1
|
Hernández-Mustieles MA, Lima-Carmona YE, Pacheco-Ramírez MA, Mendoza-Armenta AA, Romero-Gómez JE, Cruz-Gómez CF, Rodríguez-Alvarado DC, Arceo A, Cruz-Garza JG, Ramírez-Moreno MA, Lozoya-Santos JDJ. Wearable Biosensor Technology in Education: A Systematic Review. SENSORS (BASEL, SWITZERLAND) 2024; 24:2437. [PMID: 38676053 PMCID: PMC11054421 DOI: 10.3390/s24082437] [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: 03/07/2024] [Revised: 03/31/2024] [Accepted: 04/02/2024] [Indexed: 04/28/2024]
Abstract
Wearable Biosensor Technology (WBT) has emerged as a transformative tool in the educational system over the past decade. This systematic review encompasses a comprehensive analysis of WBT utilization in educational settings over a 10-year span (2012-2022), highlighting the evolution of this field to address challenges in education by integrating technology to solve specific educational challenges, such as enhancing student engagement, monitoring stress and cognitive load, improving learning experiences, and providing real-time feedback for both students and educators. By exploring these aspects, this review sheds light on the potential implications of WBT on the future of learning. A rigorous and systematic search of major academic databases, including Google Scholar and Scopus, was conducted in accordance with the PRISMA guidelines. Relevant studies were selected based on predefined inclusion and exclusion criteria. The articles selected were assessed for methodological quality and bias using established tools. The process of data extraction and synthesis followed a structured framework. Key findings include the shift from theoretical exploration to practical implementation, with EEG being the predominant measurement, aiming to explore mental states, physiological constructs, and teaching effectiveness. Wearable biosensors are significantly impacting the educational field, serving as an important resource for educators and a tool for students. Their application has the potential to transform and optimize academic practices through sensors that capture biometric data, enabling the implementation of metrics and models to understand the development and performance of students and professors in an academic environment, as well as to gain insights into the learning process.
Collapse
Affiliation(s)
- María A. Hernández-Mustieles
- Mechatronics Department, School of Engineering and Sciences, Monterrey Campus, Tecnologico de Monterrey, Monterrey 64700, Mexico; (M.A.H.-M.); (Y.E.L.-C.); (M.A.P.-R.); (A.A.M.-A.); (C.F.C.-G.); (D.C.R.-A.); (A.A.); (M.A.R.-M.)
| | - Yoshua E. Lima-Carmona
- Mechatronics Department, School of Engineering and Sciences, Monterrey Campus, Tecnologico de Monterrey, Monterrey 64700, Mexico; (M.A.H.-M.); (Y.E.L.-C.); (M.A.P.-R.); (A.A.M.-A.); (C.F.C.-G.); (D.C.R.-A.); (A.A.); (M.A.R.-M.)
| | - Maxine A. Pacheco-Ramírez
- Mechatronics Department, School of Engineering and Sciences, Monterrey Campus, Tecnologico de Monterrey, Monterrey 64700, Mexico; (M.A.H.-M.); (Y.E.L.-C.); (M.A.P.-R.); (A.A.M.-A.); (C.F.C.-G.); (D.C.R.-A.); (A.A.); (M.A.R.-M.)
| | - Axel A. Mendoza-Armenta
- Mechatronics Department, School of Engineering and Sciences, Monterrey Campus, Tecnologico de Monterrey, Monterrey 64700, Mexico; (M.A.H.-M.); (Y.E.L.-C.); (M.A.P.-R.); (A.A.M.-A.); (C.F.C.-G.); (D.C.R.-A.); (A.A.); (M.A.R.-M.)
| | - José Esteban Romero-Gómez
- Mechatronics Department, School of Engineering and Sciences, Guadalajara Campus, Tecnologico de Monterrey, Guadalajara 45201, Mexico;
| | - César F. Cruz-Gómez
- Mechatronics Department, School of Engineering and Sciences, Monterrey Campus, Tecnologico de Monterrey, Monterrey 64700, Mexico; (M.A.H.-M.); (Y.E.L.-C.); (M.A.P.-R.); (A.A.M.-A.); (C.F.C.-G.); (D.C.R.-A.); (A.A.); (M.A.R.-M.)
| | - Diana C. Rodríguez-Alvarado
- Mechatronics Department, School of Engineering and Sciences, Monterrey Campus, Tecnologico de Monterrey, Monterrey 64700, Mexico; (M.A.H.-M.); (Y.E.L.-C.); (M.A.P.-R.); (A.A.M.-A.); (C.F.C.-G.); (D.C.R.-A.); (A.A.); (M.A.R.-M.)
| | - Alejandro Arceo
- Mechatronics Department, School of Engineering and Sciences, Monterrey Campus, Tecnologico de Monterrey, Monterrey 64700, Mexico; (M.A.H.-M.); (Y.E.L.-C.); (M.A.P.-R.); (A.A.M.-A.); (C.F.C.-G.); (D.C.R.-A.); (A.A.); (M.A.R.-M.)
| | - Jesús G. Cruz-Garza
- Department of Neurosurgery, Houston Methodist Research Institute, Houston, TX 77030, USA;
| | - Mauricio A. Ramírez-Moreno
- Mechatronics Department, School of Engineering and Sciences, Monterrey Campus, Tecnologico de Monterrey, Monterrey 64700, Mexico; (M.A.H.-M.); (Y.E.L.-C.); (M.A.P.-R.); (A.A.M.-A.); (C.F.C.-G.); (D.C.R.-A.); (A.A.); (M.A.R.-M.)
| | - Jorge de J. Lozoya-Santos
- Mechatronics Department, School of Engineering and Sciences, Monterrey Campus, Tecnologico de Monterrey, Monterrey 64700, Mexico; (M.A.H.-M.); (Y.E.L.-C.); (M.A.P.-R.); (A.A.M.-A.); (C.F.C.-G.); (D.C.R.-A.); (A.A.); (M.A.R.-M.)
| |
Collapse
|
2
|
Husselman TA, Filho E, Zugic LW, Threadgold E, Ball LJ. Stimulus Complexity Can Enhance Art Appreciation: Phenomenological and Psychophysiological Evidence for the Pleasure-Interest Model of Aesthetic Liking. J Intell 2024; 12:42. [PMID: 38667709 PMCID: PMC11051202 DOI: 10.3390/jintelligence12040042] [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: 01/05/2024] [Revised: 03/11/2024] [Accepted: 03/26/2024] [Indexed: 04/28/2024] Open
Abstract
We tested predictions deriving from the "Pleasure-Interest Model of Aesthetic Liking" (PIA Model), whereby aesthetic preferences arise from two fluency-based processes: an initial automatic, percept-driven default process and a subsequent perceiver-driven reflective process. One key trigger for reflective processing is stimulus complexity. Moreover, if meaning can be derived from such complexity, then this can engender increased interest and elevated liking. Experiment 1 involved graffiti street-art images, pre-normed to elicit low, moderate and high levels of interest. Subjective reports indicated a predicted enhancement in liking across increasing interest levels. Electroencephalography (EEG) recordings during image viewing revealed different patterns of alpha power in temporal brain regions across interest levels. Experiment 2 enforced a brief initial image-viewing stage and a subsequent reflective image-viewing stage. Differences in alpha power arose in most EEG channels between the initial and deliberative viewing stages. A linear increase in aesthetic liking was again seen across interest levels, with different patterns of alpha activity in temporal and occipital regions across these levels. Overall, the phenomenological data support the PIA Model, while the physiological data suggest that enhanced aesthetic liking might be associated with "flow-feelings" indexed by alpha activity in brain regions linked to visual attention and reducing distraction.
Collapse
Affiliation(s)
- Tammy-Ann Husselman
- School of Psychology & Neuroscience, University of Glasgow, 62 Hillhead Street, Glasgow G12 8QB, UK;
| | - Edson Filho
- Wheelock College of Education & Human Development, Boston University, 2 Silber Way, Boston, MA 02215, USA;
| | - Luca W. Zugic
- School of Psychology & Humanities, University of Central Lancashire, Fylde Road, Preston PR1 8TY, UK (E.T.)
| | - Emma Threadgold
- School of Psychology & Humanities, University of Central Lancashire, Fylde Road, Preston PR1 8TY, UK (E.T.)
| | - Linden J. Ball
- School of Psychology & Humanities, University of Central Lancashire, Fylde Road, Preston PR1 8TY, UK (E.T.)
| |
Collapse
|
3
|
Zhou W, Wu X. The impact of internal-generated contextual clues on EFL vocabulary learning: insights from EEG. Front Psychol 2024; 15:1332098. [PMID: 38371709 PMCID: PMC10873923 DOI: 10.3389/fpsyg.2024.1332098] [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: 11/24/2023] [Accepted: 01/17/2024] [Indexed: 02/20/2024] Open
Abstract
With the popularity of learning vocabulary online among English as a Foreign Language (EFL) learners today, educators and researchers have been considering ways to enhance the effectiveness of this approach. Prior research has underscored the significance of contextual clues in vocabulary acquisition. However, few studies have compared the context provided by instructional materials and that generated by learners themselves. Hence, this present study sought to explore the impact of internal-generated contextual clues in comparison to those provided by instructional materials on EFL learners' online vocabulary acquisition. A total of 26 university students were enrolled and underwent electroencephalography (EEG). Based on a within-subjects design, all participants learned two groups of vocabulary words through a series of video clips under two conditions: one where the contexts were externally provided and the other where participants themselves generated the contexts. In this regard, participants were tasked with either viewing contextual clues presented on the screen or creating their own contextual clues for word comprehension. EEG signals were recorded during the learning process to explore neural activities, and post-tests were conducted to assess learning performance after each vocabulary learning session. Our behavioral results indicated that comprehending words with internal-generated contextual clues resulted in superior learning performance compared to using context provided by instructional materials. Furthermore, EEG data revealed that learners expended greater cognitive resources and mental effort in semantically integrating the meaning of words when they self-created contextual clues, as evidenced by stronger alpha and beta-band oscillations. Moreover, the stronger alpha-band oscillations and lower inter-subject correlation (ISC) among learners suggested that the generative task of creating context enhanced their top-down attentional control mechanisms and selective visual processing when learning vocabulary from videos. These findings underscored the positive effects of internal-generated contextual clues, indicating that instructors should encourage learners to construct their own contexts in online EFL vocabulary instruction rather than providing pre-defined contexts. Future research should aim to explore the limits and conditions of employing these two types of contextual clues in online EFL vocabulary learning. This could be achieved by manipulating the quality and understandability of contexts and considering learners' language proficiency levels.
Collapse
Affiliation(s)
- Weichen Zhou
- School of Teacher Education, Shaoxing University, Shaoxing, China
| | - Xia Wu
- Department of Psychology, Shaoxing University, Shaoxing, China
| |
Collapse
|
4
|
Yu H, Xu M, Xiao X, Xu F, Ming D. Detection of dynamic changes of electrodermal activity to predict the classroom performance of college students. Cogn Neurodyn 2024; 18:173-184. [PMID: 38406194 PMCID: PMC10881450 DOI: 10.1007/s11571-023-09930-6] [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: 09/02/2022] [Revised: 12/02/2022] [Accepted: 01/09/2023] [Indexed: 02/20/2023] Open
Abstract
It is emphasized in the Self-regulated learning (SRL) framework that self-monitoring of learning state is vital for students to keep effective in studying. However, it's still challenging to get an accurate and timely understanding of their learning states during classes. In this study, we propose to use electrodermal activity (EDA) signals which are deemed to be associated with physiological arousal state to predict the college student's classroom performance. Twenty college students were recruited to attend eight lectures in the classroom, during which their EDA signals were recorded simultaneously. For each lecture, the students should complete pre- and after-class tests, and a self-reported scale (SRS) on their learning experience. EDA indices were extracted from both time and frequency domains, and they were furtherly mapped to the student's learning efficiency. As a result, the indices relevant to the dynamic changes of EDA had significant positive correlations with the learning efficiency. Furthermore, compared with only using SRS, a combination with EDA indices had significantly higher accuracy in predicting the learning efficiency. In conclusion, our findings demonstrate that the EDA dynamics are sensitive to the changes in learning efficiency, suggesting a promising approach to predicting the classroom performance of college students.
