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Lee C, Shin M, Eniyandunmo D, Anwar A, Kim E, Kim K, Yoo JK, Lee C. Predicting Driver's mental workload using physiological signals: A functional data analysis approach. APPLIED ERGONOMICS 2024; 118:104274. [PMID: 38521001 DOI: 10.1016/j.apergo.2024.104274] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Revised: 03/01/2024] [Accepted: 03/17/2024] [Indexed: 03/25/2024]
Abstract
This study investigates the impact of advanced driver-assistance systems on drivers' mental workload. Using a combination of physiological signals including ECG, EMG, EDA, EEG (af4 and fc6 channels from the theta band), and eye diameter data, this study aims to predict and categorize drivers' mental workload into low, adequate, and high levels. Data were collected from five different driving situations with varying cognitive demands. A functional linear regression model was employed for prediction, and the accuracy rate was calculated. Among the 31 tested combinations of physiological variables, 9 combinations achieved the highest accuracy result of 90%. These results highlight the potential benefits of utilizing raw physiological signal data and employing functional data analysis methods to understand and assess driver mental workload. The findings of this study have implications for the design and improvement of driver-assistance systems to optimize safety and performance.
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Affiliation(s)
- Chaeyoung Lee
- Mechanical, Automotive, and Materials Engineering, University of Windsor, 401 Sunset Ave, Windsor, ON N9B 3P4, Canada; Department of Statistics, Ewha Womans University, 52, Ewhayeodae-gil, Seodaemun-gu, Seoul, 03760, Republic of Korea.
| | - MinJu Shin
- Mechanical, Automotive, and Materials Engineering, University of Windsor, 401 Sunset Ave, Windsor, ON N9B 3P4, Canada; Department of Statistics, Ewha Womans University, 52, Ewhayeodae-gil, Seodaemun-gu, Seoul, 03760, Republic of Korea.
| | - David Eniyandunmo
- Mechanical, Automotive, and Materials Engineering, University of Windsor, 401 Sunset Ave, Windsor, ON N9B 3P4, Canada.
| | - Alvee Anwar
- Mechanical, Automotive, and Materials Engineering, University of Windsor, 401 Sunset Ave, Windsor, ON N9B 3P4, Canada.
| | - Eunsik Kim
- Mechanical, Automotive, and Materials Engineering, University of Windsor, 401 Sunset Ave, Windsor, ON N9B 3P4, Canada.
| | - Kyongwon Kim
- Department of Statistics, Ewha Womans University, 52, Ewhayeodae-gil, Seodaemun-gu, Seoul, 03760, Republic of Korea.
| | - Jae Keun Yoo
- Department of Statistics, Ewha Womans University, 52, Ewhayeodae-gil, Seodaemun-gu, Seoul, 03760, Republic of Korea.
| | - Chris Lee
- Civil and Environmental Engineering, University of Windsor, 401 Sunset Ave, Windsor, ON, N9B 3P4, Canada.
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Sagehorn M, Johnsdorf M, Kisker J, Gruber T, Schöne B. Electrophysiological correlates of face and object perception: A comparative analysis of 2D laboratory and virtual reality conditions. Psychophysiology 2024; 61:e14519. [PMID: 38219244 DOI: 10.1111/psyp.14519] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Revised: 12/12/2023] [Accepted: 12/26/2023] [Indexed: 01/16/2024]
Abstract
Human face perception is a specialized visual process with inherent social significance. The neural mechanisms reflecting this intricate cognitive process have evolved in spatially complex and emotionally rich environments. Previous research using VR to transfer an established face perception paradigm to realistic conditions has shown that the functional properties of face-sensitive neural correlates typically observed in the laboratory are attenuated outside the original modality. The present study builds on these results by comparing the perception of persons and objects under conventional laboratory (PC) and realistic conditions in VR. Adhering to established paradigms, the PC- and VR modalities both featured images of persons and cars alongside standard control images. To investigate the individual stages of realistic face processing, response times, the typical face-sensitive N170 component, and relevant subsequent components (L1, L2; pre-, post-response) were analyzed within and between modalities. The between-modality comparison of response times and component latencies revealed generally faster processing under realistic conditions. However, the obtained N170 latency and amplitude differences showed reduced discriminative capacity under realistic conditions during this early stage. These findings suggest that the effects commonly observed in the lab are specific to monitor-based presentations. Analyses of later and response-locked components showed specific neural mechanisms for identification and evaluation are employed when perceiving the stimuli under realistic conditions, reflected in discernible amplitude differences in response to faces and objects beyond the basic perceptual features. Conversely, the results do not provide evidence for comparable stimulus-specific perceptual processing pathways when viewing pictures of the stimuli under conventional laboratory conditions.
