1
|
Wireless EEG: A survey of systems and studies. Neuroimage 2023; 269:119774. [PMID: 36566924 DOI: 10.1016/j.neuroimage.2022.119774] [Citation(s) in RCA: 34] [Impact Index Per Article: 34.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Revised: 11/18/2022] [Accepted: 11/27/2022] [Indexed: 12/24/2022] Open
Abstract
The popular brain monitoring method of electroencephalography (EEG) has seen a surge in commercial attention in recent years, focusing mostly on hardware miniaturization. This has led to a varied landscape of portable EEG devices with wireless capability, allowing them to be used by relatively unconstrained users in real-life conditions outside of the laboratory. The wide availability and relative affordability of these devices provide a low entry threshold for newcomers to the field of EEG research. The large device variety and the at times opaque communication from their manufacturers, however, can make it difficult to obtain an overview of this hardware landscape. Similarly, given the breadth of existing (wireless) EEG knowledge and research, it can be challenging to get started with novel ideas. Therefore, this paper first provides a list of 48 wireless EEG devices along with a number of important-sometimes difficult-to-obtain-features and characteristics to enable their side-by-side comparison, along with a brief introduction to each of these aspects and how they may influence one's decision. Secondly, we have surveyed previous literature and focused on 110 high-impact journal publications making use of wireless EEG, which we categorized by application and analyzed for device used, number of channels, sample size, and participant mobility. Together, these provide a basis for informed decision making with respect to hardware and experimental precedents when considering new, wireless EEG devices and research. At the same time, this paper provides background material and commentary about pitfalls and caveats regarding this increasingly accessible line of research.
Collapse
|
2
|
Bolouki A. Neurobiological effects of urban built and natural environment on mental health: systematic review. REVIEWS ON ENVIRONMENTAL HEALTH 2023; 38:169-179. [PMID: 35112526 DOI: 10.1515/reveh-2021-0137] [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: 09/29/2021] [Accepted: 01/15/2022] [Indexed: 06/14/2023]
Abstract
Although rapid global urbanization improves people in many ways, it also increases the prevalence of major mental disorders in urban communities. Exposure to natural surroundings, whether real or virtual, on the other hand, has been found to reduce arousal and stress. The purpose of this study is to provide an overview of the existing literature on how brain function changes when exposed to natural and urban settings. As a highly effective technique for determining human brain activity, this review considers literature using neuroimaging techniques, i.e., electroencephalography (EEG), functional magnetic resonance imaging (fMRI), and functional near-infrared spectroscopy (fNIRS). SCOPUS and PubMed were searched for peer-reviewed literature published prior to September 2021. Twenty-six sources were included, returning 263 papers; 18 empirical articles published from 1991 to 2021 were included in the final synthesis. EEG findings were generally consistent with those obtained from fMRI/NIRS data. Natural settings were linked to greater alpha EEG values and fewer demands on information processing and stronger functional connectivity in fMRI/NIRS studies, which indicate feelings of relaxation and restoration. These findings offer a better understanding of the functional activities during environmental exposures and also imply that nature exposure improves cognitive functions and mental health.
Collapse
|
3
|
Wascher E, Reiser J, Rinkenauer G, Larrá M, Dreger FA, Schneider D, Karthaus M, Getzmann S, Gutberlet M, Arnau S. Neuroergonomics on the Go: An Evaluation of the Potential of Mobile EEG for Workplace Assessment and Design. HUMAN FACTORS 2023; 65:86-106. [PMID: 33861182 PMCID: PMC9846382 DOI: 10.1177/00187208211007707] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/15/2021] [Accepted: 03/13/2021] [Indexed: 06/12/2023]
Abstract
OBJECTIVE We demonstrate and discuss the use of mobile electroencephalogram (EEG) for neuroergonomics. Both technical state of the art as well as measures and cognitive concepts are systematically addressed. BACKGROUND Modern work is increasingly characterized by information processing. Therefore, the examination of mental states, mental load, or cognitive processing during work is becoming increasingly important for ergonomics. RESULTS Mobile EEG allows to measure mental states and processes under real live conditions. It can be used for various research questions in cognitive neuroergonomics. Besides measures in the frequency domain that have a long tradition in the investigation of mental fatigue, task load, and task engagement, new approaches-like blink-evoked potentials-render event-related analyses of the EEG possible also during unrestricted behavior. CONCLUSION Mobile EEG has become a valuable tool for evaluating mental states and mental processes on a highly objective level during work. The main advantage of this technique is that working environments don't have to be changed while systematically measuring brain functions at work. Moreover, the workflow is unaffected by such neuroergonomic approaches.