Collapse
Affiliation(s)
- Haiqing Yu
- Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin, China
| | - Minpeng Xu
- Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin, China
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
| | - Xiaolin Xiao
- Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin, China
| | - Fangzhou Xu
- Department of Physics, School of Electronic and Information Engineering, Qilu University of Technology, Jinan, Shandong China
| | - Dong Ming
- Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin, China
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
| |
Collapse
|
5
|
Massaeli F, Power SD. EEG-based hierarchical classification of level of demand and modality of auditory and visual sensory processing. J Neural Eng 2024; 21:016008. [PMID: 38176028 DOI: 10.1088/1741-2552/ad1ac1] [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: 05/23/2023] [Accepted: 01/04/2024] [Indexed: 01/06/2024]
Abstract
Objective.To date, most research on electroencephalography (EEG)-based mental workload detection for passive brain-computer interface (pBCI) applications has focused on identifying the overall level of cognitive resources required, such as whether the workload is high or low. We propose, however, that being able to determine the specific type of cognitive resources being used, such as visual or auditory, would also be useful. This would enable the pBCI to take more appropriate action to reduce the overall level of cognitive demand on the user. For example, if a high level of workload was detected and it is determined that the user is primarily engaged in visual information processing, then the pBCI could cause some information to be presented aurally instead. In our previous work we showed that EEG could be used to differentiate visual from auditory processing tasks when the level of processing is high, but the two modalities could not be distinguished when the level of cognitive processing demand was very low. The current study aims to build on this work and move toward the overall objective of developing a pBCI that is capable of predicting both the level and the type of cognitive resources being used.Approach.Fifteen individuals undertook carefully designed visual and auditory tasks while their EEG data was being recorded. In this study, we incorporated a more diverse range of sensory processing conditions including not only single-modality conditions (i.e. those requiring one of either visual or auditory processing) as in our previous study, but also dual-modality conditions (i.e. those requiring both visual and auditory processing) and no-task/baseline conditions (i.e. when the individual is not engaged in either visual or auditory processing).Main results.Using regularized linear discriminant analysis within a hierarchical classification algorithm, the overall cognitive demand was predicted with an accuracy of more than 86%, while the presence or absence of visual and auditory sensory processing were each predicted with an accuracy of approximately 70%.Significance.The findings support the feasibility of establishing a pBCI that can determine both the level and type of attentional resources required by the user at any given moment. This pBCI could assist in enhancing safety in hazardous jobs by triggering the most effective and efficient adaptation strategies when high workload conditions are detected.
Collapse
Affiliation(s)
- Faghihe Massaeli
- Faculty of Engineering and Applied Science, Memorial University of Newfoundland, St. Johns, Canada
| | - Sarah D Power
- Faculty of Engineering and Applied Science, Memorial University of Newfoundland, St. Johns, Canada
- Faculty of Medicine, Memorial University of Newfoundland, St. Johns, Canada
| |
Collapse
|
6
|
Soni S, Overton J, Kam JWY, Pexman P, Prabhu A, Garza N, Saez I, Girgis F. Intracranial recordings reveal high-frequency activity in the human temporal-parietal cortex supporting non-literal language processing. Front Neurosci 2024; 17:1304031. [PMID: 38260011 PMCID: PMC10800947 DOI: 10.3389/fnins.2023.1304031] [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: 09/28/2023] [Accepted: 12/26/2023] [Indexed: 01/24/2024] Open
Abstract
Objective Non-literal expressions such as sarcasm, metaphor and simile refer to words and sentences that convey meanings or intentions that are different and more abstract than literal expressions. Neuroimaging studies have shown activations in a variety of frontal, parietal and temporal brain regions implicated in non-literal language processing. However, neurophysiological correlates of these brain areas underlying non-literal processing remain underexplored. Methods To address this, we investigated patterns of intracranial EEG activity during non-literal processing by leveraging a unique patient population. Seven neurosurgical patients with invasive electrophysiological monitoring of superficial brain activity were recruited. Intracranial neural responses were recorded over the temporal-parietal junction (TPJ) and its surrounding areas while patients performed a language task. Participants listened to vignettes that ended with non-literal or literal statements and were then asked related questions to which they responded verbally. Results We found differential neurophysiological activity during the processing of non-literal statements as compared to literal statements, especially in low-Gamma (30-70 Hz) and delta (1-4 Hz) bands. In addition, we found that neural responses related to non-literal processing in the high-gamma band (>70 Hz) were significantly more prominent at TPJ electrodes as compared to non-TPJ (i.e., control) electrodes in most subjects. Moreover, in half of patients, high-gamma activity related to non-literal processing was accompanied by delta-band modulation. Conclusion These results suggest that both low- and high-frequency electrophysiological activities in the temporal-parietal junction play a crucial role during non-literal language processing in the human brain. The current investigation, utilizing better spatial and temporal resolution of human intracranial electrocorticography, provides a unique opportunity to gain insights into the localized brain dynamics of the TPJ during the processing of non-literal language expressions.
Collapse
Affiliation(s)
- Shweta Soni
- Department of Clinical Neurosciences, University of Calgary, Calgary, AB, Canada
| | - Jacqueline Overton
- Department of Neuroscience and Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Julia W. Y. Kam
- Department of Psychology, University of Calgary, Calgary, AB, Canada
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
| | - Penny Pexman
- Department of Psychology, University of Calgary, Calgary, AB, Canada
| | - Akshay Prabhu
- Department of Neurological Surgery, University of California, Davis, Davis, CA, United States
| | - Nicholas Garza
- Department of Neurological Surgery, University of California, Davis, Davis, CA, United States
| | - Ignacio Saez
- Department of Neuroscience, Neurosurgery and Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Fady Girgis
- Department of Clinical Neurosciences, University of Calgary, Calgary, AB, Canada
- Department of Neurological Surgery, University of California, Davis, Davis, CA, United States
| |
Collapse
|
7
|
Chung-Fat-Yim A, Bobb SC, Hoshino N, Marian V. Bilingualism Alters the Neural Correlates of Sustained Attention. TRANSLATIONAL ISSUES IN PSYCHOLOGICAL SCIENCE 2023; 9:409-421. [PMID: 38312330 PMCID: PMC10836257 DOI: 10.1037/tps0000373] [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] [Indexed: 02/06/2024]
Abstract
The present study examined whether monolingual and bilingual language experience -- including first and second language proficiency, exposure, and age of acquisition -- modify the neural mechanisms of attention during nonverbal sound discrimination. English monolinguals and Korean-English bilinguals performed an auditory two-stimulus oddball task while their EEG was recorded. Participants heard a series of two different tones (high pitch tone versus low pitch tone), one of which occurred less frequently (deviant trials) than the other (standard trials), and were asked to mentally count the number of infrequent tones. We found that in the early time window, bilinguals had larger amplitudes than monolinguals in response to both standard and deviant trials, suggesting that bilinguals initially increased attention to identify which of the two tones they heard. In the later time window, however, bilinguals had a smaller ERP effect (deviant minus standard trials) relative to monolinguals, suggesting that bilinguals used fewer cognitive resources for the infrequent stimuli at later stages of processing. Furthermore, across the entire sample, increased exposure to the native language led to larger early, middle, and late ERP effects. These results suggest that native language exposure shapes perceptual processes involved in detection and monitoring. Knowing more than one language may alter sustained attentional processes, with implications for perception and learning.
Collapse
Affiliation(s)
| | | | | | - Viorica Marian
- Northwestern University, Evanston, Illinois, United States
| |
Collapse
|
8
|
Zadina JN. The Synergy Zone: Connecting the Mind, Brain, and Heart for the Ideal Classroom Learning Environment. Brain Sci 2023; 13:1314. [PMID: 37759915 PMCID: PMC10526388 DOI: 10.3390/brainsci13091314] [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: 06/29/2023] [Revised: 09/08/2023] [Accepted: 09/09/2023] [Indexed: 09/29/2023] Open
Abstract
This paper proposes a new perspective on implementing neuroeducation in the classroom. The pandemic exacerbated the mental health issues of faculty and students, creating a mental health crisis that impairs learning. It is important to get our students back in "the zone", both cognitively and emotionally, by creating an ideal learning environment for capturing our students and keeping them-the Synergy Zone. Research that examines the classroom environment often focuses on the foreground-instructors' organizational and instructional aspects and content. However, the emotional climate of the classroom affects student well-being. This emotional climate would ideally exhibit the brain states of engagement, attention, connection, and enjoyment by addressing the mind, brain, and heart. This ideal learning environment would be achieved by combining proposed practices derived from three areas of research: flow theory, brain synchronization, and positive emotion with heart engagement. Each of these enhances the desired brain states in a way that the whole is greater than the sum of the individual parts. I call this the Synergy Zone. A limitation of this proposed model is that implementation of some aspects may be challenging, and professional development resources might be needed. This essay presenting this perspective provides the relevant scientific research and the educational implications of implementation.
Collapse
Affiliation(s)
- Janet N Zadina
- Brain Research and Instruction, New Orleans, LA 70002, USA
| |
Collapse
|
9
|
Privitera AJ, Ng SHS, Chen SHA. Defining the Science of Learning: A scoping review. Trends Neurosci Educ 2023; 32:100206. [PMID: 37689432 DOI: 10.1016/j.tine.2023.100206] [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: 07/06/2023] [Revised: 08/06/2023] [Accepted: 08/07/2023] [Indexed: 09/11/2023]
Abstract
BACKGROUND Interest in research on the Science of Learning continues to grow. However, ambiguity about what this field is can negatively impact communication and collaboration and may inadequately inform educational training programs or funding initiatives that are not sufficiently inclusive in focus. METHODS The present scoping review aimed to synthesize a working definition of the Science of Learning using Web of Science and ProQuest database searches. RESULTS In total, 43 unique definitions were identified across 50 documents including journal articles, theses, conference papers, and book chapters. Definitions of the Science of Learning differed considerably when describing the fields thought to contribute to research on this topic. CONCLUSIONS Based on findings, we propose a working definition of the Science of Learning for discussion and further refinement: the scientific study of the underlying bases of learning with the goal of describing, understanding, or improving learning across developmental stages and diverse contexts.
Collapse
Affiliation(s)
- A J Privitera
- Centre for Research and Development in Learning, Nanyang Technological University, Singapore.
| | - S H S Ng
- Centre for Research and Development in Learning, Nanyang Technological University, Singapore; Institute for Pedagogical Innovation, Research and Excellence, Nanyang Technological University, Singapore
| | - S H A Chen
- Centre for Research and Development in Learning, Nanyang Technological University, Singapore; School of Social Sciences, Nanyang Technological University, Singapore; National Institute of Education, Nanyang Technological University, Singapore; Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore
| |
Collapse
|
10
|
Achanccaray D, Sumioka H. Analysis of Physiological Response of Attention and Stress States in Teleoperation Performance of Social Tasks. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-4. [PMID: 38083262 DOI: 10.1109/embc40787.2023.10340007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
Some studies addressed monitoring mental states by physiological responses analysis in robots' teleoperation in traditional applications such as inspection and exploration; however, no study analyzed the physiological response during teleoperated social tasks to the best of our knowledge. We analyzed the physiological response of attention and stress mental states by computing the correlation between multimodal biomarkers and performance, pleasure-arousal scale, and workload. Physiological data were recorded during simulated teleoperated social tasks to induce mental states, such as normal, attention, and stress. The results showed that task performance and workload subscales achieved moderate correlations with some multimodal biomarkers. The correlations depended on the induced state. The cognitive workload was related to brain biomarkers of attention in the frontal and frontal-central regions. These regions were close to the frontopolar region, which is commonly reported in attentional studies. Thus, some multimodal biomarkers of attention and stress mental states could monitor or predict metrics related to the performance in teleoperation of social tasks.