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Affiliation(s)
- Merle Sagehorn
- Experimental Psychology I, Institute of Psychology, Osnabrück University, Osnabrück, Germany
| | - Marike Johnsdorf
- Experimental Psychology I, Institute of Psychology, Osnabrück University, Osnabrück, Germany
| | - Joanna Kisker
- Experimental Psychology I, Institute of Psychology, Osnabrück University, Osnabrück, Germany
| | - Thomas Gruber
- Experimental Psychology I, Institute of Psychology, Osnabrück University, Osnabrück, Germany
| | - Benjamin Schöne
- Experimental Psychology I, Institute of Psychology, Osnabrück University, Osnabrück, Germany
- Department of Psychology, Norwegian University of Science and Technology, Trondheim, Norway
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3
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Feng Y, Li J, Song X. Testing conditional quantile independence with functional covariate. Biometrics 2024; 80:ujae036. [PMID: 38742907 DOI: 10.1093/biomtc/ujae036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2023] [Revised: 03/10/2024] [Accepted: 04/25/2024] [Indexed: 05/16/2024]
Abstract
We propose a new non-parametric conditional independence test for a scalar response and a functional covariate over a continuum of quantile levels. We build a Cramer-von Mises type test statistic based on an empirical process indexed by random projections of the functional covariate, effectively avoiding the "curse of dimensionality" under the projected hypothesis, which is almost surely equivalent to the null hypothesis. The asymptotic null distribution of the proposed test statistic is obtained under some mild assumptions. The asymptotic global and local power properties of our test statistic are then investigated. We specifically demonstrate that the statistic is able to detect a broad class of local alternatives converging to the null at the parametric rate. Additionally, we recommend a simple multiplier bootstrap approach for estimating the critical values. The finite-sample performance of our statistic is examined through several Monte Carlo simulation experiments. Finally, an analysis of an EEG data set is used to show the utility and versatility of our proposed test statistic.
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Affiliation(s)
- Yongzhen Feng
- Center for Statistical Science and Department of Industrial Engineering, Tsinghua University, Beijing 100084, China
| | - Jie Li
- Center for Applied Statistics and School of Statistics, Renmin University of China, Beijing 100872, China
| | - Xiaojun Song
- Department of Business Statistics and Econometrics, Guanghua School of Management and Center for Statistical Science, Peking University, Beijing 100871, China
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Laufer I, Mizrahi D, Zuckerman I. Enhancing EEG-based attachment style prediction: unveiling the impact of feature domains. Front Psychol 2024; 15:1326791. [PMID: 38318079 PMCID: PMC10838989 DOI: 10.3389/fpsyg.2024.1326791] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Accepted: 01/04/2024] [Indexed: 02/07/2024] Open
Abstract
Introduction Attachment styles are crucial in human relationships and have been explored through neurophysiological responses and EEG data analysis. This study investigates the potential of EEG data in predicting and differentiating secure and insecure attachment styles, contributing to the understanding of the neural basis of interpersonal dynamics. Methods We engaged 27 participants in our study, employing an XGBoost classifier to analyze EEG data across various feature domains, including time-domain, complexity-based, and frequency-based attributes. Results The study found significant differences in the precision of attachment style prediction: a high precision rate of 96.18% for predicting insecure attachment, and a lower precision of 55.34% for secure attachment. Balanced accuracy metrics indicated an overall model accuracy of approximately 84.14%, taking into account dataset imbalances. Discussion These results highlight the challenges in using EEG patterns for attachment style prediction due to the complex nature of attachment insecurities. Individuals with heightened perceived insecurity predominantly aligned with the insecure attachment category, suggesting a link to their increased emotional reactivity and sensitivity to social cues. The study underscores the importance of time-domain features in prediction accuracy, followed by complexity-based features, while noting the lesser impact of frequency-based features. Our findings advance the understanding of the neural correlates of attachment and pave the way for future research, including expanding demographic diversity and integrating multimodal data to refine predictive models.
