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Deng H, Li M, Zuo H, Zhou H, Qi E, Wu X, Xu G. Personalized motor imagery prediction model based on individual difference of ERP. J Neural Eng 2024; 21:016027. [PMID: 38359457 DOI: 10.1088/1741-2552/ad29d6] [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: 07/28/2023] [Accepted: 02/15/2024] [Indexed: 02/17/2024]
Abstract
Objective. Motor imagery-based brain-computer interaction (MI-BCI) is a novel method of achieving human and external environment interaction that can assist individuals with motor disorders to rehabilitate. However, individual differences limit the utility of the MI-BCI. In this study, a personalized MI prediction model based on the individual difference of event-related potential (ERP) is proposed to solve the MI individual difference.Approach.A novel paradigm named action observation-based multi-delayed matching posture task evokes ERP during a delayed matching posture task phase by retrieving picture stimuli and videos, and generates MI electroencephalogram through action observation and autonomous imagery in an action observation-based motor imagery phase. Based on the correlation between the ERP and MI, a logistic regression-based personalized MI prediction model is built to predict each individual's suitable MI action. 32 subjects conducted the MI task with or without the help of the prediction model to select the MI action. Then classification accuracy of the MI task is used to evaluate the proposed model and three traditional MI methods.Main results.The personalized MI prediction model successfully predicts suitable action among 3 sets of daily actions. Under suitable MI action, the individual's ERP amplitude and event-related desynchronization (ERD) intensity are the largest, which helps to improve the accuracy by 14.25%.Significance.The personalized MI prediction model that uses the temporal ERP features to predict the classification accuracy of MI is feasible for improving the individual's MI-BCI performance, providing a new personalized solution for the individual difference and practical BCI application.
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Affiliation(s)
- Haodong Deng
- State Key Laboratory of Reliability and Intelligence of Electrical Equipment, 300132 Tianjin, People's Republic of China
- School of Health Sciences and Biomedical Engineering, Hebei University of Technology, 300132 Tianjin, People's Republic of China
- Hebei Key Laboratory of Bioelectromagnetics and Neuroengineering, 300132 Tianjin, People's Republic of China
- Tianjin Key Laboratory of Bioelectromagnetic Technology and Intelligent Health, 300132 Tianjin, People's Republic of China
| | - Mengfan Li
- State Key Laboratory of Reliability and Intelligence of Electrical Equipment, 300132 Tianjin, People's Republic of China
- School of Health Sciences and Biomedical Engineering, Hebei University of Technology, 300132 Tianjin, People's Republic of China
- Hebei Key Laboratory of Bioelectromagnetics and Neuroengineering, 300132 Tianjin, People's Republic of China
- Tianjin Key Laboratory of Bioelectromagnetic Technology and Intelligent Health, 300132 Tianjin, People's Republic of China
| | - Haoxin Zuo
- State Key Laboratory of Reliability and Intelligence of Electrical Equipment, 300132 Tianjin, People's Republic of China
- School of Health Sciences and Biomedical Engineering, Hebei University of Technology, 300132 Tianjin, People's Republic of China
- Hebei Key Laboratory of Bioelectromagnetics and Neuroengineering, 300132 Tianjin, People's Republic of China
- Tianjin Key Laboratory of Bioelectromagnetic Technology and Intelligent Health, 300132 Tianjin, People's Republic of China
| | - Huihui Zhou
- Peng Cheng Laboratory, 518000 Shenzhen, People's Republic of China
| | - Enming Qi
- State Key Laboratory of Reliability and Intelligence of Electrical Equipment, 300132 Tianjin, People's Republic of China
- School of Health Sciences and Biomedical Engineering, Hebei University of Technology, 300132 Tianjin, People's Republic of China
- Hebei Key Laboratory of Bioelectromagnetics and Neuroengineering, 300132 Tianjin, People's Republic of China
- Tianjin Key Laboratory of Bioelectromagnetic Technology and Intelligent Health, 300132 Tianjin, People's Republic of China
| | - Xue Wu
- State Key Laboratory of Reliability and Intelligence of Electrical Equipment, 300132 Tianjin, People's Republic of China
- School of Health Sciences and Biomedical Engineering, Hebei University of Technology, 300132 Tianjin, People's Republic of China
- Hebei Key Laboratory of Bioelectromagnetics and Neuroengineering, 300132 Tianjin, People's Republic of China
- Tianjin Key Laboratory of Bioelectromagnetic Technology and Intelligent Health, 300132 Tianjin, People's Republic of China
| | - Guizhi Xu
- State Key Laboratory of Reliability and Intelligence of Electrical Equipment, 300132 Tianjin, People's Republic of China
- School of Health Sciences and Biomedical Engineering, Hebei University of Technology, 300132 Tianjin, People's Republic of China
- Hebei Key Laboratory of Bioelectromagnetics and Neuroengineering, 300132 Tianjin, People's Republic of China
- Tianjin Key Laboratory of Bioelectromagnetic Technology and Intelligent Health, 300132 Tianjin, People's Republic of China