Collapse
|
11
|
Matheus A, Samer G, Lu W, Nengah H, Samantha W, Carl SY, Reena M. Machine learning polysomnographically-derived electroencephalography biomarkers predictive of epworth sleepiness scale. Sci Rep 2023; 13:9120. [PMID: 37277423 DOI: 10.1038/s41598-023-34716-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2022] [Accepted: 05/05/2023] [Indexed: 06/07/2023] Open
Abstract
Excessive daytime sleepiness (EDS) causes difficulty in concentrating and continuous fatigue during the day. In the clinical setting, the assessment and diagnosis of EDS rely mostly on subjective questionnaires and verbal reports, which compromises the reliability of clinical diagnosis and the ability to robustly discern candidacy for available therapies and track treatment response. In this study, we used a computational pipeline for the automated, rapid, high-throughput, and objective analysis of previously collected encephalography (EEG) data to identify surrogate biomarkers for EDS, thereby defining the quantitative EEG changes in individuals with high Epworth Sleepiness Scale (ESS) (n = 31), compared to a group of individuals with low ESS (n = 41) at the Cleveland Clinic. The epochs of EEG analyzed were extracted from a large overnight polysomnogram registry during the most proximate period of wakefulness. Signal processing of EEG showed significantly different EEG features in the low ESS group compared to high ESS, including enhanced power in the alpha and beta bands and attenuation in the delta and theta bands. Our machine learning (ML) algorithms trained on the binary classification of high vs. low ESS reached an accuracy of 80.2%, precision of 79.2%, recall of 73.8% and specificity of 85.3%. Moreover, we ruled out the effects of confounding clinical variables by evaluating the statistical contribution of these variables on our ML models. These results indicate that EEG data contain information in the form of rhythmic activity that could be leveraged for the quantitative assessment of EDS using ML.
Collapse
Affiliation(s)
- Araujo Matheus
- Sleep Disorders Center, Neurological Institute, Cleveland Clinic Foundation, Cleveland, OH, USA
| | - Ghosn Samer
- Department of Biomedical Engineering, Cleveland Clinic Foundation, Cleveland, OH, USA
| | - Wang Lu
- Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic Foundation, Cleveland, OH, USA
| | | | - Wells Samantha
- Sleep Disorders Center, Neurological Institute, Cleveland Clinic Foundation, Cleveland, OH, USA
| | - Saab Y Carl
- Department of Biomedical Engineering, Cleveland Clinic Foundation, Cleveland, OH, USA
- Department of Biomedical Engineering, Brown University, Providence, RI, USA
| | - Mehra Reena
- Sleep Disorders Center, Neurological Institute, Cleveland Clinic Foundation, Cleveland, OH, USA.
- Respiratory Institute, Cleveland Clinic Foundation, Cleveland, OH, USA.
- Cardiovascular and Metabolic Sciences, Lerner Research Institute, Cleveland Clinic Foundation, Cleveland, OH, USA.
- Heart and Vascular Institute, Cleveland Clinic Foundation, Cleveland, OH, USA.
| |
Collapse
|
12
|
Abdollahzade Z, Hadian MR, Khanmohammadi R, Talebian S. Efficacy of stretching exercises versus transcranial direct current stimulation (tDCS) on task performance, kinematic and electroencephalography (EEG) spectrum in subjects with slump posture: a study protocol. Trials 2023; 24:351. [PMID: 37221565 DOI: 10.1186/s13063-023-07359-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2022] [Accepted: 05/05/2023] [Indexed: 05/25/2023] Open
Abstract
BACKGROUND Slump sitting is a common posture in workplaces. There is limited evidence that poor posture impacts the mental state. This study aims to investigate whether slump posture results in more mental fatigue during computer typing, compared with normal posture and also to compare the effectiveness of stretching exercises with tDCS in fatigue monitoring. METHODS The sample size for this study is set at 36 participants with slump posture and 36 participants with normal posture. In the first step, to find out the differences between normal and poor posture, they will be asked to perform the typewriting task for 60 min. During the first and last 3 min of typing, mental fatigue as the primary outcome using EEG signals and further measures including kinematic behavior of neck, visual analog fatigue scale, and musculoskeletal discomfort will be assessed. Post-experiment task performance will be calculated based on typing speed and typing errors. In the next step, to compare the effect of tDCS and stretching exercises on the outcome measures, the slump posture group will receive these interventions in two separate sessions before the typing task. DISCUSSION With the assumption of showing significant differences in terms of outcome measures between slump and normal posture groups and also by showing the possible changes of the measures, by using either tDCS as a central modality or stretching exercises as a peripheral modality; the findings may provide evidence to indicate that poor posture has adverse effect on mental state and to introduce the effective method to overcome mental fatigue and promote work productivity. TRIAL REGISTRATION Registered on the Iranian Registry of Clinical Trials on 21 September 2022, IRCT Identifier: IRCT20161026030516N2.
Collapse
Affiliation(s)
- Zahra Abdollahzade
- Department of Physiotherapy, School of Rehabilitation, Tehran University of Medical Sciences, Tehran, Iran
| | - Mohammad Reza Hadian
- Department of Physiotherapy, School of Rehabilitation, Tehran University of Medical Sciences, Tehran, Iran.
| | - Roya Khanmohammadi
- Department of Physiotherapy, School of Rehabilitation, Tehran University of Medical Sciences, Tehran, Iran
| | - Saeed Talebian
- Department of Physiotherapy, School of Rehabilitation, Tehran University of Medical Sciences, Tehran, Iran
| |
Collapse
|
13
|
Chen J, Qian P, Gao X, Li B, Zhang Y, Zhang D. Inter-brain coupling reflects disciplinary differences in real-world classroom learning. NPJ SCIENCE OF LEARNING 2023; 8:11. [PMID: 37130852 PMCID: PMC10154329 DOI: 10.1038/s41539-023-00162-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/11/2022] [Accepted: 04/06/2023] [Indexed: 05/04/2023]
Abstract
The classroom is the primary site for learning. A vital feature of classroom learning is the division of educational content into various disciplines. While disciplinary differences could substantially influence the learning process toward success, little is known about the neural mechanism underlying successful disciplinary learning. In the present study, wearable EEG devices were used to record a group of high school students during their classes of a soft (Chinese) and a hard (Math) discipline throughout one semester. Inter-brain coupling analysis was conducted to characterize students' classroom learning process. The students with higher scores in the Math final exam were found to have stronger inter-brain couplings to the class (i.e., all the other classmates), whereas the students with higher scores in Chinese were found to have stronger inter-brain couplings to the top students in the class. These differences in inter-brain couplings were also reflected in distinct dominant frequencies for the two disciplines. Our results illustrate disciplinary differences in the classroom learning from an inter-brain perspective, suggesting that an individual's inter-brain coupling to the class and to the top students could serve as potential neural correlates for successful learning in hard and soft disciplines correspondingly.
Collapse
Affiliation(s)
- Jingjing Chen
- Department of Psychology, School of Social Sciences, Tsinghua University, Beijing, China
- Tsinghua Laboratory of Brain and Intelligence, Tsinghua University, Beijing, China
| | - Penghao Qian
- College of Information and Electrical Engineering, China Agricultural University, Beijing, China
| | | | - Baosong Li
- Beijing No. 19 High School, Beijing, China
- College of Education, Zhejiang Normal University, Jinhua, China
| | - Yu Zhang
- Institution of Education, Tsinghua University, Beijing, China.
| | - Dan Zhang
- Department of Psychology, School of Social Sciences, Tsinghua University, Beijing, China.
- Tsinghua Laboratory of Brain and Intelligence, Tsinghua University, Beijing, China.
| |
Collapse
|
14
|
Davidesco I, Laurent E, Valk H, West T, Milne C, Poeppel D, Dikker S. The Temporal Dynamics of Brain-to-Brain Synchrony Between Students and Teachers Predict Learning Outcomes. Psychol Sci 2023; 34:633-643. [PMID: 37053267 DOI: 10.1177/09567976231163872] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/15/2023] Open
Abstract
Much of human learning happens through interaction with other people, but little is known about how this process is reflected in the brains of students and teachers. Here, we concurrently recorded electroencephalography (EEG) data from nine groups, each of which contained four students and a teacher. All participants were young adults from the northeast United States. Alpha-band (8-12 Hz) brain-to-brain synchrony between students predicted both immediate and delayed posttest performance. Further, brain-to-brain synchrony was higher in specific lecture segments associated with questions that students answered correctly. Brain-to-brain synchrony between students and teachers predicted learning outcomes at an approximately 300-ms lag in the students' brain activity relative to the teacher's brain activity, which is consistent with the time course of spoken-language comprehension. These findings provide key new evidence for the importance of collecting brain data simultaneously from groups of learners in ecologically valid settings.
Collapse
Affiliation(s)
- Ido Davidesco
- Department of Educational Psychology, University of Connecticut
| | | | | | - Tessa West
- Department of Psychology, New York University
| | | | - David Poeppel
- Department of Psychology, New York University
- Ernst Strüngmann Institute for Neuroscience, Frankfurt, Germany
| | | |
Collapse
|
15
|
Chen T, Tang R, Yang X, Peng M, Cai M. Moral transgression modulates fairness considerations in the ultimatum game: Evidence from ERP and EEG data. Int J Psychophysiol 2023; 188:1-11. [PMID: 36889599 DOI: 10.1016/j.ijpsycho.2023.03.001] [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: 12/19/2022] [Revised: 02/22/2023] [Accepted: 03/01/2023] [Indexed: 03/08/2023]
Abstract
People tend to dislike and punish unfair behaviors in social interactions, and this disposition may be moderated by the characteristics of their interaction partner. We used a modified ultimatum game (UG) to investigate players' responses to fair and unfair offers from proposers described as having performed either a moral transgression or a neutral behavior, and recorded an electroencephalogram. The participants' behavior in the UG suggests that people quickly demand more fairness from proposers who have committed moral transgressions rather than neutral behavior. Event-related potentials (ERPs) revealed a significant effect of offer type and of proposer type on P300 activity. The prestimulus α-oscillation power in the neutral behavior condition was significantly lower than that in the moral transgression condition. The post-stimulus β-event-related synchronization (β-ERS) was larger for the moral transgression condition than the neutral behavior condition in response to the least fair offers, and larger for neutral behavior than the moral transgression condition in response to the fairest offers. In summary, β-ERS was influenced by both proposer type and offer type, which revealed different neural responses to the offer from either a morally transgressive or a neutral behavior proposer.
Collapse
Affiliation(s)
- Tianlong Chen
- Key Laboratory of Adolescent Cyberpsychology and Behavior of the Ministry of Education and School of Psychology, Central China Normal University, Wuhan, China; School of Psychology, Central China Normal University, Wuhan, China
| | - Rui Tang
- Key Laboratory of Adolescent Cyberpsychology and Behavior of the Ministry of Education and School of Psychology, Central China Normal University, Wuhan, China; School of Psychology, Central China Normal University, Wuhan, China
| | - Xiaoying Yang
- Key Laboratory of Adolescent Cyberpsychology and Behavior of the Ministry of Education and School of Psychology, Central China Normal University, Wuhan, China; School of Psychology, Central China Normal University, Wuhan, China
| | - Ming Peng
- Key Laboratory of Adolescent Cyberpsychology and Behavior of the Ministry of Education and School of Psychology, Central China Normal University, Wuhan, China; School of Psychology, Central China Normal University, Wuhan, China; Key Laboratory of Human Development and Mental Health of Hubei Province, Wuhan 430079, China.
| | - Mengfei Cai
- Department of Psychology, Manhattanville College, New York, NY, USA
| |
Collapse
|
16
|
Massaeli F, Bagheri M, Power SD. EEG-based detection of modality-specific visual and auditory sensory processing. J Neural Eng 2023; 20. [PMID: 36749989 DOI: 10.1088/1741-2552/acb9be] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Accepted: 02/07/2023] [Indexed: 02/09/2023]
Abstract
Objective.A passive brain-computer interface (pBCI) is a system that enhances a human-machine interaction by monitoring the mental state of the user and, based on this implicit information, making appropriate modifications to the interaction. Key to the development of such a system is the ability to reliably detect the mental state of interest via neural signals. Many different mental states have been investigated, including fatigue, attention and various emotions, however one of the most commonly studied states is mental workload, i.e. the amount of attentional resources required to perform a task. The emphasis of mental workload studies to date has been almost exclusively on detecting and predicting the 'level' of cognitive resources required (e.g. high vs. low), but we argue that having information regarding the specific 'type' of resources (e.g. visual or auditory) would allow the pBCI to apply more suitable adaption techniques than would be possible knowing just the overall workload level.Approach.15 participants performed carefully designed visual and auditory tasks while electroencephalography (EEG) data was recorded. The tasks were designed to be as similar as possible to one another except for the type of attentional resources required. The tasks were performed at two different levels of demand. Using traditional machine learning algorithms, we investigated, firstly, if EEG can be used to distinguish between auditory and visual processing tasks and, secondly, what effect level of sensory processing demand has on the ability to distinguish between auditory and visual processing tasks.Main results.The results show that at the high level of demand, the auditory vs. visual processing tasks could be distinguished with an accuracy of 77.1% on average. However, in the low demand condition in this experiment, the tasks were not classified with an accuracy exceeding chance.Significance.These results support the feasibility of developing a pBCI for detecting not only the level, but also the type, of attentional resources being required of the user at a given time. Further research is required to determine if there is a threshold of demand under which the type of sensory processing cannot be detected, but even if that is the case, these results are still promising since it is the high end of demand that is of most concern in safety critical scenarios. Such a BCI could help improve safety in high risk occupations by initiating the most effective and efficient possible adaptation strategies when high workload conditions are detected.