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Affiliation(s)
| | - Dor Mizrahi
- Department of Industrial Engineering and Management, Ariel University, Ariel, Israel
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Lo JH, Huang HP, Sung SC. Working Memory Ability Evaluation Based on Fuzzy Support Vector Regression. SENSORS (BASEL, SWITZERLAND) 2023; 23:8246. [PMID: 37837076 PMCID: PMC10574859 DOI: 10.3390/s23198246] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Revised: 09/27/2023] [Accepted: 09/29/2023] [Indexed: 10/15/2023]
Abstract
One's working memory process is a fundamental cognitive activity which often serves as an indicator of brain disease and cognitive impairment. In this research, the approach to evaluate working memory ability by means of electroencephalography (EEG) analysis was proposed. The result shows that the EEG signals of subjects share some characteristics when performing working memory tasks. Through correlation analysis, a working memory model describes the changes in EEG signals within alpha, beta and gamma waves, which shows an inverse tendency compared to Zen meditation. The working memory ability of subjects can be predicted using multi-linear support vector regression (SVR) with fuzzy C-mean (FCM) clustering and knowledge-based fuzzy support vector regression (FSVR), which reaches the mean square error of 0.6 in our collected data. The latter, designed based on the working memory model, achieves the best performance. The research provides the insight of the working memory process from the EEG aspect to become an example of cognitive function analysis and prediction.
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Affiliation(s)
- Jia-Hsun Lo
- Department of Mechanical Engineering, National Taiwan University, Taipei 10617, Taiwan
| | - Han-Pang Huang
- Department of Mechanical Engineering, National Taiwan University, Taipei 10617, Taiwan
| | - Su-Ching Sung
- Department of Gerontology and Health Care Management, Chang Gung University of Science and Technology, Taoyuan City 33303, Taiwan;
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Martínez Vásquez DA, Posada-Quintero HF, Rivera Pinzón DM. Mutual Information between EDA and EEG in Multiple Cognitive Tasks and Sleep Deprivation Conditions. Behav Sci (Basel) 2023; 13:707. [PMID: 37753985 PMCID: PMC10525564 DOI: 10.3390/bs13090707] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Revised: 08/05/2023] [Accepted: 08/22/2023] [Indexed: 09/28/2023] Open
Abstract
Sleep deprivation, a widespread phenomenon that affects one-third of normal American adults, induces adverse changes in physical and cognitive performance, which in turn increases the occurrence of accidents. Sleep deprivation is known to increase resting blood pressure and decrease muscle sympathetic nerve activity. Monitoring changes in the interplay between the central and autonomic sympathetic nervous system can be a potential indicator of human's readiness to perform tasks that involve a certain level of cognitive load (e.g., driving). The electroencephalogram (EEG) is the standard to assess the brain's activity. The electrodermal activity (EDA) is a reflection of the general state of arousal regulated by the activation of the sympathetic nervous system through sweat gland stimulation. In this work, we calculated the mutual information between EDA and EEG recordings in order to consider linear and non-linear interactions and provide an insight of the relationship between brain activity and peripheral autonomic sympathetic activity. We analyzed EEG and EDA data from ten participants performing four cognitive tasks every two hours during 24 h (12 trials). We decomposed EEG data into delta, theta, alpha, beta, and gamma spectral components, and EDA into tonic and phasic components. The results demonstrate high values of mutual information between the EDA and delta component of EEG, mainly in working memory tasks. Additionally, we found an increase in the theta component of EEG in the presence of fatigue caused by sleep deprivation, the alpha component in tasks demanding inhibition and attention, and the delta component in working memory tasks. In terms of the location of brain activity, most of the tasks report high mutual information in frontal regions in the initial trials, with a trend to decrease and become uniform for all the nine analyzed EEG channels as a consequence of the sleep deprivation effect. Our results evidence the interplay between central and sympathetic nervous activity and can be used to mitigate the consequences of sleep deprivation.
