

Artificial Neural Network for Predicting the Safe Temporary Artery Occlusion Time in Intracranial Aneurysmal Surgery.

BACKGROUND: Temporary artery clipping facilitates safe cerebral aneurysm management, besides a risk for cerebral ischemia. We developed an artificial neural network (ANN) to predict the safe clipping time of temporary artery occlusion (TAO) during intracranial aneurysm surgery. METHOD: We devised a three-layer model to predict the safe clipping time for TAO. We considered age, the diameter of the right and left middle cerebral arteries (MCAs), the diameter of the right and left A1 segment of anterior cerebral arteries (ACAs), the diameter of the anterior communicating artery, mean velocity of flow at the right and left MCAs, and the mean velocity of flow at the right and left ACAs, as well as the Fisher grading scale of brain CT scans as the input values for the model. RESULTS: This study included 125 patients: 105 patients from a retrospective cohort for training the model and 20 patients from a prospective cohort for validating the model. The output of the neural network yielded up to 960 s overall safe clipping time for TAO. The input values with the greatest impact on safe TAO were mean velocity of blood at left MCA and left ACA, and Fisher grading scale of brain CT scan. CONCLUSION: This study presents an axillary framework to improve the accuracy of the estimated safe clipping time interval of temporary artery occlusion in intracranial aneurysm surgery.

Emotion recognition through EEG phase space dynamics and Dempster-Shafer theory.

Emotions play an important role in our life. Emotion recognition which is considered a subset of brain computer interface (BCI), has drawn a great deal of attention during recent years. Researchers from different fields have tried to classify emotions through physiological signals. Nonlinear analysis has been reported to be successful and effective in emotion classification due to the nonlinear and non-stationary behavior of biological signals. In this study, phase space reconstruction and Poincare planes are employed to describe the dynamics of electroencephalogram (EEG) in emotional states. EEG signals are taken from a reliable database and phase space is reconstructed. A new transformation is introduced in order to quantify the phase space. Dynamic characteristics of the new space are considered as features. Most significant features are selected and samples are classified into four groups including high arousal - high valence (HAHV), low arousal - high valence (LAHV), high arousal - low valence (HALV) and low arousal - low valence (LALV). Classification accuracy was about 90% on average. Results suggest that the proposed method is successful and classification performance is good in comparison with most studies in this field. Brain activity is also reported with respect to investigating brain function during emotion elicitation. We managed to introduce a new way to analyze EEG phase space. The proposed method is applied in a real world and challenging application (i.e. emotion classification). Not only does the proposed method describe EEG changes due to different emotional states but also it is able to represent new characteristics of complex systems. The suggested approach paves the way for researchers to analyze and understand more about chaotic signals and systems.

A novel approach to emotion recognition using local subset feature selection and modified Dempster-Shafer theory.

BACKGROUND: Emotion recognition is an increasingly important field of research in brain computer interactions. INTRODUCTION: With the advance of technology, automatic emotion recognition systems no longer seem far-fetched. Be that as it may, detecting neural correlates of emotion has remained a substantial bottleneck. Settling this issue will be a breakthrough of significance in the literature. METHODS: The current study aims to identify the correlations between different emotions and brain regions with the help of suitable electrodes. Initially, independent component analysis algorithm is employed to remove artifacts and extract the independent components. The informative channels are then selected based on the thresholded average activity value for obtained components. Afterwards, effective features are extracted from selected channels common between all emotion classes. Features are reduced using the local subset feature selection method and then fed to a new classification model using modified Dempster-Shafer theory of evidence. RESULTS: The presented method is employed to DEAP dataset and the results are compared to those of previous studies, which highlights the significant ability of this method to recognize emotions through electroencephalography, by the accuracy of about 91%. Finally, the obtained results are discussed and new aspects are introduced. CONCLUSIONS: The present study addresses the long-standing challenge of finding neural correlates between human emotions and the activated brain regions. Also, we managed to solve uncertainty problem in emotion classification which is one of the most challenging issues in this field. The proposed method could be employed in other practical applications in future.