Collapse
Affiliation(s)
- Faghihe Massaeli
- Faculty of Engineering and Applied Science, Memorial University of Newfoundland, St. Johns, Canada
| | - Mohammad Bagheri
- Faculty of Engineering and Applied Science, Memorial University of Newfoundland, St. Johns, Canada
| | - Sarah D Power
- Faculty of Engineering and Applied Science, Memorial University of Newfoundland, St. Johns, Canada.,Faculty of Medicine, Memorial University of Newfoundland, St. Johns, Canada
| |
Collapse
|
17
|
Nasiri E, Khalilzad M, Hakimzadeh Z, Isari A, Faryabi-Yousefabad S, Sadigh-Eteghad S, Naseri A. A comprehensive review of attention tests: can we assess what we exactly do not understand? THE EGYPTIAN JOURNAL OF NEUROLOGY, PSYCHIATRY AND NEUROSURGERY 2023. [DOI: 10.1186/s41983-023-00628-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/19/2023] Open
Abstract
AbstractAttention, as it is now defined as a process matching data from the environment to the needs of the organism, is one of the main aspects of human cognitive processes. There are several aspects to attention including tonic alertness (a process of intrinsic arousal that varies by minutes to hours), phasic alertness (a process that causes a quick change in attention as a result of a brief stimulus), selective attention (a process differentiating multiple stimuli), and sustained attention (a process maintaining persistence of response and continuous effort over an extended period). Attention dysfunction is associated with multiple disorders; therefore, there has been much effort in assessing attention and its domains, resulting in a battery of tests evaluating one or several attentional domains; instances of which are the Stroop color-word test, Test of Everyday Attention, Wisconsin Card Sorting Test, and Cambridge Neuropsychological Test Automated Battery. These tests vary in terms of utilities, range of age, and domains. The role of attention in human life and the importance of assessing it merits an inclusive review of the efforts made to assess attention and the resulting tests; Here we highlight all the necessary data regarding neurophysiological tests which assess human attentive function and investigates the evolution of attention tests over time. Also, the ways of assessing the attention in untestable patients who have difficulty in reading or using a computer, along with the lack of ability to comprehend verbal instructions and executive tasks, are discussed. This review can be of help as a platform for designing new studies to researchers who are interested in working on attention and conditions causing deficits in this aspect of body function, by collecting and organizing information on its assessment.
Collapse
|
18
|
Makov S, Pinto D, Har-Shai Yahav P, Miller LM, Zion Golumbic E. "Unattended, distracting or irrelevant": Theoretical implications of terminological choices in auditory selective attention research. Cognition 2023; 231:105313. [PMID: 36344304 DOI: 10.1016/j.cognition.2022.105313] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2022] [Revised: 09/30/2022] [Accepted: 10/19/2022] [Indexed: 11/06/2022]
Abstract
For seventy years, auditory selective attention research has focused on studying the cognitive mechanisms of prioritizing the processing a 'main' task-relevant stimulus, in the presence of 'other' stimuli. However, a closer look at this body of literature reveals deep empirical inconsistencies and theoretical confusion regarding the extent to which this 'other' stimulus is processed. We argue that many key debates regarding attention arise, at least in part, from inappropriate terminological choices for experimental variables that may not accurately map onto the cognitive constructs they are meant to describe. Here we critically review the more common or disruptive terminological ambiguities, differentiate between methodology-based and theory-derived terms, and unpack the theoretical assumptions underlying different terminological choices. Particularly, we offer an in-depth analysis of the terms 'unattended' and 'distractor' and demonstrate how their use can lead to conflicting theoretical inferences. We also offer a framework for thinking about terminology in a more productive and precise way, in hope of fostering more productive debates and promoting more nuanced and accurate cognitive models of selective attention.
Collapse
Affiliation(s)
- Shiri Makov
- The Gonda Multidisciplinary Center for Brain Research, Bar Ilan University, Israel
| | - Danna Pinto
- The Gonda Multidisciplinary Center for Brain Research, Bar Ilan University, Israel
| | - Paz Har-Shai Yahav
- The Gonda Multidisciplinary Center for Brain Research, Bar Ilan University, Israel
| | - Lee M Miller
- The Center for Mind and Brain, University of California, Davis, CA, United States of America; Department of Neurobiology, Physiology, & Behavior, University of California, Davis, CA, United States of America; Department of Otolaryngology / Head and Neck Surgery, University of California, Davis, CA, United States of America
| | - Elana Zion Golumbic
- The Gonda Multidisciplinary Center for Brain Research, Bar Ilan University, Israel.
| |
Collapse
|
19
|
Simonetti I, Tamborra L, Giorgi A, Ronca V, Vozzi A, Aricò P, Borghini G, Sciaraffa N, Trettel A, Babiloni F, Picardi M, Di Flumeri G. Neurophysiological Evaluation of Students' Experience during Remote and Face-to-Face Lessons: A Case Study at Driving School. Brain Sci 2023; 13:brainsci13010095. [PMID: 36672076 PMCID: PMC9856302 DOI: 10.3390/brainsci13010095] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Revised: 12/28/2022] [Accepted: 12/29/2022] [Indexed: 01/06/2023] Open
Abstract
Nowadays, fostered by technological progress and contextual circumstances such as the economic crisis and pandemic restrictions, remote education is experiencing growing deployment. However, this growth has generated widespread doubts about the actual effectiveness of remote/online learning compared to face-to-face education. The present study was aimed at comparing face-to-face and remote education through a multimodal neurophysiological approach. It involved forty students at a driving school, in a real classroom, experiencing both modalities. Wearable devices to measure brain, ocular, heart and sweating activities were employed in order to analyse the students' neurophysiological signals to obtain insights into the cognitive dimension. In particular, four parameters were considered: the Eye Blink Rate, the Heart Rate and its Variability and the Skin Conductance Level. In addition, the students filled out a questionnaire at the end to obtain an explicit measure of their learning performance. Data analysis showed higher cognitive activity, in terms of attention and mental engagement, in the in-presence setting compared to the remote modality. On the other hand, students in the remote class felt more stressed, particularly during the first part of the lesson. The analysis of questionnaires demonstrated worse performance for the remote group, thus suggesting a common "disengaging" behaviour when attending remote courses, thus undermining their effectiveness. In conclusion, neuroscientific tools could help to obtain insights into mental concerns, often "blind", such as decreasing attention and increasing stress, as well as their dynamics during the lesson itself, thus allowing the definition of proper countermeasures to emerging issues when introducing new practices into daily life.
Collapse
Affiliation(s)
- Ilaria Simonetti
- Department of Anatomical, Histological, Forensic and Orthopedic Sciences, Sapienza University of Rome, 00161 Rome, Italy
- BrainSigns srl, 00198 Rome, Italy
| | - Luca Tamborra
- Department of Anatomical, Histological, Forensic and Orthopedic Sciences, Sapienza University of Rome, 00161 Rome, Italy
- BrainSigns srl, 00198 Rome, Italy
| | - Andrea Giorgi
- Department of Anatomical, Histological, Forensic and Orthopedic Sciences, Sapienza University of Rome, 00161 Rome, Italy
- BrainSigns srl, 00198 Rome, Italy
| | - Vincenzo Ronca
- Department of Anatomical, Histological, Forensic and Orthopedic Sciences, Sapienza University of Rome, 00161 Rome, Italy
- BrainSigns srl, 00198 Rome, Italy
| | - Alessia Vozzi
- Department of Anatomical, Histological, Forensic and Orthopedic Sciences, Sapienza University of Rome, 00161 Rome, Italy
- BrainSigns srl, 00198 Rome, Italy
| | | | - Gianluca Borghini
- BrainSigns srl, 00198 Rome, Italy
- Laboratory of Industrial Neuroscience, Department of Molecular Medicine, Sapienza University of Rome, 00161 Rome, Italy
| | | | | | - Fabio Babiloni
- BrainSigns srl, 00198 Rome, Italy
- Laboratory of Industrial Neuroscience, Department of Molecular Medicine, Sapienza University of Rome, 00161 Rome, Italy
- School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou 310018, China
| | | | - Gianluca Di Flumeri
- BrainSigns srl, 00198 Rome, Italy
- Laboratory of Industrial Neuroscience, Department of Molecular Medicine, Sapienza University of Rome, 00161 Rome, Italy
- Correspondence:
| |
Collapse
|
20
|
Jamil N, Belkacem AN, Lakas A. On enhancing students' cognitive abilities in online learning using brain activity and eye movements. EDUCATION AND INFORMATION TECHNOLOGIES 2023; 28:4363-4397. [PMID: 36277512 PMCID: PMC9574174 DOI: 10.1007/s10639-022-11372-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/05/2022] [Accepted: 09/19/2022] [Indexed: 05/11/2023]
Abstract
The COVID-19 pandemic has interrupted education institutions in over 150 nations, affecting billions of students. Many governments have forced a transition in higher education from in-person to remote learning. After this abrupt, worldwide transition away from the classroom, some question whether online education will continue to grow in acceptance in post-pandemic times. However, new technology, such as the brain-computer interface and eye-tracking, have the potential to improve the remote learning environment, which currently faces several obstacles and deficiencies. Cognitive brain computer interfaces can help us develop a better understanding of brain functions, allowing for the development of more effective learning methodologies and the enhancement of brain-based skills. We carried out a systematic literature review of research on the use of brain computer interfaces and eye-tracking to measure students' cognitive skills during online learning. We found that, because many experimental tasks depend on recorded rather than real-time video, students don't have direct and real-time interaction with their teacher. Further, we found no evidence in any of the reviewed papers for brain-to-brain synchronization during remote learning. This points to a potentially fruitful future application of brain computer interfaces in education, investigating whether the brains of student-teacher pairs who interact with the same course content have increasingly similar brain patterns.
Collapse
Affiliation(s)
- Nuraini Jamil
- Department of Computer Science and Software Engineering, College of Information Technology, UAE University, P.O. Box 15551 Abu Dhabi Al-Ain, UAE
| | - Abdelkader Nasreddine Belkacem
- Department of Computer and Network Engineering, College of Information Technology, UAE University, P.O. Box 15551 Abu Dhabi Al-Ain, UAE
| | - Abderrahmane Lakas
- Department of Computer and Network Engineering, College of Information Technology, UAE University, P.O. Box 15551 Abu Dhabi Al-Ain, UAE
| |
Collapse
|
21
|
Educational neurotechnology: Where do we go from here? Trends Neurosci Educ 2022; 29:100195. [PMID: 36470622 DOI: 10.1016/j.tine.2022.100195] [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: 10/05/2022] [Revised: 10/26/2022] [Accepted: 10/26/2022] [Indexed: 11/06/2022]
Abstract
Recent educational trends point to an interest in educational neurotechnology. While these tools have the potential to change education, little is known about whether their use improves educational outcomes. Additionally, their adoption may be negatively impacted by teachers' lack of knowledge about the brain. In this paper we outline the potential of educational neurotechnology including what we know, what we do not yet know, and additional considerations for the ethical, successful adoption of these tools in classrooms around the world. Special consideration is given to the training needs of pre- and in-service educators whose support will be essential to the successful adoption of educational neurotechnology.