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Ke Y, Wang T, He F, Liu S, Ming D. Enhancing EEG-based cross-day mental workload classification using periodic component of power spectrum. J Neural Eng 2023; 20:066028. [PMID: 37995362 DOI: 10.1088/1741-2552/ad0f3d] [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: 05/02/2023] [Accepted: 11/23/2023] [Indexed: 11/25/2023]
Abstract
Objective. The day-to-day variability of electroencephalogram (EEG) poses a significant challenge to decode human brain activity in EEG-based passive brain-computer interfaces (pBCIs). Conventionally, a time-consuming calibration process is required to collect data from users on a new day to ensure the performance of the machine learning-based decoding model, which hinders the application of pBCIs to monitor mental workload (MWL) states in real-world settings.Approach. This study investigated the day-to-day stability of the raw power spectral density (PSD) and their periodic and aperiodic components decomposed by the Fitting Oscillations and One-Over-F algorithm. In addition, we validated the feasibility of using periodic components to improve cross-day MWL classification performance.Main results. Compared to the raw PSD (69.9% ± 18.5%) and the aperiodic component (69.4% ± 19.2%), the periodic component had better day-to-day stability and significantly higher cross-day classification accuracy (84.2% ± 11.0%).Significance. These findings indicate that periodic components of EEG have the potential to be applied in decoding brain states for more robust pBCIs.
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Affiliation(s)
- Yufeng Ke
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, People's Republic of China
- Haihe Laboratory of Brain-computer Interaction and Human-machine Integration, Tianjin, People's Republic of China
| | - Tao Wang
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, People's Republic of China
- Haihe Laboratory of Brain-computer Interaction and Human-machine Integration, Tianjin, People's Republic of China
| | - Feng He
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, People's Republic of China
- Haihe Laboratory of Brain-computer Interaction and Human-machine Integration, Tianjin, People's Republic of China
| | - Shuang Liu
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, People's Republic of China
- Haihe Laboratory of Brain-computer Interaction and Human-machine Integration, Tianjin, People's Republic of China
| | - Dong Ming
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, People's Republic of China
- Haihe Laboratory of Brain-computer Interaction and Human-machine Integration, Tianjin, People's Republic of China
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Yousefi MR, Dehghani A, Taghaavifar H. Enhancing the accuracy of electroencephalogram-based emotion recognition through Long Short-Term Memory recurrent deep neural networks. Front Hum Neurosci 2023; 17:1174104. [PMID: 37881690 PMCID: PMC10597690 DOI: 10.3389/fnhum.2023.1174104] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2023] [Accepted: 09/25/2023] [Indexed: 10/27/2023] Open
Abstract
Introduction Emotions play a critical role in human communication, exerting a significant influence on brain function and behavior. One effective method of observing and analyzing these emotions is through electroencephalography (EEG) signals. Although numerous studies have been dedicated to emotion recognition (ER) using EEG signals, achieving improved accuracy in recognition remains a challenging task. To address this challenge, this paper presents a deep-learning approach for ER using EEG signals. Background ER is a dynamic field of research with diverse practical applications in healthcare, human-computer interaction, and affective computing. In ER studies, EEG signals are frequently employed as they offer a non-invasive and cost-effective means of measuring brain activity. Nevertheless, accurately identifying emotions from EEG signals poses a significant challenge due to the intricate and non-linear nature of these signals. Methods The present study proposes a novel approach for ER that encompasses multiple stages, including feature extraction, feature selection (FS) employing clustering, and classification using Dual-LSTM. To conduct the experiments, the DEAP dataset was employed, wherein a clustering technique was applied to Hurst's view and statistical features during the FS phase. Ultimately, Dual-LSTM was employed for accurate ER. Results The proposed method achieved a remarkable accuracy of 97.5% in accurately classifying emotions across four categories: arousal, valence, liking/disliking, dominance, and familiarity. This high level of accuracy serves as strong evidence for the effectiveness of the deep-learning approach to emotion recognition (ER) utilizing EEG signals. Conclusion The deep-learning approach proposed in this paper has shown promising results in emotion recognition using EEG signals. This method can be useful in various applications, such as developing more effective therapies for individuals with mood disorders or improving human-computer interaction by allowing machines to respond more intelligently to users' emotional states. However, further research is needed to validate the proposed method on larger datasets and to investigate its applicability to real-world scenarios.