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Affiliation(s)
- David Alejandro Martínez Vásquez
- Electronic Engineering Faculty, Universidad Santo Tomás, Bogotá 110231, Colombia
- Department of Technology, Universidad Pedagógica Nacional, Bogotá 110221, Colombia;
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Jaramillo-Jimenez A, Tovar-Rios DA, Ospina JA, Mantilla-Ramos YJ, Loaiza-López D, Henao Isaza V, Zapata Saldarriaga LM, Cadavid Castro V, Suarez-Revelo JX, Bocanegra Y, Lopera F, Pineda-Salazar DA, Tobón Quintero CA, Ochoa-Gomez JF, Borda MG, Aarsland D, Bonanni L, Brønnick K. Spectral features of resting-state EEG in Parkinson's Disease: A multicenter study using functional data analysis. Clin Neurophysiol 2023; 151:28-40. [PMID: 37146531 DOI: 10.1016/j.clinph.2023.03.363] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2022] [Revised: 02/18/2023] [Accepted: 03/27/2023] [Indexed: 05/07/2023]
Abstract
OBJECTIVE This study aims 1) To analyse differences in resting-state electroencephalogram (rs-EEG) spectral features of Parkinson's Disease (PD) and healthy subjects (non-PD) using Functional Data Analysis (FDA) and 2) To explore, in four independent cohorts, the external validity and reproducibility of the findings using both epoch-to-epoch FDA and averaged-epochs approach. METHODS We included 169 subjects (85 non-PD; 84 PD) from four centres. Rs-EEG signals were preprocessed with a combination of automated pipelines. Sensor-level relative power spectral density (PSD), dominant frequency (DF), and DF variability (DFV) features were extracted. Differences in each feature were compared between PD and non-PD on averaged epochs and using FDA to model the epoch-to-epoch change of each feature. RESULTS For averaged epochs, significantly higher theta relative PSD in PD was found across all datasets. Also, higher pre-alpha relative PSD was observed in three of four datasets in PD patients. For FDA, similar findings were achieved in theta, but all datasets showed consistently significant posterior pre-alpha differences across multiple epochs. CONCLUSIONS Increased generalised theta, with posterior pre-alpha relative PSD, was the most reproducible finding in PD. SIGNIFICANCE Rs-EEG theta and pre-alpha findings are generalisable in PD. FDA constitutes a reliable and powerful tool to analyse epoch-to-epoch the rs-EEG.
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Affiliation(s)
- Alberto Jaramillo-Jimenez
- Centre for Age-Related Medicine (SESAM), Stavanger University Hospital. Stavanger, Norway; Faculty of Health Sciences, University of Stavanger. Stavanger, Norway; Grupo de Neurociencias de Antioquia, Universidad de Antioquia, School of Medicine. Medellín, Colombia; Grupo Neuropsicología y Conducta, Universidad de Antioquia, School of Medicine. Medellín, Colombia; Semillero de Investigación SINAPSIS, Universidad de Antioquia, School of Medicine. Medellín, Colombia; Semillero de Investigación NeuroCo, Universidad de Antioquia, School of Medicine & School of Engenieering. Medellín, Colombia.