Collapse
|
22
|
Decoding the cognitive states of attention and distraction in a real-life setting using EEG. Sci Rep 2022; 12:20649. [PMID: 36450871 PMCID: PMC9712397 DOI: 10.1038/s41598-022-24417-w] [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: 06/14/2022] [Accepted: 11/15/2022] [Indexed: 12/11/2022] Open
Abstract
Lapses in attention can have serious consequences in situations such as driving a car, hence there is considerable interest in tracking it using neural measures. However, as most of these studies have been done in highly controlled and artificial laboratory settings, we want to explore whether it is also possible to determine attention and distraction using electroencephalogram (EEG) data collected in a natural setting using machine/deep learning. 24 participants volunteered for the study. Data were collected from pairs of participants simultaneously while they engaged in Tibetan Monastic debate, a practice that is interesting because it is a real-life situation that generates substantial variability in attention states. We found that attention was on average associated with increased left frontal alpha, increased left parietal theta, and decreased central delta compared to distraction. In an attempt to predict attention and distraction, we found that a Long Short Term Memory model classified attention and distraction with maximum accuracy of 95.86% and 95.4% corresponding to delta and theta waves respectively. This study demonstrates that EEG data collected in a real-life setting can be used to predict attention states in participants with good accuracy, opening doors for developing Brain-Computer Interfaces that track attention in real-time using data extracted in daily life settings, rendering them much more usable.
Collapse
|
23
|
Sharma P, Zhang Z, Conroy TB, Hui X, Kan EC. Attention Detection by Heartbeat and Respiratory Features from Radio-Frequency Sensor. SENSORS (BASEL, SWITZERLAND) 2022; 22:8047. [PMID: 36298396 PMCID: PMC9610852 DOI: 10.3390/s22208047] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Revised: 10/13/2022] [Accepted: 10/17/2022] [Indexed: 06/16/2023]
Abstract
This work presents a study on users' attention detection with reference to a relaxed inattentive state using an over-the-clothes radio-frequency (RF) sensor. This sensor couples strongly to the internal heart, lung, and diaphragm motion based on the RF near-field coherent sensing principle, without requiring a tension chest belt or skin-contact electrocardiogram. We use cardiac and respiratory features to distinguish attention-engaging vigilance tasks from a relaxed, inattentive baseline state. We demonstrate high-quality vitals from the RF sensor compared to the reference electrocardiogram and respiratory tension belts, as well as similar performance for attention detection, while improving user comfort. Furthermore, we observed a higher vigilance-attention detection accuracy using respiratory features rather than heartbeat features. A high influence of the user's baseline emotional and arousal levels on the learning model was noted; thus, individual models with personalized prediction were designed for the 20 participants, leading to an average accuracy of 83.2% over unseen test data with a high sensitivity and specificity of 85.0% and 79.8%, respectively.
Collapse
|
24
|
Wang S, Zhu L, Gao L, Yuan J, Li G, Sun Y, Qi P. Modulating break types induces divergent low band EEG processes during post-break improvement: A power spectral analysis. Front Hum Neurosci 2022; 16:960286. [PMID: 36188173 PMCID: PMC9524192 DOI: 10.3389/fnhum.2022.960286] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2022] [Accepted: 08/03/2022] [Indexed: 11/13/2022] Open
Abstract
Conventional wisdom suggests mid-task rest as a potential approach to relieve the time-on-task (TOT) effect while accumulating evidence indicated that acute exercise might also effectively restore mental fatigue. However, few studies have explored the neural mechanism underlying these different break types, and the results were scattered. This study provided one of the first looks at how different types of fatigue-recovery break exerted influence on the cognitive processes by evaluating the corresponding behavioral improvement and neural response (EEG power spectral) in a sustained attention task. Specifically, 19 participants performed three sessions of psychomotor vigilance tasks (PVT), with one session including a continuous 30-min PVT while the other two sessions additionally inserted a 15-min mid-task cycling and rest break, respectively. For behavioral performance, both types of break could restore objective vigilance transiently, while subjective feeling was only maintained after mid-task rest. Moreover, divergent patterns of EEG change were observed during post-break improvement. In detail, relative theta decreased and delta increased immediately after mid-task exercise, while decreased delta was found near the end of the rest-inserted task. Meanwhile, theta and delta could serve as neurological indicators to predict the reaction time change for exercise and rest intervention, respectively. In sum, our findings provided novel evidence to demonstrate divergent neural patterns following the mid-task exercise and rest intervention to counter TOT effects, which might lead to new insights into the nascent field of neuroergonomics for mental fatigue restoration.
Collapse
Affiliation(s)
- Sujie Wang
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, Zhejiang University, Hangzhou, China
| | - Li Zhu
- School of Physical Education and Health Science, Guangxi University for Nationalities, Nanning, China
| | - Lingyun Gao
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, Zhejiang University, Hangzhou, China
| | - Jingjia Yuan
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, Zhejiang University, Hangzhou, China
| | - Gang Li
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, Zhejiang University, Hangzhou, China
- College of Mathematical Medicine, Zhejiang Normal University, Jinhua, China
| | - Yu Sun
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, Zhejiang University, Hangzhou, China
- Department of Neurology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Yu Sun
| | - Peng Qi
- Department of Control Science and Engineering, College of Electronics and Information Engineering, Tongji University, Shanghai, China
- *Correspondence: Peng Qi
| |
Collapse
|
25
|
Xu K, Torgrimson SJ, Torres R, Lenartowicz A, Grammer JK. EEG Data Quality in Real-World Settings: Examining Neural Correlates of Attention in School-Aged Children. MIND, BRAIN AND EDUCATION : THE OFFICIAL JOURNAL OF THE INTERNATIONAL MIND, BRAIN, AND EDUCATION SOCIETY 2022; 16:221-227. [PMID: 38169954 PMCID: PMC10760994 DOI: 10.1111/mbe.12314] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/09/2020] [Accepted: 12/27/2021] [Indexed: 01/05/2024]
Abstract
Advances in mobile electroencephalography (EEG) technology have made it possible to examine covert cognitive processes in real-world settings such as student attention in the classroom. Here, we outline research using wired and wireless EEG technology to examine attention in elementary school children across increasingly naturalistic paradigms in schools, ranging from a lab-based paradigm where children met one-on-one with an experimenter in a field laboratory to mobile EEG testing conducted in the same school during semi-naturalistic classroom lessons. Despite an increase of data loss with the classroom-based paradigm, we demonstrate that it is feasible to collect quality data in classroom settings with young children. We also provide a test case for how robust EEG signals, such as alpha oscillations, can be used to identify measurable differences in covert processes like attention in classrooms. We end with pragmatic suggestions for researchers interested in employing naturalistic EEG methods in real-world, multisensory contexts.
Collapse
Affiliation(s)
- Keye Xu
- School of Education and Information Studies, University of California, Los Angeles
| | - Sarah Jo Torgrimson
- School of Education and Information Studies, University of California, Los Angeles
| | - Remi Torres
- School of Education and Information Studies, University of California, Los Angeles
| | - Agatha Lenartowicz
- Semel Institute for Neuroscience and Behavior, University of California, Los Angeles
| | - Jennie K. Grammer
- School of Education and Information Studies, University of California, Los Angeles
| |
Collapse
|
26
|
Kant P, Laskar SH, Hazarika J. Transfer learning-based EEG analysis of visual attention and working memory on motor cortex for BCI. Neural Comput Appl 2022. [DOI: 10.1007/s00521-022-07580-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
|
27
|
A Virtual Reality and Online Learning Immersion Experience Evaluation Model Based on SVM and Wearable Recordings. ELECTRONICS 2022. [DOI: 10.3390/electronics11091429] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
The increasing development in the field of biosensing technologies makes it feasible to monitor students’ physiological signals in natural learning scenarios. With the rise of mobile learning, educators are attaching greater importance to the learning immersion experience of students, especially with the global background of COVID-19. However, traditional methods, such as questionnaires and scales, to evaluate the learning immersion experience are greatly influenced by individuals’ subjective factors. Herein, our research aims to explore the relationship and mechanism between human physiological recordings and learning immersion experiences to eliminate subjectivity as much as possible. We collected electroencephalogram and photoplethysmographic signals, as well as self-reports on the immersive experience of thirty-seven college students during virtual reality and online learning to form the fundamental feature set. Then, we proposed an evaluation model based on a support vector machine and got a precision accuracy of 89.72%. Our research results provide evidence supporting the possibility of predicting students’ learning immersion experience by their EEGs and PPGs.
Collapse
|
28
|
The Relationship between Cognitive Status and Retained Activity Participation among Community-Dwelling Older Adults. Eur J Investig Health Psychol Educ 2022; 12:400-416. [PMID: 35447747 PMCID: PMC9025576 DOI: 10.3390/ejihpe12040029] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2022] [Revised: 03/18/2022] [Accepted: 03/23/2022] [Indexed: 12/13/2022] Open
Abstract
Identifying retained activity participation to old age can improve age-related changes in balance and cognition function. Subjects ≥ 60 years were enrolled in this study. Balance and Cognitive function include working memory, executive function, and sustained and divided attention was evaluated with “Fullerton advanced balance”, “n-back”, “Wisconsin card sort”, “sustain and divided attention test”, respectively. In addition, retained activity participation was measured using the Activity Card Sort questionnaire. The univariate and multivariate regression analyses of different domains of retained activity participation were used as independent variables, including instrumental activity, low-effort leisure, high-effort leisure, and social activity on balance and specific domains of cognition. Seventy-seven subjects (65.3 ± 4.4 years, 61% female) were included. About 47% of older adults had a college education, 32.3% had a diploma, and 20.7% had elementary−middle education. These results show that retained instrumental activity had a relationship with working memory (β = 0.079, p < 0.05). In addition, we found that retained high-effort leisure activity can increase balance, divided attention, and executive function score (β = 0.1, β = 0.05, β = 0.02, p < 0.05). Moreover, there was a positive relationship between retained low-effort activity and sustained attention (β = 0.08, p < 0.05). In addition, the coefficient of determination (R2) for balance, working memory, executive function, sustained, and divided attention were 0.45, 0.25, 0.13, 0.11 and 0.18, respectively. The study suggests that retained activity participation types may have various effects on balance and some selective cognitive components in older people.
Collapse
|
29
|
Attention Classification Based on Biosignals during Standard Cognitive Tasks for Occupational Domains. COMPUTERS 2022. [DOI: 10.3390/computers11040049] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Occupational disorders considerably impact workers’ quality of life and organizational productivity, and even affect mortality worldwide. Such health issues are related to mental health and ergonomics risk factors. In particular, mental health may be affected by cognitive strain caused by unexpected interruptions and other attention compromising factors. Risk factors assessment associated with cognitive strain in office environments, namely related to attention states, still suffers from the lack of scientifically validated tools. In this work, we aim to develop a series of classification models that can classify attention during pre-defined cognitive tasks based on the acquisition of biosignals to create a ground truth of attention. Biosignals, such as electrocardiography, electroencephalography, and functional near-infrared spectroscopy, were acquired from eight subjects during standard cognitive tasks inducing attention. Individually tuned machine learning models trained with those biosignals allowed us to successfully detect attention on the individual level, with results in the range of 70–80%. The electroencephalogram and electrocardiogram were revealed to be the most appropriate sensors in this context, and the combination of multiple sensors demonstrated the importance of using multiple sources. These models prove to be relevant for the development of attention identification tools by providing ground truth to determine which human–computer interaction variables have strong associations with attention.