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Affiliation(s)
- Mohammad Reza Yousefi
- Department of Electrical Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran
- Digital Processing and Machine Vision Research Center, Najafabad Branch, Islamic Azad University, Najafabad, Iran
| | - Amin Dehghani
- Department of Electrical Engineering, K. N. Toosi University of Technology, Tehran, Iran
| | - Hamid Taghaavifar
- Digital Processing and Machine Vision Research Center, Najafabad Branch, Islamic Azad University, Najafabad, Iran
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Salehzadeh R, Soylu F, Jalili N. A comparative study of machine learning methods for classifying ERP scalp distribution. Biomed Phys Eng Express 2023; 9:045027. [PMID: 37279711 DOI: 10.1088/2057-1976/acdbd0] [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: 03/06/2023] [Accepted: 06/06/2023] [Indexed: 06/08/2023]
Abstract
Objective. Machine learning (ML) methods are used in different fields for classification and regression purposes with different applications. These methods are also used with various non-invasive brain signals, including Electroencephalography (EEG) signals to detect some patterns in the brain signals. ML methods are considered critical tools for EEG analysis since could overcome some of the limitations in the traditional methods of EEG analysis such as Event-related potentials (ERPs) analysis. The goal of this paper was to apply ML classification methods on ERP scalp distribution to investigate the performance of these methods in identifying numerical information carried in different finger-numeral configurations (FNCs). FNCs in their three forms of montring, counting, and non-canonical counting are used for communication, counting, and doing arithmetic across the world between children and even adults. Studies have shown the relationship between perceptual and semantic processing of FNCs, and neural differences in visually identifying different types of FNCs.Approach.A publicly available 32-channel EEG dataset recorded for 38 participants while they were shown a picture of an FNC (i.e., three categories and four numbers of 1,2,3, and 4) was used. EEG data were pre-processed and ERP scalp distribution of different FNCs was classified across time by six ML methods, including support vector machine, linear discriminant analysis, naïve Bayes, decision tree, K-nearest neighbor, and neural network. The classification was conducted in two conditions: classifying all FNCs together (i.e., 12 classes) and classifying FNCs of each category separately (i.e., 4 classes).Results.The support vector machine had the highest classification accuracy for both conditions. For classifying all FNCs together, the K-nearest neighbor was the next in line; however, the neural network could retrieve numerical information from the FNCs for category-specific classification.Significance.The significance of this study is in exploring the application of multiple ML methods in recognizing numerical information contained in ERP scalp distribution of different finger-numeral configurations.
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Affiliation(s)
- Roya Salehzadeh
- Department of Mechanical Engineering, The University of Alabama, Tuscaloosa, AL 35487, United States of America
| | - Firat Soylu
- Department of Educational Studies, The University of Alabama, Tuscaloosa, AL 35487, United States of America
| | - Nader Jalili
- Department of Mechanical Engineering, The University of Alabama, Tuscaloosa, AL 35487, United States of America
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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.
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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
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Reece R, Bornioli A, Bray I, Alford C. Exposure to Green and Historic Urban Environments and Mental Well-Being: Results from EEG and Psychometric Outcome Measures. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:13052. [PMID: 36293634 PMCID: PMC9603209 DOI: 10.3390/ijerph192013052] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Revised: 10/06/2022] [Accepted: 10/06/2022] [Indexed: 06/16/2023]
Abstract
Previous studies have identified the benefits of exposure to green or historic environments using qualitative methods and psychometric measures, but studies using a combination of measures are lacking. This study builds on current literature by focusing specifically on green and historic urban environments and using both psychological and physiological measures to investigate the impact of virtual exposure on well-being. Results from the psychological measures showed that the presence of historic elements was associated with a significantly stronger recuperation of hedonic tone (p = 0.01) and reduction in stress (p = 0.04). However, the presence of greenness had no significant effect on hedonic tone or stress. In contrast, physiological measures (EEG) showed significantly lower levels of alpha activity (p < 0.001) in occipital regions of the brain when participants viewed green environments, reflecting increased engagement and visual attention. In conclusion, this study has added to the literature by showing the impact that historic environments can have on well-being, as well as highlighting a lack of concordance between psychological and physiological measures. This supports the use of a combination of subjective and direct objective measures in future research in this field.
