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Bai J, Sun X, Cao S, Wang Q, Wu J. Exploring the Timing of Disengagement From Nondriving Related Tasks in Scheduled Takeovers With Pre-Alerts: An Analysis of Takeover-Related Measures. HUMAN FACTORS 2024; 66:2669-2690. [PMID: 38207243 DOI: 10.1177/00187208231226052] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/13/2024]
Abstract
OBJECTIVES This study aimed to investigate drivers' disengagement from nondriving related tasks (NDRT) during scheduled takeovers and to evaluate its impact on takeover performance. BACKGROUND During scheduled takeovers, drivers typically have sufficient time to prepare. However, inadequate disengagement from NDRTs can introduce safety risks. METHOD Participants experienced scheduled takeovers using a driving simulator, undergoing two conditions, with and without an NDRT. We assessed their takeover performance and monitored their NDRT disengagement from visual, cognitive, and physical perspectives. RESULTS The study examined three NDRT disengagement timings (DTs): DT1 (disengaged before the takeover request), DT2 (disengaged after the request but before taking over), and DT3 (not disengaged). The impact of NDRT on takeover performance varied depending on DTs. Specifically, DT1 demonstrated no adverse effects; DT2 impaired takeover time, while DT3 impaired both takeover time and quality. Additionally, participants who displayed DT1 exhibited longer eye-off-NDRT duration and a higher eye-off-NDRT count during the prewarning stage compared to those with DT2 and DT3. CONCLUSION Drivers can benefit from earlier disengagement from NDRTs, demonstrating resilience to the adverse effects of NDRTs on takeover performance. The disengagement of cognition is often delayed compared to that of eyes and hands, potentially leading to DT3. Moreover, visual disengagement from NDRTs during the prewarning stage could distinguish DT1 from the other two. APPLICATION Our study emphasizes considering NDRT disengagement in designing systems for scheduled takeovers. Measures should be taken to promote early disengagement, facilitate cognitive disengagement, and employ visual disengagement during the prewarning period as predictive indicators of DTs.
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Affiliation(s)
| | - Xu Sun
- University of Nottingham Ningbo, China
- Nottingham Ningbo China Beacons of Excellence Research and Innovation Institute, China
| | - Shi Cao
- University of Waterloo, Canada
| | - Qingfeng Wang
- Nottingham University Business School China, University of Nottingham, China
| | - Jiang Wu
- University of Nottingham Ningbo, China
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Chen IC, Chang CL, Huang IW, Chang MH, Ko LW. Electrophysiological functional connectivity and complexity reflecting cognitive processing speed heterogeneity in young children with ADHD. Psychiatry Res 2024; 340:116100. [PMID: 39121760 DOI: 10.1016/j.psychres.2024.116100] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/10/2022] [Revised: 05/19/2024] [Accepted: 07/21/2024] [Indexed: 08/12/2024]
Abstract
Early intervention is imperative for young children with attention-deficit/hyperactivity disorder (ADHD) who manifest heterogeneous neurocognitive deficits. The study investigated the functional connectivity and complexity of brain activity among young children with ADHD exhibiting a fast cognitive processing speed (ADHD-F, n = 26), with ADHD exhibiting a slow cognitive processing speed (ADHD-S, n = 17), and typically developing children (n = 35) using wireless electroencephalography (EEG) during rest and task conditions. During rest, compared with the typically developing group, the ADHD-F group displayed lower long-range intra-hemispheric connectivity, while the ADHD-S group had lower frontal beta inter-hemispheric connectivity. During task performance, the ADHD-S group displayed lower frontal beta inter-hemispheric connectivity than the typically developing group. The ADHD-S group had lower frontal inter-hemispheric connectivity in broader frequency bands than the ADHD-F group, indicating ADHD heterogeneity in mental processing speed. Regarding complexity, the ADHD-S group tended to show lower frontal entropy estimators than the typically developing group during the task condition. These findings suggest that the EEG profile of brain connectivity and complexity can aid the early clinical diagnosis of ADHD, support subgrouping young children with ADHD based on cognitive processing speed heterogeneity, and may contain specific novel neural biomarkers for early intervention planning.