Collapse
|
30
|
Early prediction of cognitive impairments using physiological signal for enhanced socioeconomic status. Inf Process Manag 2022. [DOI: 10.1016/j.ipm.2021.102845] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
|
31
|
Hölle D, Blum S, Kissner S, Debener S, Bleichner MG. Real-Time Audio Processing of Real-Life Soundscapes for EEG Analysis: ERPs Based on Natural Sound Onsets. FRONTIERS IN NEUROERGONOMICS 2022; 3:793061. [PMID: 38235458 PMCID: PMC10790832 DOI: 10.3389/fnrgo.2022.793061] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/11/2021] [Accepted: 01/03/2021] [Indexed: 01/19/2024]
Abstract
With smartphone-based mobile electroencephalography (EEG), we can investigate sound perception beyond the lab. To understand sound perception in the real world, we need to relate naturally occurring sounds to EEG data. For this, EEG and audio information need to be synchronized precisely, only then it is possible to capture fast and transient evoked neural responses and relate them to individual sounds. We have developed Android applications (AFEx and Record-a) that allow for the concurrent acquisition of EEG data and audio features, i.e., sound onsets, average signal power (RMS), and power spectral density (PSD) on smartphone. In this paper, we evaluate these apps by computing event-related potentials (ERPs) evoked by everyday sounds. One participant listened to piano notes (played live by a pianist) and to a home-office soundscape. Timing tests showed a stable lag and a small jitter (< 3 ms) indicating a high temporal precision of the system. We calculated ERPs to sound onsets and observed the typical P1-N1-P2 complex of auditory processing. Furthermore, we show how to relate information on loudness (RMS) and spectra (PSD) to brain activity. In future studies, we can use this system to study sound processing in everyday life.
Collapse
Affiliation(s)
- Daniel Hölle
- Neurophysiology of Everyday Life Group, Department of Psychology, University of Oldenburg, Oldenburg, Germany
| | - Sarah Blum
- Neuropsychology Lab, Department of Psychology, University of Oldenburg, Oldenburg, Germany
- Cluster of Excellence Hearing4all, Oldenburg, Germany
| | - Sven Kissner
- Institute for Hearing Technology and Audiology, Jade University of Applied Sciences, Oldenburg, Germany
| | - Stefan Debener
- Neuropsychology Lab, Department of Psychology, University of Oldenburg, Oldenburg, Germany
| | - Martin G. Bleichner
- Neurophysiology of Everyday Life Group, Department of Psychology, University of Oldenburg, Oldenburg, Germany
| |
Collapse
|
32
|
Yu M, Xiao S, Hua M, Wang H, Chen X, Tian F, Li Y. EEG-based emotion recognition in an immersive virtual reality environment: From local activity to brain network features. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2021.103349] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
|
33
|
Longitudinal maturation of resting state networks: Relevance to sustained attention and attention deficit/hyperactivity disorder. COGNITIVE, AFFECTIVE & BEHAVIORAL NEUROSCIENCE 2022; 22:1432-1446. [PMID: 35676491 PMCID: PMC9622522 DOI: 10.3758/s13415-022-01017-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Accepted: 05/21/2022] [Indexed: 01/27/2023]
Abstract
The transition from childhood to adolescence involves important neural function, cognition, and behavior changes. However, the links between maturing brain function and sustained attention over this period could be better understood. This study examined typical changes in network functional connectivity over childhood to adolescence, developmental differences in attention deficit/hyperactivity disorder (ADHD), and how functional connectivity might underpin variability in sustained attention development in a longitudinal sample. A total of 398 resting state scans were collected from 173 children and adolescents (88 ADHD, 85 control) at up to three timepoints across ages 9-14 years. The effects of age, sex, and diagnostic group on changes in network functional connectivity were assessed, followed by relationships between functional connectivity and sustained attention development using linear mixed effects modelling. The ADHD group displayed greater decreases in functional connectivity between salience and visual networks compared with controls. Lower childhood functional connectivity between the frontoparietal and several brain networks was associated with more rapid sustained attention development, whereas frontoparietal to dorsal attention network connectivity related to attention trajectories in children with ADHD alone. Brain network segregation may increase into adolescence as predicted by key developmental theories; however, participants with ADHD demonstrated altered developmental trajectories between salience and visual networks. The segregation of the frontoparietal network from other brain networks may be a mechanism supporting sustained attention development. Frontoparietal to dorsal attention connectivity can be a focus for further work in ADHD.
Collapse
|
34
|
Automatic Clustering of Students by Level of Situational Interest Based on Their EEG Features. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app12010389] [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
The usage of physiological measures in detecting student’s interest is often said to improve the weakness of psychological measures by decreasing the susceptibility of subjective bias. The existing methods, especially EEG-based, use classification, which needs a predefined class and complex computational to analyze. However, the predefined classes are mostly based on subjective measurement (e.g., questionnaires). This work proposed a new scheme to automatically cluster the students by the level of situational interest (SI) during learning-based lessons on their electroencephalography (EEG) features. The formed clusters are then used as ground truth for classification purposes. A simultaneous recording of EEG was performed on 30 students while attending a lecture in a real classroom. The frontal mean delta and alpha power as well as the frontal alpha asymmetry metric served as the input for k-means and Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering algorithms. Using the collected data, 29 models were trained within nine domain classifiers, then the classifiers with the highest performance were selected. We validated all the models through 10-fold cross-validation. The high SI group was clustered to students having lower frontal mean delta and alpha power together with negative Frontal Alpha Asymmetry (FAA). It was found that k-means performed better by giving the maximum performance assessment parameters of 100% in clustering the students into three groups: high SI, medium SI and low SI. The findings show that the DBSCAN had reduced the performance to cluster dataset without the outlier. The findings of this study give a promising option to cluster the students by their SI level, as well as address the drawbacks of the existing methods, which use subjective measures.
Collapse
|
35
|
Yuan H, Li Y, Yang J, Li H, Yang Q, Guo C, Zhu S, Shu X. State of the Art of Non-Invasive Electrode Materials for Brain-Computer Interface. MICROMACHINES 2021; 12:1521. [PMID: 34945371 PMCID: PMC8705666 DOI: 10.3390/mi12121521] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/09/2021] [Revised: 12/01/2021] [Accepted: 12/02/2021] [Indexed: 02/02/2023]
Abstract
The brain-computer interface (BCI) has emerged in recent years and has attracted great attention. As an indispensable part of the BCI signal acquisition system, brain electrodes have a great influence on the quality of the signal, which determines the final effect. Due to the special usage scenario of brain electrodes, some specific properties are required for them. In this study, we review the development of three major types of EEG electrodes from the perspective of material selection and structural design, including dry electrodes, wet electrodes, and semi-dry electrodes. Additionally, we provide a reference for the current chaotic performance evaluation of EEG electrodes in some aspects such as electrochemical performance, stability, and so on. Moreover, the challenges and future expectations for EEG electrodes are analyzed.
Collapse
Affiliation(s)
- Haowen Yuan
- State Key Laboratory of Metal Matrix Composites, Shanghai Jiao Tong University, Shanghai 200240, China; (H.Y.); (J.Y.); (H.L.); (Q.Y.); (C.G.); (S.Z.)
| | - Yao Li
- State Key Laboratory of Metal Matrix Composites, Shanghai Jiao Tong University, Shanghai 200240, China; (H.Y.); (J.Y.); (H.L.); (Q.Y.); (C.G.); (S.Z.)
| | - Junjun Yang
- State Key Laboratory of Metal Matrix Composites, Shanghai Jiao Tong University, Shanghai 200240, China; (H.Y.); (J.Y.); (H.L.); (Q.Y.); (C.G.); (S.Z.)
| | - Hongjie Li
- State Key Laboratory of Metal Matrix Composites, Shanghai Jiao Tong University, Shanghai 200240, China; (H.Y.); (J.Y.); (H.L.); (Q.Y.); (C.G.); (S.Z.)
| | - Qinya Yang
- State Key Laboratory of Metal Matrix Composites, Shanghai Jiao Tong University, Shanghai 200240, China; (H.Y.); (J.Y.); (H.L.); (Q.Y.); (C.G.); (S.Z.)
| | - Cuiping Guo
- State Key Laboratory of Metal Matrix Composites, Shanghai Jiao Tong University, Shanghai 200240, China; (H.Y.); (J.Y.); (H.L.); (Q.Y.); (C.G.); (S.Z.)
| | - Shenmin Zhu
- State Key Laboratory of Metal Matrix Composites, Shanghai Jiao Tong University, Shanghai 200240, China; (H.Y.); (J.Y.); (H.L.); (Q.Y.); (C.G.); (S.Z.)
| | - Xiaokang Shu
- School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
| |
Collapse
|
36
|
Evaluation of a Fast Test Based on Biometric Signals to Assess Mental Fatigue at the Workplace-A Pilot Study. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph182211891. [PMID: 34831645 PMCID: PMC8621458 DOI: 10.3390/ijerph182211891] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/05/2021] [Revised: 10/19/2021] [Accepted: 11/02/2021] [Indexed: 01/29/2023]
Abstract
Non-pathological mental fatigue is a recurring, but undesirable condition among people in the fields of office work, industry, and education. This type of mental fatigue can often lead to negative outcomes, such as performance reduction and cognitive impairment in education; loss of focus and burnout syndrome in office work; and accidents leading to injuries or death in the transportation and manufacturing industries. Reliable mental fatigue assessment tools are promising in the improvement of performance, mental health and safety of students and workers, and at the same time, in the reduction of risks, accidents and the associated economic loss (e.g., medical fees and equipment reparations). The analysis of biometric (brain, cardiac, skin conductance) signals has proven to be effective in discerning different stages of mental fatigue; however, many of the reported studies in the literature involve the use of long fatigue-inducing tests and subject-specific models in their methodologies. Recent trends in the modeling of mental fatigue suggest the usage of non subject-specific (general) classifiers and a time reduction of calibration procedures and experimental setups. In this study, the evaluation of a fast and short-calibration mental fatigue assessment tool based on biometric signals and inter-subject modeling, using multiple linear regression, is presented. The proposed tool does not require fatigue-inducing tests, which allows fast setup and implementation. Electroencephalography, photopletismography, electrodermal activity, and skin temperature from 17 subjects were recorded, using an OpenBCI helmet and an Empatica E4 wristband. Correlations to self-reported mental fatigue levels (using the fatigue assessment scale) were calculated to find the best mental fatigue predictors. Three-class mental fatigue models were evaluated, and the best model obtained an accuracy of 88% using three features, β/θ (C3), and the α/θ (O2 and C3) ratios, from one minute of electroencephalography measurements. The results from this pilot study show the feasibility and potential of short-calibration procedures and inter-subject classifiers in mental fatigue modeling, and will contribute to the use of wearable devices for the development of tools oriented to the well-being of workers and students, and also in daily living activities.
Collapse
|
37
|
Janssen TW, Grammer JK, Bleichner MG, Bulgarelli C, Davidesco I, Dikker S, Jasińska KK, Siugzdaite R, Vassena E, Vatakis A, Zion‐Golumbic E, van Atteveldt N. Opportunities and Limitations of Mobile Neuroimaging Technologies in Educational Neuroscience. MIND, BRAIN AND EDUCATION : THE OFFICIAL JOURNAL OF THE INTERNATIONAL MIND, BRAIN, AND EDUCATION SOCIETY 2021; 15:354-370. [PMID: 35875415 PMCID: PMC9292610 DOI: 10.1111/mbe.12302] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/11/2020] [Revised: 06/18/2021] [Accepted: 09/01/2021] [Indexed: 05/20/2023]
Abstract
As the field of educational neuroscience continues to grow, questions have emerged regarding the ecological validity and applicability of this research to educational practice. Recent advances in mobile neuroimaging technologies have made it possible to conduct neuroscientific studies directly in naturalistic learning environments. We propose that embedding mobile neuroimaging research in a cycle (Matusz, Dikker, Huth, & Perrodin, 2019), involving lab-based, seminaturalistic, and fully naturalistic experiments, is well suited for addressing educational questions. With this review, we take a cautious approach, by discussing the valuable insights that can be gained from mobile neuroimaging technology, including electroencephalography and functional near-infrared spectroscopy, as well as the challenges posed by bringing neuroscientific methods into the classroom. Research paradigms used alongside mobile neuroimaging technology vary considerably. To illustrate this point, studies are discussed with increasingly naturalistic designs. We conclude with several ethical considerations that should be taken into account in this unique area of research.