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Affiliation(s)
- Rebecca Reece
- Centre for Public Health and Wellbeing, University of the West of England, Bristol BS16 1QY, UK
| | - Anna Bornioli
- Erasmus Centre for Urban, Port and Transport Economics, Erasmus University Rotterdam, Burgemeester Oudlaan 50, 3062 PA Rotterdam, The Netherlands
| | - Isabelle Bray
- Centre for Public Health and Wellbeing, University of the West of England, Bristol BS16 1QY, UK
| | - Chris Alford
- Psychological Sciences Research Group, University of the West of England, Bristol BS16 1QY, UK
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Recognition of the Mental Workloads of Pilots in the Cockpit Using EEG Signals. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12052298] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
The commercial flightdeck is a naturally multi-tasking work environment, one in which interruptions are frequent come in various forms, contributing in many cases to aviation incident reports. Automatic characterization of pilots’ workloads is essential to preventing these kind of incidents. In addition, minimizing the physiological sensor network as much as possible remains both a challenge and a requirement. Electroencephalogram (EEG) signals have shown high correlations with specific cognitive and mental states, such as workload. However, there is not enough evidence in the literature to validate how well models generalize in cases of new subjects performing tasks with workloads similar to the ones included during the model’s training. In this paper, we propose a convolutional neural network to classify EEG features across different mental workloads in a continuous performance task test that partly measures working memory and working memory capacity. Our model is valid at the general population level and it is able to transfer task learning to pilot mental workload recognition in a simulated operational environment.
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Bagheri M, Power SD. Simultaneous Classification of Both Mental Workload and Stress Level Suitable for an Online Passive Brain-Computer Interface. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22020535. [PMID: 35062495 PMCID: PMC8781201 DOI: 10.3390/s22020535] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Revised: 01/03/2022] [Accepted: 01/09/2022] [Indexed: 05/10/2023]
Abstract
Research studies on EEG-based mental workload detection for a passive BCI generally focus on classifying cognitive states associated with the performance of tasks at different levels of difficulty, with no other aspects of the user's mental state considered. However, in real-life situations, different aspects of the user's state such as their cognitive (e.g., level of mental workload) and affective (e.g., level of stress/anxiety) states will often change simultaneously, and performance of a BCI system designed considering just one state may be unreliable. Moreover, multiple mental states may be relevant to the purposes of the BCI-for example both mental workload and stress level might be related to an aircraft pilot's risk of error-and the simultaneous prediction of states may be critical in maximizing the practical effectiveness of real-life online BCI systems. In this study we investigated the feasibility of performing simultaneous classification of mental workload and stress level in an online passive BCI. We investigated both subject-specific and cross-subject classification approaches, the latter with and without the application of a transfer learning technique to align the distributions of data from the training and test subjects. Using cross-subject classification with transfer learning in a simulated online analysis, we obtained accuracies of 77.5 ± 6.9% and 84.1 ± 5.9%, across 18 participants for mental workload and stress level detection, respectively.
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Affiliation(s)
- Mahsa Bagheri
- Faculty of Engineering and Applied Science, Memorial University of Newfoundland, St. John’s, NL A1C 5S7, Canada;
| | - Sarah D. Power
- Faculty of Engineering and Applied Science, Memorial University of Newfoundland, St. John’s, NL A1C 5S7, Canada;
- Faculty of Medicine, Memorial University of Newfoundland, St. John’s, NL A1C 5S7, Canada
- Correspondence:
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9
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Bak S, Shin J, Jeong J. Subdividing Stress Groups into Eustress and Distress Groups Using Laterality Index Calculated from Brain Hemodynamic Response. BIOSENSORS 2022; 12:bios12010033. [PMID: 35049661 PMCID: PMC8773747 DOI: 10.3390/bios12010033] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Revised: 01/07/2022] [Accepted: 01/07/2022] [Indexed: 12/28/2022]
Abstract
A stress group should be subdivided into eustress (low-stress) and distress (high-stress) groups to better evaluate personal cognitive abilities and mental/physical health. However, it is challenging because of the inconsistent pattern in brain activation. We aimed to ascertain the necessity of subdividing the stress groups. The stress group was screened by salivary alpha-amylase (sAA) and then, the brain’s hemodynamic reactions were measured by functional near-infrared spectroscopy (fNIRS) based on the near-infrared biosensor. We compared the two stress subgroups categorized by sAA using a newly designed emotional stimulus-response paradigm with an international affective picture system (IAPS) to enhance hemodynamic signals induced by the target effect. We calculated the laterality index for stress (LIS) from the measured signals to identify the dominantly activated cortex in both the subgroups. Both the stress groups exhibited brain activity in the right frontal cortex. Specifically, the eustress group exhibited the largest brain activity, whereas the distress group exhibited recessive brain activity, regardless of positive or negative stimuli. LIS values were larger in the order of the eustress, control, and distress groups; this indicates that the stress group can be divided into eustress and distress groups. We built a foundation for subdividing stress groups into eustress and distress groups using fNIRS.