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Affiliation(s)
- I-Chun Chen
- Department of Physical Medicine and Rehabilitation, Ton-Yen General Hospital, Hsinchu, Taiwan, ROC; Department of Early Childhood Education and Care, Minghsin University of Science and Technology, Hsinchu, Taiwan, ROC; International Ph.D. Program in Interdisciplinary Neuroscience, College of Biological Science and Technology, National Yang Ming Chiao Tung University, Hsinchu, Taiwan, ROC.
| | | | - I-Wen Huang
- Institute of Biomedical Engineering, National Yang Ming Chiao Tung University, Hsinchu, Taiwan, ROC
| | - Meng-Han Chang
- Department of Psychiatry, Ton-Yen General Hospital, Hsinchu, Taiwan, ROC
| | - Li-Wei Ko
- Department of Electronics and Electrical Engineering, Institute of Electrical and Control Engineering, National Yang Ming Chiao Tung University, Hsinchu, Taiwan, ROC; Center for Intelligent Drug Systems and Smart Bio-devices (IDS2B), National Yang Ming Chiao Tung University, Hsinchu, Taiwan, ROC; Department of Biomedical Science and Environment Biology, Drug Development and Value Creation Research Center, Kaohsiung Medical University, Kaohsiung, Taiwan, ROC.
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Rahnuma T, Jothiraj SN, Kuvar V, Faber M, Knight RT, Kam JWY. Gaze-Based Detection of Thoughts across Naturalistic Tasks Using a PSO-Optimized Random Forest Algorithm. Bioengineering (Basel) 2024; 11:760. [PMID: 39199718 PMCID: PMC11351278 DOI: 10.3390/bioengineering11080760] [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: 06/16/2024] [Revised: 07/13/2024] [Accepted: 07/23/2024] [Indexed: 09/01/2024] Open
Abstract
One key aspect of the human experience is our ongoing stream of thoughts. These thoughts can be broadly categorized into various dimensions, which are associated with different impacts on mood, well-being, and productivity. While the past literature has often identified eye movements associated with a specific thought dimension (task-relatedness) during experimental tasks, few studies have determined if these various thought dimensions can be classified by oculomotor activity during naturalistic tasks. Employing thought sampling, eye tracking, and machine learning, we assessed the classification of nine thought dimensions (task-relatedness, freely moving, stickiness, goal-directedness, internal-external orientation, self-orientation, others orientation, visual modality, and auditory modality) across seven multi-day recordings of seven participants during self-selected computer tasks. Our analyses were based on a total of 1715 thought probes across 63 h of recordings. Automated binary-class classification of the thought dimensions was based on statistical features extracted from eye movement measures, including fixation and saccades. These features all served as input into a random forest (RF) classifier, which was then improved with particle swarm optimization (PSO)-based selection of the best subset of features for classifier performance. The mean Matthews correlation coefficient (MCC) values from the PSO-based RF classifier across the thought dimensions ranged from 0.25 to 0.54, indicating above-chance level performance in all nine thought dimensions across participants and improved performance compared to the RF classifier without feature selection. Our findings highlight the potential of machine learning approaches combined with eye movement measures for the real-time prediction of naturalistic ongoing thoughts, particularly in ecologically valid contexts.
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Affiliation(s)
- Tarannum Rahnuma
- Department of Psychology, University of Calgary, Calgary, AB T2N 1N4, Canada
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB T2N 4N1, Canada
| | - Sairamya Nanjappan Jothiraj
- Department of Psychology, University of Calgary, Calgary, AB T2N 1N4, Canada
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB T2N 4N1, Canada
| | - Vishal Kuvar
- Department of Educational Psychology, University of Minnesota Twin Cities, Minneapolis, MN 55455, USA
| | - Myrthe Faber
- Department of Cognitive Science and Artificial Intelligence, Tilburg University, 5037 AB Tilburg, The Netherlands
- Donders Centre for Cognitive Neuroimaging, Radboud University, 6525 EN Nijmegen, The Netherlands
| | - Robert T. Knight
- Helen Wills Neuroscience Institute, University of California, Berkeley, CA 94720, USA
- Department of Psychology, University of California, Berkeley, CA 94704, USA
| | - Julia W. Y. Kam
- Department of Psychology, University of Calgary, Calgary, AB T2N 1N4, Canada
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB T2N 4N1, Canada
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Tang S, Li Z. EEG complexity measures for detecting mind wandering during video-based learning. Sci Rep 2024; 14:8209. [PMID: 38589498 PMCID: PMC11001605 DOI: 10.1038/s41598-024-58889-9] [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: 01/15/2024] [Accepted: 04/04/2024] [Indexed: 04/10/2024] Open
Abstract
This study explores the efficacy of various EEG complexity measures in detecting mind wandering during video-based learning. Employing a modified probe-caught method, we recorded EEG data from participants engaged in viewing educational videos and subsequently focused on the discrimination between mind wandering (MW) and non-MW states. We systematically investigated various EEG complexity metrics, including metrics that reflect a system's regularity like multiscale permutation entropy (MPE), and metrics that reflect a system's dimensionality like detrended fluctuation analysis (DFA). We also compare these features to traditional band power (BP) features. Data augmentation methods and feature selection were applied to optimize detection accuracy. Results show BP features excelled (mean area under the receiver operating characteristic curve (AUC) 0.646) in datasets without eye-movement artifacts, while MPE showed similar performance (mean AUC 0.639) without requiring removal of eye-movement artifacts. Combining all kinds of features improved decoding performance to 0.66 mean AUC. Our findings demonstrate the potential of these complexity metrics in EEG analysis for mind wandering detection, highlighting their practical implications in educational contexts.