Collapse
Affiliation(s)
- Edmund Wascher
- IfADo – Leibniz Research Centre for Working Environment and
Human Factors, Dortmund, Germany
| | - Julian Reiser
- IfADo – Leibniz Research Centre for Working Environment and
Human Factors, Dortmund, Germany
| | - Gerhard Rinkenauer
- IfADo – Leibniz Research Centre for Working Environment and
Human Factors, Dortmund, Germany
| | - Mauro Larrá
- IfADo – Leibniz Research Centre for Working Environment and
Human Factors, Dortmund, Germany
| | - Felix A. Dreger
- IfADo – Leibniz Research Centre for Working Environment and
Human Factors, Dortmund, Germany
| | - Daniel Schneider
- IfADo – Leibniz Research Centre for Working Environment and
Human Factors, Dortmund, Germany
| | - Melanie Karthaus
- IfADo – Leibniz Research Centre for Working Environment and
Human Factors, Dortmund, Germany
| | - Stephan Getzmann
- IfADo – Leibniz Research Centre for Working Environment and
Human Factors, Dortmund, Germany
| | | | - Stefan Arnau
- IfADo – Leibniz Research Centre for Working Environment and
Human Factors, Dortmund, Germany
| |
Collapse
|
4
|
Johnson T, Kanjo E, Woodward K. DigitalExposome: quantifying impact of urban environment on wellbeing using sensor fusion and deep learning. COMPUTATIONAL URBAN SCIENCE 2023; 3:14. [PMID: 36970599 PMCID: PMC10025809 DOI: 10.1007/s43762-023-00088-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Revised: 02/20/2023] [Accepted: 03/05/2023] [Indexed: 03/29/2023]
Abstract
The increasing level of air pollutants (e.g. particulates, noise and gases) within the atmosphere are impacting mental wellbeing. In this paper, we define the term 'DigitalExposome' as a conceptual framework that takes us closer towards understanding the relationship between environment, personal characteristics, behaviour and wellbeing using multimodal mobile sensing technology. Specifically, we simultaneously collected (for the first time) multi-sensor data including urban environmental factors (e.g. air pollution including: Particulate Matter (PM1), (PM2.5), (PM10), Oxidised, Reduced, Ammonia (NH3) and Noise, People Count in the vicinity), body reaction (physiological reactions including: EDA, HR, HRV, Body Temperature, BVP and movement) and individuals' perceived responses (e.g. self-reported valence) in urban settings. Our users followed a pre-specified urban path and collected the data using a comprehensive sensing edge device. The data is instantly fused, time-stamped and geo-tagged at the point of collection. A range of multivariate statistical analysis techniques have been applied including Principle Component Analysis, Regression and Spatial Visualisations to unravel the relationship between the variables. Results showed that Electrodermal Activity (EDA) and Heart Rate Variability (HRV) are noticeably impacted by the level of Particulate Matter in the environment. Furthermore, we adopted Convolutional Neural Network (CNN) to classify self-reported wellbeing from the multimodal dataset which achieved an f1-score of 0.76.