Collapse
Affiliation(s)
- Tieme W.P. Janssen
- Department of Clinical, Neuro‐ & Developmental Psychology, Vrije Universiteit
| | - Jennie K. Grammer
- Graduate School of Education and Information Studies, University of California Los Angeles
| | | | - Chiara Bulgarelli
- Centre for Brain and Cognitive Development, Birkbeck University of London
| | - Ido Davidesco
- Department of Educational Psychology, University of Connecticut
| | | | - Kaja K. Jasińska
- Department of Applied Psychology and Human Development, University of Toronto
| | | | - Eliana Vassena
- Donders Institute for Brain, Cognition and Behaviour, Radboud University
| | | | | | | |
Collapse
|
38
|
Habibzadeh H, Norton JJS, Vaughan TM, Soyata T, Zois DS. A Voting-Enhanced Dynamic-Window-Length Classifier for SSVEP-Based BCIs. IEEE Trans Neural Syst Rehabil Eng 2021; 29:1766-1773. [PMID: 34428141 PMCID: PMC8496754 DOI: 10.1109/tnsre.2021.3106876] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
We present a dynamic window-length classifier for steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) that does not require the user to choose a feature extraction method or channel set. Instead, the classifier uses multiple feature extraction methods and channel selections to infer the SSVEP and relies on majority voting to pick the most likely target. The classifier extends the window length dynamically if no target obtains the majority of votes. Compared with existing solutions, our classifier: (i) does not assume that any single feature extraction method will consistently outperform the others; (ii) adapts the channel selection to individual users or tasks; (iii) uses dynamic window lengths; (iv) is unsupervised (i.e., does not need training). Collectively, these characteristics make the classifier easy-to-use, especially for caregivers and others with limited technical expertise. We evaluated the performance of our classifier on a publicly available benchmark dataset from 35 healthy participants. We compared the information transfer rate (ITR) of this new classifier to those of the minimum energy combination (MEC), maximum synchronization index (MSI), and filter bank canonical correlation analysis (FBCCA). The new classifier increases average ITR to 123.5 bits-per-minute (bpm), 47.5, 51.2, and 19.5 bpm greater than the MEC, MSI, and FBCCA classifiers, respectively.
Collapse
|
39
|
Neural Dynamics of Target Detection via Wireless EEG in Embodied Cognition. SENSORS 2021; 21:s21155213. [PMID: 34372448 PMCID: PMC8348206 DOI: 10.3390/s21155213] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Revised: 07/23/2021] [Accepted: 07/28/2021] [Indexed: 11/16/2022]
Abstract
Embodied cognitive attention detection is important for many real-world applications, such as monitoring attention in daily driving and studying. Exploring how the brain and behavior are influenced by visual sensory inputs becomes a major challenge in the real world. The neural activity of embodied mind cognitive states can be understood through simple symbol experimental design. However, searching for a particular target in the real world is more complicated than during a simple symbol experiment in the laboratory setting. Hence, the development of realistic situations for investigating the neural dynamics of subjects during real-world environments is critical. This study designed a novel military-inspired target detection task for investigating the neural activities of performing embodied cognition tasks in the real-world setting. We adopted independent component analysis (ICA) and electroencephalogram (EEG) dipole source localization methods to study the participant's event-related potentials (ERPs), event-related spectral perturbation (ERSP), and power spectral density (PSD) during the target detection task using a wireless EEG system, which is more convenient for real-life use. Behavioral results showed that the response time in the congruent condition (582 ms) was shorter than those in the incongruent (666 ms) and nontarget (863 ms) conditions. Regarding the EEG observation, we observed N200-P300 wave activation in the middle occipital lobe and P300-N500 wave activation in the right frontal lobe and left motor cortex, which are associated with attention ERPs. Furthermore, delta (1-4 Hz) and theta (4-7 Hz) band powers in the right frontal lobe, as well as alpha (8-12 Hz) and beta (13-30 Hz) band powers in the left motor cortex were suppressed, whereas the theta (4-7 Hz) band powers in the middle occipital lobe were increased considerably in the attention task. Experimental results showed that the embodied body function influences human mental states and psychological performance under cognition attention tasks. These neural markers will be also feasible to implement in the real-time brain computer interface. Novel findings in this study can be helpful for humans to further understand the interaction between the brain and behavior in multiple target detection conditions in real life.
Collapse
|
40
|
Grammer JK, Xu K, Lenartowicz A. Effects of context on the neural correlates of attention in a college classroom. NPJ SCIENCE OF LEARNING 2021; 6:15. [PMID: 34230485 PMCID: PMC8260629 DOI: 10.1038/s41539-021-00094-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/17/2020] [Accepted: 04/28/2021] [Indexed: 05/04/2023]
Abstract
Activities that are effective in supporting attention have the potential to increase opportunities for student learning. However, little is known about the impact of instructional contexts on student attention, in part due to limitations in our ability to measure attention in the classroom, typically based on behavioral observation and self-reports. To address this issue, we used portable electroencephalography (EEG) measurements of neural oscillations to evaluate the effects of learning context on student attention. The results suggest that attention, as indexed by lower alpha power as well as higher beta and gamma power, is stronger during student-initiated activities than teacher-initiated activities. EEG data revealed different patterns in student attention as compared to standardized coding of attentional behaviors. We conclude that EEG signals offer a powerful tool for understanding differences in student cognitive states as a function of classroom instruction that are unobservable from behavior alone.
Collapse
Affiliation(s)
- Jennie K Grammer
- School of Education and Information Studies, University of California, Los Angeles, CA, USA.
| | - Keye Xu
- School of Education and Information Studies, University of California, Los Angeles, CA, USA
| | - Agatha Lenartowicz
- Semel Institute for Neuroscience and Behavior, Department of Psychiatry & Biobehavioral Sciences, University of California, Los Angeles, CA, USA
| |
Collapse
|
41
|
Peng C, Peng W, Feng W, Zhang Y, Xiao J, Wang D. EEG Correlates of Sustained Attention Variability during Discrete Multi-finger Force Control Tasks. IEEE TRANSACTIONS ON HAPTICS 2021; 14:526-537. [PMID: 33523817 DOI: 10.1109/toh.2021.3055842] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
The neurophysiological characteristics of sustained attention states are unclear in discrete multi-finger force control tasks. In this article, we developed an immersive visuo-haptic task for conducting stimulus-response measurements. Visual cues were randomly provided to signify the required amplitude and tolerance of fingertip force. Participants were required to respond to the visual cues by pressing force transducers using their fingertips. Response time variation was taken as a behavioral measure of sustained attention states during the task. 50% low-variability trials were classified as the optimal state and the other high-variability trials were classified as the suboptimal state using z-scoring over time. A 64-channel electroencephalogram (EEG) acquisition system was used to collect brain activities during the tasks. The haptics-elicited potential amplitude at 20 ∼ 40 ms in latency and over the frontal-central region significantly decreased in the optimal state. Furthermore, the alpha-band power in the spectra of 8 ∼ 13 Hz was significantly suppressed in the frontal-central, right temporal, and parietal regions in the optimal state. Taken together, we have identified neuroelectrophysiological features that were associated with sustained attention during multi-finger force control tasks, which would be potentially used in the development of closed-loop attention detection and training systems exploiting haptic interaction.
Collapse
|
42
|
Zhang G, Etemad A. Capsule Attention for Multimodal EEG-EOG Representation Learning With Application to Driver Vigilance Estimation. IEEE Trans Neural Syst Rehabil Eng 2021; 29:1138-1149. [PMID: 34129500 DOI: 10.1109/tnsre.2021.3089594] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Driver vigilance estimation is an important task for transportation safety. Wearable and portable brain-computer interface devices provide a powerful means for real-time monitoring of the vigilance level of drivers to help with avoiding distracted or impaired driving. In this paper, we propose a novel multimodal architecture for in-vehicle vigilance estimation from Electroencephalogram and Electrooculogram. To enable the system to focus on the most salient parts of the learned multimodal representations, we propose an architecture composed of a capsule attention mechanism following a deep Long Short-Term Memory (LSTM) network. Our model learns hierarchical dependencies in the data through the LSTM and capsule feature representation layers. To better explore the discriminative ability of the learned representations, we study the effect of the proposed capsule attention mechanism including the number of dynamic routing iterations as well as other parameters. Experiments show the robustness of our method by outperforming other solutions and baseline techniques, setting a new state-of-the-art. We then provide an analysis on different frequency bands and brain regions to evaluate their suitability for driver vigilance estimation. Lastly, an analysis on the role of capsule attention, multimodality, and robustness to noise is performed, highlighting the advantages of our approach.
Collapse
|
43
|
The Time Varying Networks of the Interoceptive Attention and Rest. eNeuro 2021; 8:ENEURO.0341-20.2021. [PMID: 33975858 PMCID: PMC8174797 DOI: 10.1523/eneuro.0341-20.2021] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2020] [Revised: 03/09/2021] [Accepted: 04/27/2021] [Indexed: 11/21/2022] Open
Abstract
Focused attention to spontaneous sensations is a dynamic process that demands interoceptive abilities. Failure to control it has been linked to neuropsychiatric disorders like illness-anxiety disorder. Regulatory strategies, such as focused attention meditation (FAM), may enhance the ability to control focused attention particularly to body sensations, which can be reflected on functional neuroanatomy. The functional connectivity (FC) related to focused attention has been described, however, the dynamic brain organization associated to this process and the differences to the resting state remains to be studied. To quantify the cerebral dynamic counterpart of focused attention to interoception, we examined fifteen experienced meditators while performing a 20-min attentional task to spontaneous sensations. Subjects underwent three scanning sessions obtaining a resting-state scan before and after the task. Sliding window dynamic FC (DFC) and k-means clustering identified five recurrent FC patterns along the dorsal attention network (DAN), default mode network (DMN), and frontoparietal network (FPN). Subjects remained longer in a low connectivity brain pattern during the resting conditions. By contrast, subjects spent a higher proportion of time in complex patterns during the task than rest. Moreover, a carry-over effect in FC was observed following the interoceptive task performance, suggestive of an active role in the learning process linked to cognitive training. Our results suggest that focused attention to interoceptive processes, demands a dynamic brain organization with specific features that distinguishes it from the resting condition. This approach may provide new insights characterizing the neural basis of the focused attention, an essential component for human adaptability.
Collapse
|
44
|
Ramírez-Moreno MA, Díaz-Padilla M, Valenzuela-Gómez KD, Vargas-Martínez A, Tudón-Martínez JC, Morales-Menendez R, Ramírez-Mendoza RA, Pérez-Henríquez BL, Lozoya-Santos JDJ. EEG-Based Tool for Prediction of University Students' Cognitive Performance in the Classroom. Brain Sci 2021; 11:698. [PMID: 34073242 PMCID: PMC8227309 DOI: 10.3390/brainsci11060698] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Revised: 05/14/2021] [Accepted: 05/15/2021] [Indexed: 11/16/2022] Open
Abstract
This study presents a neuroengineering-based machine learning tool developed to predict students' performance under different learning modalities. Neuroengineering tools are used to predict the learning performance obtained through two different modalities: text and video. Electroencephalographic signals were recorded in the two groups during learning tasks, and performance was evaluated with tests. The results show the video group obtained a better performance than the text group. A correlation analysis was implemented to find the most relevant features to predict students' performance, and to design the machine learning tool. This analysis showed a negative correlation between students' performance and the (theta/alpha) ratio, and delta power, which are indicative of mental fatigue and drowsiness, respectively. These results indicate that users in a non-fatigued and well-rested state performed better during learning tasks. The designed tool obtained 85% precision at predicting learning performance, as well as correctly identifying the video group as the most efficient modality.