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Affiliation(s)
- SuJin Bak
- Department of Brain and Cognitive Engineering, Korea University, Seoul 02841, Korea;
| | - Jaeyoung Shin
- Department of Electronic Engineering, Wonkwang University, Iksan 54538, Korea;
| | - Jichai Jeong
- Department of Brain and Cognitive Engineering, Korea University, Seoul 02841, Korea;
- Correspondence:
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Tang S, Liu C, Zhang Q, Gu H, Li X, Li Z. Mental workload classification based on ignored auditory probes and spatial covariance. J Neural Eng 2021; 18. [PMID: 34280906 DOI: 10.1088/1741-2552/ac15e5] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2021] [Accepted: 07/19/2021] [Indexed: 11/12/2022]
Abstract
Objective.Estimation of mental workload (MWL) levels by electroencephalography (EEG)-based mental state monitoring systems has been widely explored. Using event-related potentials (ERPs), elicited by ignored auditory probes, minimizes intrusiveness and has shown high performance for estimating MWL level when tested in laboratory situations. However, when facing real-world applications, the characteristics of ERP waveforms, like latency and amplitude, are often affected by noise, which leads to a decrease in classification performance. One approach to mitigating this is using spatial covariance, which is less sensitive to latency and amplitude distortion. In this study, we used ignored auditory probes in a single-stimulus paradigm and tested Riemannian processed covariance-based features for MWL level estimation in a realistic flight-control task.Approach.We recorded EEG data with an eight-channel system from participants while they performed a simulated drone-control task and manipulated MWL levels (high and low) by task difficulty. We compared support vector machine classification performance based on frequency band power features versus features generated via the Riemannian log map operator from spatial covariance matrices. We also compared accuracy of using data segmented as auditory ERPs versus non-ERPs, for which data windows did not overlap with the ERPs.Main results.Classification accuracy of both types of features showed no significant difference between ERPs and non-ERPs. When we ignore auditory stimuli to perform continuous decoding, covariance-based features in the gamma band had area under the receiver-operating-characteristic curve (AUC) of 0.883, which was significantly higher than band power features (AUC = 0.749).Significance.This study demonstrates that Riemannian-processed covariance features are viable for MWL classification under a realistic experimental scenario.
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Affiliation(s)
- Shaohua Tang
- Center for Cognition and Neuroergonomics, State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University at Zhuhai, Zhuhai, People's Republic of China
| | - Chuancai Liu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, People's Republic of China
| | - Qiankun Zhang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, People's Republic of China
| | - Heng Gu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, People's Republic of China
| | - Xiaoli Li
- Center for Cognition and Neuroergonomics, State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University at Zhuhai, Zhuhai, People's Republic of China.,State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, People's Republic of China
| | - Zheng Li
- Center for Cognition and Neuroergonomics, State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University at Zhuhai, Zhuhai, People's Republic of China.,State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, People's Republic of China
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Bagheri M, Power SD. Investigating hierarchical and ensemble classification approaches to mitigate the negative effect of varying stress state on EEG-based detection of mental workload level - and vice versa. BRAIN-COMPUTER INTERFACES 2021. [DOI: 10.1080/2326263x.2021.1948756] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Affiliation(s)
- Mahsa Bagheri
- Faculty of Engineering and Applied Science, Memorial University of Newfoundland, St. John’s, Canada
| | - Sarah D. Power
- Faculty of Engineering and Applied Science, Memorial University of Newfoundland, St. John’s, Canada
- Faculty of Medicine, Memorial University of Newfoundland, St. John’s, Canada
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