Collapse
Affiliation(s)
- Thomas Johnson
- grid.12361.370000 0001 0727 0669Department of Computer Science, Nottingham Trent University, Nottingham, UK
| | - Eiman Kanjo
- grid.12361.370000 0001 0727 0669Department of Computer Science, Nottingham Trent University, Nottingham, UK
| | - Kieran Woodward
- grid.12361.370000 0001 0727 0669Department of Computer Science, Nottingham Trent University, Nottingham, UK
| |
Collapse
|
5
|
Ancora LA, Blanco-Mora DA, Alves I, Bonifácio A, Morgado P, Miranda B. Cities and neuroscience research: A systematic literature review. Front Psychiatry 2022; 13:983352. [PMID: 36440407 PMCID: PMC9684645 DOI: 10.3389/fpsyt.2022.983352] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Accepted: 10/21/2022] [Indexed: 11/11/2022] Open
Abstract
Background Cities are becoming the socio-economic hubs for most of the world's population. Understanding how our surroundings can mentally affect everyday life has become crucial to integrate environmental sustainability into urban development. The present review aims to explore the empirical studies investigating neural mechanisms underlying cognitive and emotional processes elicited by the exposure to different urban built and natural spaces. It also tries to identify new research questions and to leverage neurourbanism as a framework to achieve healthier and sustainable cities. Methods By following the PRISMA framework, we conducted a structured search on PubMed, ProQuest, Web of Science, and Scopus databases. Only articles related to how urban environment-built or natural-affects brain activity through objective measurement (with either imaging or electrophysiological techniques) were considered. Further inclusion criteria were studies on human adult populations, peer-reviewed, and in English language. Results Sixty-two articles met the inclusion criteria. They were qualitatively assessed and analyzed to determine the main findings and emerging concepts. Overall, the results suggest that urban built exposure (when compared to natural spaces) elicit activations in brain regions or networks strongly related to perceptual, attentional, and (spatial) cognitive demands. The city's-built environment also triggers neural circuits linked to stress and negative affect. Convergence of these findings was observed across neuroscience techniques, and for both laboratory and real-life settings. Additionally, evidence also showed associations between neural social stress processing with urban upbringing or current city living-suggesting a mechanistic link to certain mood and anxiety disorders. Finally, environmental diversity was found to be critical for positive affect and individual well-being. Conclusion Contemporary human-environment interactions and planetary challenges imply greater understanding of the neurological underpinnings on how the urban space affects cognition and emotion. This review provides scientific evidence that could be applied for policy making on improved urban mental health. Several studies showed that high-quality green or blue spaces, and bio-diverse urban areas, are important allies for positive neural, cognitive, and emotional processes. Nonetheless, the spatial perception in social contexts (e.g., city overcrowding) deserves further attention by urban planners and scientists. The implications of these observations for some theories in environmental psychology and research are discussed. Future work should take advantage of technological advancements to better characterize behavior, brain physiology, and environmental factors and apply them to the remaining complexity of contemporary cities.
Collapse
Affiliation(s)
- Leonardo A. Ancora
- Institute of Physiology, Lisbon School of Medicine, University of Lisbon, Lisbon, Portugal
| | | | - Inês Alves
- Institute of Molecular Medicine, Lisbon School of Medicine, University of Lisbon, Lisbon, Portugal
| | - Ana Bonifácio
- Centre of Geographical Studies, Institute of Geography and Spatial Planning, University of Lisbon, Lisbon, Portugal
| | - Paulo Morgado
- Centre of Geographical Studies, Institute of Geography and Spatial Planning, University of Lisbon, Lisbon, Portugal
| | - Bruno Miranda
- Institute of Physiology, Lisbon School of Medicine, University of Lisbon, Lisbon, Portugal
- Institute of Molecular Medicine, Lisbon School of Medicine, University of Lisbon, Lisbon, Portugal
| |
Collapse
|
6
|
Vasconcelos B, Fiedler P, Machts R, Haueisen J, Fonseca C. The Arch Electrode: A Novel Dry Electrode Concept for Improved Wearing Comfort. Front Neurosci 2021; 15:748100. [PMID: 34733134 PMCID: PMC8558300 DOI: 10.3389/fnins.2021.748100] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2021] [Accepted: 09/27/2021] [Indexed: 11/27/2022] Open
Abstract
Electroencephalography (EEG) is increasingly used for repetitive and prolonged applications like neurofeedback, brain computer interfacing, and long-term intermittent monitoring. Dry-contact electrodes enable rapid self-application. A common drawback of existing dry electrodes is the limited wearing comfort during prolonged application. We propose a novel dry Arch electrode. Five semi-circular arches are arranged parallelly on a common baseplate. The electrode substrate material is a flexible thermoplastic polyurethane (TPU) produced by additive manufacturing. A chemical coating of Silver/Silver-Chloride (Ag/AgCl) is applied by electroless plating using a novel surface functionalization method. Arch electrodes were manufactured and validated in terms of mechanical durability, electrochemical stability, in vivo applicability, and signal characteristics. We compare the results of the dry arch electrodes with dry pin-shaped and conventional gel-based electrodes. 21-channel EEG recordings were acquired on 10 male and 5 female volunteers. The tests included resting state EEG, alpha activity, and a visual evoked potential. Wearing comfort was rated by the subjects directly after application, as well as at 30 min and 60 min of wearing. Our results show that the novel plating technique provides a well-adhering electrically conductive and electrochemically stable coating, withstanding repetitive strain and bending tests. The signal quality of the Arch electrodes is comparable to pin-shaped dry electrodes. The average channel reliability of the Arch electrode setup was 91.9 ± 9.5%. No considerable differences in signal characteristics have been observed for the gel-based, dry pin-shaped, and arch-shaped electrodes after the identification and exclusion of bad channels. The comfort was improved in comparison to pin-shaped electrodes and enabled applications of over 60 min duration. Arch electrodes required individual adaptation of the electrodes to the orientation and hairstyle of the volunteers. This initial preparation time of the 21-channel cap increased from an average of 5 min for pin-like electrodes to 15 min for Arch electrodes and 22 min for gel-based electrodes. However, when re-applying the arch electrode cap on the same volunteer, preparation times of pin-shaped and arch-shaped electrodes were comparable. In summary, our results indicate the applicability of the novel Arch electrode and coating for EEG acquisition. The novel electrode enables increased comfort for prolonged dry-contact measurement.