| | - Diego A Tovar-Rios
- Centre for Age-Related Medicine (SESAM), Stavanger University Hospital. Stavanger, Norway; Faculty of Health Sciences, University of Stavanger. Stavanger, Norway; Universidad del Valle, Grupo de Investigación en Estadística Aplicada - INFERIR, Faculty of Engineering, Santiago de Cali, Colombia; Universidad del Valle, Prevención y Control de la Enfermedad Crónica - PRECEC, Faculty of Health, Santiago de Cali, Colombia
| | - Johann Alexis Ospina
- Facultad de Ciencias Básicas, Universidad Autónoma de Occidente, Santiago de Cali, Colombia
| | - Yorguin-Jose Mantilla-Ramos
- Grupo Neuropsicología y Conducta, Universidad de Antioquia, School of Medicine. Medellín, Colombia; Semillero de Investigación NeuroCo, Universidad de Antioquia, School of Medicine & School of Engenieering. Medellín, Colombia
| | - Daniel Loaiza-López
- Grupo Neuropsicología y Conducta, Universidad de Antioquia, School of Medicine. Medellín, Colombia; Semillero de Investigación NeuroCo, Universidad de Antioquia, School of Medicine & School of Engenieering. Medellín, Colombia
| | - Verónica Henao Isaza
- Grupo Neuropsicología y Conducta, Universidad de Antioquia, School of Medicine. Medellín, Colombia; Semillero de Investigación NeuroCo, Universidad de Antioquia, School of Medicine & School of Engenieering. Medellín, Colombia
| | - Luisa María Zapata Saldarriaga
- Grupo Neuropsicología y Conducta, Universidad de Antioquia, School of Medicine. Medellín, Colombia; Semillero de Investigación NeuroCo, Universidad de Antioquia, School of Medicine & School of Engenieering. Medellín, Colombia
| | - Valeria Cadavid Castro
- Grupo Neuropsicología y Conducta, Universidad de Antioquia, School of Medicine. Medellín, Colombia; Semillero de Investigación NeuroCo, Universidad de Antioquia, School of Medicine & School of Engenieering. Medellín, Colombia
| | - Jazmin Ximena Suarez-Revelo
- Grupo de Neurociencias de Antioquia, Universidad de Antioquia, School of Medicine. Medellín, Colombia; Grupo Neuropsicología y Conducta, Universidad de Antioquia, School of Medicine. Medellín, Colombia
| | - Yamile Bocanegra
- Grupo de Neurociencias de Antioquia, Universidad de Antioquia, School of Medicine. Medellín, Colombia; Grupo Neuropsicología y Conducta, Universidad de Antioquia, School of Medicine. Medellín, Colombia
| | - Francisco Lopera
- Grupo de Neurociencias de Antioquia, Universidad de Antioquia, School of Medicine. Medellín, Colombia
| | - David Antonio Pineda-Salazar
- Grupo de Neurociencias de Antioquia, Universidad de Antioquia, School of Medicine. Medellín, Colombia; Grupo Neuropsicología y Conducta, Universidad de Antioquia, School of Medicine. Medellín, Colombia
| | - Carlos Andrés Tobón Quintero
- Grupo de Neurociencias de Antioquia, Universidad de Antioquia, School of Medicine. Medellín, Colombia; Grupo Neuropsicología y Conducta, Universidad de Antioquia, School of Medicine. Medellín, Colombia; Área Investigación e Innovación, Hospital Alma Mater de Antioquia. Medellín, Colombia
| | - John Fredy Ochoa-Gomez
- Grupo Neuropsicología y Conducta, Universidad de Antioquia, School of Medicine. Medellín, Colombia
| | - Miguel Germán Borda
- Centre for Age-Related Medicine (SESAM), Stavanger University Hospital. Stavanger, Norway; Faculty of Health Sciences, University of Stavanger. Stavanger, Norway; Semillero de Neurociencias y Envejecimiento, Pontificia Universidad Javeriana, Ageing Institute, Medical School. Bogotá, Colombia
| | - Dag Aarsland
- Centre for Age-Related Medicine (SESAM), Stavanger University Hospital. Stavanger, Norway; Faculty of Health Sciences, University of Stavanger. Stavanger, Norway; Department of Old Age Psychiatry, Institute of Psychiatry, Psychology, and Neuroscience, King's College London. London, UK
| | - Laura Bonanni
- Department of Medicine and Aging Sciences, G. d'Annunzio University. Chieti, Italy
| | - Kolbjørn Brønnick
- Centre for Age-Related Medicine (SESAM), Stavanger University Hospital. Stavanger, Norway; Faculty of Health Sciences, University of Stavanger. Stavanger, Norway
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Using Neural Networks to Uncover the Relationship between Highly Variable Behavior and EEG during a Working Memory Task with Distractors. MATHEMATICS 2022. [DOI: 10.3390/math10111848] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Value-driven attention capture (VDAC) occurs when previously rewarded stimuli capture attention and impair goal-directed behavior. In a working memory (WM) task with VDAC-related distractors, we observe behavioral variability both within and across individuals. Individuals differ in their ability to maintain relevant information and ignore distractions. These cognitive components shift over time with changes in motivation and attention, making it difficult to identify underlying neural mechanisms of individual differences. In this study, we develop the first participant-specific feedforward neural network models of reaction time from neural data during a VDAC WM task. We used short epochs of electroencephalography (EEG) data from 16 participants to develop the feedforward neural network (NN) models of RT aimed at understanding both WM and VDAC. Using general linear models (GLM), we identified 20 EEG features to predict RT across participants (r=0.53±0.08). The linear model was compared to the NN model, which improved the predicted trial-by-trial RT for all participants (r=0.87±0.04). We found that right frontal gamma-band activity and fronto-posterior functional connectivity in the alpha, beta, and gamma bands explain individual differences. Our study shows that NN models can link neural activity to highly variable behavior and can identify potential new targets for neuromodulation interventions.