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Affiliation(s)
- Shaohua Tang
- School of Systems Science, Beijing Normal University, Beijing, China
- International Academic Center of Complex Systems, Beijing Normal University, Zhuhai, China
- Center for Cognition and Neuroergonomics, State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Zhuhai, China
| | - Zheng Li
- Center for Cognition and Neuroergonomics, State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Zhuhai, China.
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Tang S, Liang Y, Li Z. Mind wandering state detection during video-based learning via EEG. Front Hum Neurosci 2023; 17:1182319. [PMID: 37323927 PMCID: PMC10267732 DOI: 10.3389/fnhum.2023.1182319] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Accepted: 05/16/2023] [Indexed: 06/17/2023] Open
Abstract
The aim of this study is to explore the potential of technology for detecting mind wandering, particularly during video-based distance learning, with the ultimate benefit of improving learning outcomes. To overcome the challenges of previous mind wandering research in ecological validity, sample balance, and dataset size, this study utilized practical electroencephalography (EEG) recording hardware and designed a paradigm consisting of viewing short-duration video lectures under a focused learning condition and a future planning condition. Participants estimated statistics of their attentional state at the end of each video, and we combined this rating scale feedback with self-caught key press responses during video watching to obtain binary labels for classifier training. EEG was recorded using an 8-channel system, and spatial covariance features processed by Riemannian geometry were employed. The results demonstrate that a radial basis function kernel support vector machine classifier, using Riemannian-processed covariance features from delta, theta, alpha, and beta bands, can detect mind wandering with a mean area under the receiver operating characteristic curve (AUC) of 0.876 for within-participant classification and AUC of 0.703 for cross-lecture classification. Furthermore, our results suggest that a short duration of training data is sufficient to train a classifier for online decoding, as cross-lecture classification remained at an average AUC of 0.689 when using 70% of the training set (about 9 min). The findings highlight the potential for practical EEG hardware in detecting mind wandering with high accuracy, which has potential application to improving learning outcomes during video-based distance learning.
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Affiliation(s)
- Shaohua Tang
- School of Systems Science, Beijing Normal University, Beijing, China
- International Academic Center of Complex Systems, Beijing Normal University, Zhuhai, China
- Center for Cognition and Neuroergonomics, State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Zhuhai, China
| | - Yutong Liang
- Center for Cognition and Neuroergonomics, State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Zhuhai, China
| | - Zheng Li
- Center for Cognition and Neuroergonomics, State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Zhuhai, China
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Pavlovian-based neurofeedback enhances meta-awareness of mind-wandering. Neural Netw 2023; 158:239-248. [PMID: 36473291 DOI: 10.1016/j.neunet.2022.11.024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2021] [Revised: 10/30/2022] [Accepted: 11/15/2022] [Indexed: 11/22/2022]
Abstract
Absorption in mind-wandering (MW) may worsen our mood and can cause psychological disorders. Researchers indicate the possibility that meta-awareness of MW prevents these mal-effects and enhances favorable consequences of MW, such as boosting creativity; thus, meta-awareness has attracted psychological and clinical attention. However, few studies have investigated the nature of meta-awareness of MW, because there has been no method to isolate and operate this ability. Therefore, we propose a new approach to manipulate the ability of meta-awareness. We used Pavlovian conditioning, tying to it an occurrence of MW and a neutral tone sound inducing the meta-awareness of MW. To perform paired presentations of the unconditioned stimulus (neutral tone) and the conditioned stimulus (perception accompanying MW), we detected participants' natural occurrence of MW via electroencephalogram and a machine-learning estimation method. The double-blinded randomized controlled trial with 37 participants found that a single 20-min conditioning session significantly increased the meta-awareness of MW as assessed by behavioral and neuroscientific measures. The core protocol of the proposed method is real-time feedback on participants' neural information, and in that sense, we can refer to it as neurofeedback. However, there are some differences from typical neurofeedback protocols, and we discuss them in this paper. Our novel classical conditioning is expected to contribute to future research on the modulation effect of meta-awareness on MW.
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