Collapse
Affiliation(s)
- Beatriz Vasconcelos
- Departamento de Engenharia Metalúrgica e de Materiais, Faculdade de Engenharia, Universidade do Porto, Porto, Portugal.,CEMUC - Department of Mechanical Engineering, University of Coimbra, Coimbra, Portugal
| | - Patrique Fiedler
- Institute of Biomedical Engineering and Informatics, Technische Universität Ilmenau, Ilmenau, Germany
| | - René Machts
- Institute of Biomedical Engineering and Informatics, Technische Universität Ilmenau, Ilmenau, Germany
| | - Jens Haueisen
- Institute of Biomedical Engineering and Informatics, Technische Universität Ilmenau, Ilmenau, Germany.,Department of Neurology, Biomagnetic Center, Jena University Hospital, Jena, Germany
| | - Carlos Fonseca
- Departamento de Engenharia Metalúrgica e de Materiais, Faculdade de Engenharia, Universidade do Porto, Porto, Portugal.,LAETA/INEGI, Institute of Science and Innovation in Mechanical and Industrial Engineering, Porto, Portugal
| |
Collapse
|
7
|
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
|
8
|
Deep learning analysis based on multi-sensor fusion data for hemiplegia rehabilitation training system for stoke patients. ROBOTICA 2021. [DOI: 10.1017/s0263574721000801] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
AbstractBy recognizing the motion of the healthy side, the lower limb exoskeleton robot can provide therapy to the affected side of stroke patients. To improve the accuracy of motion intention recognition based on sensor data, the research based on deep learning was carried out. Eighty healthy subjects performed gait experiments under five different gait environments (flat ground, 10
${}^\circ$
upslope and downslope, and upstairs and downstairs) by simulating stroke patients. To facilitate the training and classification of the neural network, this paper presents template processing schemes to adapt to different data formats. The novel algorithm model of a hybrid network model based on convolutional neural network (CNN) and Long–short-term memory (LSTM) model is constructed. To mitigate the data-sparse problem, a spatial–temporal-embedded LSTM model (SQLSTM) combining spatial–temporal influence with the LSTM model is proposed. The proposed CNN-SQLSTM model is evaluated on a real trajectory dataset, and the results demonstrate the effectiveness of the proposed model. The proposed method will be used to guide the control strategy design of robot system for active rehabilitation training.
Collapse
|
9
|
Fleckney P, Bentley R. The urban public realm and adolescent mental health and wellbeing: A systematic review. Soc Sci Med 2021; 284:114242. [PMID: 34333404 DOI: 10.1016/j.socscimed.2021.114242] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2021] [Revised: 07/15/2021] [Accepted: 07/16/2021] [Indexed: 10/20/2022]
Abstract
Adolescent mental health is becoming a critical concern. Mental illness rates are rising and many psychological disorders first present symptoms during teenage years. Studies consistently show associations between the built environment and mental health, including internalising mental health disorders in adults, but the evidence for adolescents is less robust and few studies attempt to isolate causality. This review examines the relationship between the urban public realm and adolescent mental health and wellbeing. Our search yielded 24 studies for inclusion. We undertook qualitative synthesis of 20 cross-sectional studies and conducted a separate quality analysis of four longitudinal studies. Greenspace and neighbourhood quality are associated with adolescent mental health and wellbeing although this may be due more to residual confounding, selection effects and same-source bias than evidence for a causal effect. Furthermore, the few longitudinal studies that seek to test causality remain prone to these biases. Overall, we find little evidence of an effect of the urban public realm on adolescent mental health and wellbeing, which, we argue, reflects the difficulty of researching complex pathways between environments and health and highlights a challenge to the field. To address this challenge, we propose a research agenda that prioritises more and better data drawn from diverse study designs, and more and better theories developed from diverse epistemologies.