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Liu S, Mao S. An Intervention Study on Children's Healthy Joint Attention Skills Based on a Mixed Instructional Approach of DTT and PRT. JOURNAL OF HEALTHCARE ENGINEERING 2022; 2022:5987582. [PMID: 35356620 PMCID: PMC8959974 DOI: 10.1155/2022/5987582] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Revised: 02/16/2022] [Accepted: 02/22/2022] [Indexed: 12/14/2022]
Abstract
Joint attention is an important element that influences children's early development of communication and sociality, and joint attention is more often than not the earliest incipient of their prosocial behavior. Joint attention skills are one of the core deficits of children with autism, and identifying and remediating the core problems of autism is a popular area of interest, with joint attention being the focus of attention. The aim of this study was to investigate whether the combined orientation model of Discrete Trial Teaching (DTT) and Pivotal Response Training (PRT) could improve the joint attention skills of children with autism. This study used a cross-behavioral multitest design in a single-subject study with two preschool children with autism as subjects, with the independent variable being joint attention teaching and the dependent variable being the three joint attention skills (eye gaze, following directions, and active display). After the instructional intervention, children with autism showed a significant increase in the correctness of "eye alternation," "following directions," and "moving displays."
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Affiliation(s)
- Shengmin Liu
- Department of Psychology, School of Teacher Education, Huzhou University, Huzhou, Zhejiang 313000, China
| | - Shufang Mao
- Mental Health Education and Guidance Center, Zhejiang Financial College, Hangzhou, Zhejiang 310018, China
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Using a Novel Functional Brain Network Approach to Locate Important Nodes for Working Memory Tasks. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph19063564. [PMID: 35329248 PMCID: PMC8955367 DOI: 10.3390/ijerph19063564] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/22/2022] [Revised: 03/07/2022] [Accepted: 03/14/2022] [Indexed: 02/04/2023]
Abstract
Working Memory (WM) is a short-term memory for processing and storing information. When investigating WM mechanisms using Electroencephalogram (EEG), its rhythmic synchronization properties inevitably become one of the focal features. To further leverage these features for better improve WM task performance, this paper uses a novel algorithm: Weight K-order propagation number (WKPN) to locate important brain nodes and their coupling characteristic in different frequency bands while subjects are proceeding French word retaining tasks, which is an intriguing but original experiment paradigm. Based on this approach, we investigated the node importance of PLV brain networks under different memory loads and found that the connectivity between frontal and parieto-occipital lobes in theta and beta frequency bands enhanced with increasing memory load. We used the node importance of the brain network as a feature vector of the SVM to classify different memory load states, and the highest classification accuracy of 95% is obtained in the beta band. Compared to the Weight degree centrality (WDC) and Weight Page Rank (WPR) algorithm, the SVM with the node importance of the brain network as the feature vector calculated by the WKPN algorithm has higher classification accuracy and shorter running time. It is concluded that the algorithm can effectively spot active central hubs so that researchers can later put more energy to study these areas where active hubs lie in such as placing Transcranial alternating current stimulation (tACS).