Collapse
Affiliation(s)
- Mauricio A. Ramírez-Moreno
- School of Engineering and Science, Mechatronics Department, Tecnologico de Monterrey, Eugenio Garza Sada 2501 Sur, Monterrey 64849, Mexico; (M.A.R.-M.); (M.D.-P.); (K.D.V.-G.); (A.V.-M.); (R.M.-M.); (R.A.R.-M.)
| | - Mariana Díaz-Padilla
- School of Engineering and Science, Mechatronics Department, Tecnologico de Monterrey, Eugenio Garza Sada 2501 Sur, Monterrey 64849, Mexico; (M.A.R.-M.); (M.D.-P.); (K.D.V.-G.); (A.V.-M.); (R.M.-M.); (R.A.R.-M.)
| | - Karla D. Valenzuela-Gómez
- School of Engineering and Science, Mechatronics Department, Tecnologico de Monterrey, Eugenio Garza Sada 2501 Sur, Monterrey 64849, Mexico; (M.A.R.-M.); (M.D.-P.); (K.D.V.-G.); (A.V.-M.); (R.M.-M.); (R.A.R.-M.)
| | - Adriana Vargas-Martínez
- School of Engineering and Science, Mechatronics Department, Tecnologico de Monterrey, Eugenio Garza Sada 2501 Sur, Monterrey 64849, Mexico; (M.A.R.-M.); (M.D.-P.); (K.D.V.-G.); (A.V.-M.); (R.M.-M.); (R.A.R.-M.)
| | - Juan C. Tudón-Martínez
- School of Engineering and Technologies, Universidad de Monterrey, San Pedro Garza García 66238, Mexico;
| | - Rubén Morales-Menendez
- School of Engineering and Science, Mechatronics Department, Tecnologico de Monterrey, Eugenio Garza Sada 2501 Sur, Monterrey 64849, Mexico; (M.A.R.-M.); (M.D.-P.); (K.D.V.-G.); (A.V.-M.); (R.M.-M.); (R.A.R.-M.)
| | - Ricardo A. Ramírez-Mendoza
- School of Engineering and Science, Mechatronics Department, Tecnologico de Monterrey, Eugenio Garza Sada 2501 Sur, Monterrey 64849, Mexico; (M.A.R.-M.); (M.D.-P.); (K.D.V.-G.); (A.V.-M.); (R.M.-M.); (R.A.R.-M.)
| | | | - Jorge de J. Lozoya-Santos
- School of Engineering and Science, Mechatronics Department, Tecnologico de Monterrey, Eugenio Garza Sada 2501 Sur, Monterrey 64849, Mexico; (M.A.R.-M.); (M.D.-P.); (K.D.V.-G.); (A.V.-M.); (R.M.-M.); (R.A.R.-M.)
| |
Collapse
|
45
|
Zhang S, Yan Z, Sapkota S, Zhao S, Ooi WT. Moment-to-Moment Continuous Attention Fluctuation Monitoring through Consumer-Grade EEG Device. SENSORS (BASEL, SWITZERLAND) 2021; 21:3419. [PMID: 34069027 PMCID: PMC8156270 DOI: 10.3390/s21103419] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Revised: 04/28/2021] [Accepted: 05/08/2021] [Indexed: 11/16/2022]
Abstract
While numerous studies have explored using various sensing techniques to measure attention states, moment-to-moment attention fluctuation measurement is unavailable. To bridge this gap, we applied a novel paradigm in psychology, the gradual-onset continuous performance task (gradCPT), to collect the ground truth of attention states. GradCPT allows for the precise labeling of attention fluctuation on an 800 ms time scale. We then developed a new technique for measuring continuous attention fluctuation, based on a machine learning approach that uses the spectral properties of EEG signals as the main features. We demonstrated that, even using a consumer grade EEG device, the detection accuracy of moment-to-moment attention fluctuations was 73.49%. Next, we empirically validated our technique in a video learning scenario and found that our technique match with the classification obtained through thought probes, with an average F1 score of 0.77. Our results suggest the effectiveness of using gradCPT as a ground truth labeling method and the feasibility of using consumer-grade EEG devices for continuous attention fluctuation detection.
Collapse
Affiliation(s)
- Shan Zhang
- NUS-HCI Lab, Department of Computer Science, School of Computing, National University of Singapore, Singapore 117417, Singapore; (Z.Y.); (S.S.); (S.Z.)
| | - Zihan Yan
- NUS-HCI Lab, Department of Computer Science, School of Computing, National University of Singapore, Singapore 117417, Singapore; (Z.Y.); (S.S.); (S.Z.)
- College of Computer Science and Technology, Zhejiang University, Hangzhou 310058, China
| | - Shardul Sapkota
- NUS-HCI Lab, Department of Computer Science, School of Computing, National University of Singapore, Singapore 117417, Singapore; (Z.Y.); (S.S.); (S.Z.)
- Division of Science, Yale-NUS College, Singapore 138527, Singapore
| | - Shengdong Zhao
- NUS-HCI Lab, Department of Computer Science, School of Computing, National University of Singapore, Singapore 117417, Singapore; (Z.Y.); (S.S.); (S.Z.)
| | - Wei Tsang Ooi
- National University of Singapore, Singapore 117417, Singapore;
| |
Collapse
|
46
|
Trenado C, Pedroarena-Leal N, Ruge D. Considering the Role of Neurodidactics in Medical Education as Inspired by Learning Studies and Music Education. MEDICAL SCIENCE EDUCATOR 2021; 31:267-272. [PMID: 34457881 PMCID: PMC8368535 DOI: 10.1007/s40670-020-01176-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 11/20/2020] [Indexed: 06/13/2023]
Affiliation(s)
- Carlos Trenado
- Institute of Clinical Neuroscience and Medical Psychology, Medical Faculty, Heinrich Heine University, Moorenstr. 5, 40225 Duesseldorf, Germany
| | - Nicole Pedroarena-Leal
- UCL-Institute of Neurology, University College London (UCL), Queen Square, London, WC1N 3BG UK
| | - Diane Ruge
- UCL-Institute of Neurology, University College London (UCL), Queen Square, London, WC1N 3BG UK
| |
Collapse
|
47
|
A Hybrid EMD-Wavelet EEG Feature Extraction Method for the Classification of Students' Interest in the Mathematics Classroom. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2021; 2021:6617462. [PMID: 33564299 PMCID: PMC7850834 DOI: 10.1155/2021/6617462] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/17/2020] [Revised: 12/30/2020] [Accepted: 01/03/2021] [Indexed: 11/21/2022]
Abstract
Situational interest (SI) is one of the promising states that can improve student's learning and increase the acquired knowledge. Electroencephalogram- (EEG-) based detection of SI could assist in understanding SI neuroscientific causes that, as a result, could explain the SI role in student's learning. In this study, 26 participants were selected based on questionnaires to participate in the mathematics classroom experiment. SI and personal interest (PI) questionnaires along with knowledge tests were undertaken to measure student's interest and knowledge levels. A hybrid method combining empirical mode decomposition (EMD) and wavelet transform was developed and employed for feature extraction. The proposed method showed significant difference using the multivariate analysis of variance (MANOVA) test and consistently outperformed other methods in the classification performance using weighted k-nearest neighbours (wkNN). The high classification accuracy of 85.7% with the sensitivity of 81.8% and specificity of 90% revealed that brain oscillation patterns of high SI students are somewhat different than students with low or no SI. In addition, the result suggests that the delta rhythm could have a significant effect on cognitive processing.
Collapse
|
48
|
An Adaptive EEG Feature Extraction Method Based on Stacked Denoising Autoencoder for Mental Fatigue Connectivity. Neural Plast 2021; 2021:3965385. [PMID: 33552154 PMCID: PMC7843194 DOI: 10.1155/2021/3965385] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2020] [Revised: 12/15/2020] [Accepted: 01/06/2021] [Indexed: 11/30/2022] Open
Abstract
Mental fatigue is a common psychobiological state elected by prolonged cognitive activities. Although, the performance and the disadvantage of the mental fatigue have been well known, its connectivity among the multiareas of the brain has not been thoroughly studied yet. This is important for the clarification of the mental fatigue mechanism. However, the common method of connectivity analysis based on EEG cannot get rid of the interference from strong noise. In this paper, an adaptive feature extraction model based on stacked denoising autoencoder has been proposed. The signal to noise ratio of the extracted feature has been analyzed. Compared with principal component analysis, the proposed method can significantly improve the signal to noise ratio and suppress the noise interference. The proposed method has been applied on the analysis of mental fatigue connectivity. The causal connectivity among the frontal, motor, parietal, and visual areas under the awake, fatigue, and sleep deprivation conditions has been analyzed, and different patterns of connectivity between conditions have been revealed. The connectivity direction under awake condition and sleep deprivation condition is opposite. Moreover, there is a complex and bidirectional connectivity relationship, from the anterior areas to the posterior areas and from the posterior areas to the anterior areas, under fatigue condition. These results imply that there are different brain patterns on the three conditions. This study provides an effective method for EEG analysis. It may be favorable to disclose the underlying mechanism of mental fatigue by connectivity analysis.
Collapse
|
49
|
Moè A, Frenzel AC, Au L, Taxer JL. Displayed enthusiasm attracts attention and improves recall. BRITISH JOURNAL OF EDUCATIONAL PSYCHOLOGY 2020; 91:911-927. [PMID: 33325548 DOI: 10.1111/bjep.12399] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2020] [Revised: 11/20/2020] [Indexed: 11/28/2022]
Abstract
BACKGROUND Instructor enthusiasm has been shown to enhance a range of positive student outcomes including recall, but the underlying mechanisms for the favourable effects of teacher enthusiasm are still largely unknown. AIMS We hypothesized that attention paid to the instructor is one mechanism and that the positive effects of enthusiasm will disappear when attention is captured by another task. SAMPLES In a series of three studies, we involved fourth and fifth graders in listening to texts read aloud with high or low levels of displayed enthusiasm. METHODS In Study 1, we obtained self-reported and observed behavioural indicators of attention while students were read texts with high versus low enthusiasm. In Study 2, we additionally manipulated attention by comparing a group who performed a concurrent attentional task while listening to the texts read with high or low enthusiasm to a group who only listened to the texts. In Study 3, we compared the attention-catching concurrent task used in Study 2 to a non-attention-catching dual task. RESULTS AND CONCLUSIONS Our results confirm that displayed enthusiasm captures attention and that attention partially explains the positive effect of displayed enthusiasm on recall.
Collapse
Affiliation(s)
- Angelica Moè
- Department of General Psychology, University of Padova, Italy
| | - Anne C Frenzel
- Department of Psychology, Ludwig Maximilians University of Munich, Germany
| | - Lik Au
- Department of Psychology, Ludwig Maximilians University of Munich, Germany
| | - Jamie L Taxer
- Department of Psychology, Stanford University, California, USA
| |
Collapse
|
50
|
Fontanillo Lopez CA, Li G, Zhang D. Beyond Technologies of Electroencephalography-Based Brain-Computer Interfaces: A Systematic Review From Commercial and Ethical Aspects. Front Neurosci 2020; 14:611130. [PMID: 33390892 PMCID: PMC7773904 DOI: 10.3389/fnins.2020.611130] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2020] [Accepted: 11/13/2020] [Indexed: 01/22/2023] Open
Abstract
The deployment of electroencephalographic techniques for commercial applications has undergone a rapid growth in recent decades. As they continue to expand in the consumer markets as suitable techniques for monitoring the brain activity, their transformative potential necessitates equally significant ethical inquiries. One of the main questions, which arises then when evaluating these kinds of applications, is whether they should be aligned or not with the main ethical concerns reported by scholars and experts. Thus, the present work attempts to unify these disciplines of knowledge by performing a comprehensive scan of the major electroencephalographic market applications as well as their most relevant ethical concerns arising from the existing literature. In this literature review, different databases were consulted, which presented conceptual and empirical discussions and findings about commercial and ethical aspects of electroencephalography. Subsequently, the content was extracted from the articles and the main conclusions were presented. Finally, an external assessment of the outcomes was conducted in consultation with an expert panel in some of the topic areas such as biomedical engineering, biomechatronics, and neuroscience. The ultimate purpose of this review is to provide a genuine insight into the cutting-edge practical attempts at electroencephalography. By the same token, it seeks to highlight the overlap between the market needs and the ethical standards that should govern the deployment of electroencephalographic consumer-grade solutions, providing a practical approach that overcomes the engineering myopia of certain ethical discussions.
Collapse
Affiliation(s)
| | - Guangye Li
- The Robotics Institute, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Dingguo Zhang
- The Department of Electronic and Electrical Engineering, University of Bath, Bath, United Kingdom
| |
Collapse
|