Collapse
Affiliation(s)
- Paul Fleckney
- Melbourne School of Design, Faculty of Architecture, Building and Planning, University of Melbourne, Masson Road, Parkville, Victoria, 3010, Australia.
| | - Rebecca Bentley
- NHMRC Centre of Research Excellence in Healthy Housing, Melbourne School of Population and Global Health, Faculty of Medicine, Dentistry and Health Sciences, The University of Melbourne, 207 Bouverie Street, Parkville, Victoria, 3010, Australia.
| |
Collapse
|
10
|
Investigating the mental health implications of urban environments with neuroscientific methods and mobile technologies: A systematic literature review. Health Place 2021; 70:102597. [PMID: 34107446 DOI: 10.1016/j.healthplace.2021.102597] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/13/2021] [Revised: 05/11/2021] [Accepted: 05/17/2021] [Indexed: 11/22/2022]
Abstract
Urbanization is an ongoing global process that is influencing and shaping individual mental health and well-being. This paper aims to provide an overview of the current literature containing state-of-the-art neuroscientific and mobile technologies that have been used to investigate the mental health implications of urban environments. Searches for peer-reviewed primary research articles were conducted in PubMed and SCOPUS, returning 33,443 papers; 90 empirical articles published from 1981 to 2021 were included in the final synthesis. Central findings suggest virtual reality and mobile electroencephalography to be the most commonly used methods, and demanding mood, affect, and health phenomena or states to be the most common concepts of study in both physical built settings and natural urban spaces. Recommendations for both future practice and study noting particular opportunities for future methodological contributions are discussed.
Collapse
|
11
|
Mironenko IA, Proskuriakova E, Rafikova V, Kozlova Y, Simonovich A, Proshina A, Danina E. Walking in St. Petersburg—Vienna Walks Continued. HUMAN ARENAS 2020. [DOI: 10.1007/s42087-019-00076-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
|
12
|
Lancheros-Cuesta DJ, Carrillo-Ramos A, Lancheros-Cuesta M. Evaluation of content adaptation. INTERNATIONAL JOURNAL OF WEB INFORMATION SYSTEMS 2019. [DOI: 10.1108/ijwis-11-2018-0078] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
PurposeStudents have learning difficulties, mainly in processes that involve attention and interpretation of written or spoken language. Technological tools allow to create computational platforms with adaptation aspects depending on the student’s characteristics. It is also important to highlight the progress of the measurement of cognitive processes such as attention through NeuroSky’s MindWave EEG sensors. This paper aims to present the results of analyzing attention levels of children with learning difficulties, based on the acquired brain waves. As a final result, an adaptive computational system that displays educational activities regarding educational profiles of children is obtained.Design/methodology/approachThe Kamachiy–Idukay platform was chosen to make the validation. The platform generates the educational activities according to the students’ profile. The validation phases were identification of the test environment, the first environment required a scenario that involved students with learning difficulties, to verify the functionality of the system, when analyzing cases of the students with learning difficulties; identification of two validation criteria, type of educational activity and attention difficulties of the students; and analysis of the brain signal when children interact with the educational content.FindingsThe adaptation of contents that include music and animations generate higher levels of attention in students with difficulty. The analysis of signals from the NeuroSky sensor to determine the attentional levels in children allowed a generation of content adapted to the characteristics of the difficulty in each child.Research limitations/implicationsFor the validation, it was necessary at the beginning of the activity to determine the stability of the signal emitted by the NeuroSky sensor. Two cases were studied in children with difficulty and their measure of attention versus adaptive contents.Practical implicationsA k-means algorithm was used to establish the attention levels of the children.Social implicationsChildren with learning difficulties have different learning styles, which implies an adaptation of content that generates an attentional process according to their characteristics.Originality/valueEvaluation content adaptation taking into account the signal brain sensor NeuroSky for learning process. The signal brain of the student when interacting with the activities is include in the student profile.
Collapse
|