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Decision Tree in Working Memory Task Effectively Characterizes EEG Signals in Healthy Aging Adults. Ing Rech Biomed 2021. [DOI: 10.1016/j.irbm.2021.12.001] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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Huang K, Chen D, Wang F, Yang L. Prediction of dispositional dialectical thinking from resting-state electroencephalography. Brain Behav 2021; 11:e2327. [PMID: 34423595 PMCID: PMC8442598 DOI: 10.1002/brb3.2327] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/16/2021] [Revised: 07/20/2021] [Accepted: 08/02/2021] [Indexed: 11/20/2022] Open
Abstract
This study aims to explore the possibility of predicting the dispositional level of dialectical thinking using resting-state electroencephalography signals. Thirty-four participants completed a self-reported measure of dialectical thinking, and their resting-state electroencephalography was recorded. After wave filtration and eye movement removal, time-frequency electroencephalography signals were converted into four frequency domains: delta (1-4 Hz), theta (4-7 Hz), alpha (7-13 Hz), and beta (13-30 Hz). Functional principal component analysis with B-spline approximation was then applied for feature reduction. Five machine learning methods (support vector regression, least absolute shrinkage and selection operator, K-nearest neighbors, random forest, and gradient boosting decision tree) were applied to the reduced features for prediction. The model ensemble technique was used to create the best performing final model. The results showed that the alpha wave of the electroencephalography signal in the early period (12-15 s) contributed most to the prediction of dialectical thinking. With data-driven electrode selection (FC1, FCz, Fz, FC3, Cz, AFz), the prediction model achieved an average coefficient of determination of 0.45 on 200 random test sets. Furthermore, a significant positive correlation was found between the alpha value of standardized low-resolution electromagnetic tomography activity in the right dorsal anterior cingulate cortex and dialectical self-scale score. The prefrontal and midline alpha oscillations of resting electroencephalography are good predictors of the dispositional level of dialectical thinking, possibly reflecting these brain structures' involvement in dialectical thinking.
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Affiliation(s)
- Kun Huang
- Center for Statistical Science and Department of Industrial Engineering, Tsinghua University, Beijing, China
| | - Dian Chen
- Department of Psychology, School of Social Sciences, Tsinghua University, Beijing, China
| | - Fei Wang
- Department of Psychology, School of Social Sciences, Tsinghua University, Beijing, China.,Laboratory of Brain and Intelligence, Tsinghua University, Beijing, China
| | - Lijian Yang
- Center for Statistical Science and Department of Industrial Engineering, Tsinghua University, Beijing, China
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Preshooting Electroencephalographic Activity of Professional Shooters in a Competitive State. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2021; 2021:6639865. [PMID: 33628214 PMCID: PMC7889390 DOI: 10.1155/2021/6639865] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/02/2020] [Revised: 01/02/2021] [Accepted: 01/11/2021] [Indexed: 11/17/2022]
Abstract
This study investigated the influence of competitive state on cerebral cortex activity of professional shooters with 10 m air rifle before shooting. Generally, professional athletes have higher neural efficiency compared with ordinary people. We recruited 11 national shooters to complete 60 shots under both noncompetitive and competitive shooting conditions, and simultaneously collected their electroencephalogram (EEG) and electrocardiogram (ECG) information. Theta, alpha, and beta power were computed in the last three seconds preceding each shot from average-reference 29-channel EEG, while EEG characteristics under two conditions were analyzed. The results showed a significant linear correlation between shooting accuracy and EEG power of anterior frontal, central, temporal, and occipital regions in beta and theta bands. In addition, the theta power in occipital regions, alpha power in frontal-central and left occipital regions, and beta power in frontal and mid-occipital regions were higher than those in noncompetitive state. However, heart rate (HR) and shooting accuracy did not change significantly under the two conditions. These findings reveal the changes of cortical activity underlying competition shooting as well as providing further understanding of the neural mechanisms of the shooting process and lay a foundation for the subsequent neuromodulation research.
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