scholarly journals Simultaneous Classification of Both Mental Workload and Stress Level Suitable for an Online Passive Brain–Computer Interface

Sensors ◽  
2022 ◽  
Vol 22 (2) ◽  
pp. 535
Author(s):  
Mahsa Bagheri ◽  
Sarah D. Power
Keyword(s):  
Transfer Learning ◽  
Stress Level ◽  
Mental Workload ◽  
Real Life ◽  
Mental States ◽  
Subject Classification ◽  
Cognitive States ◽  
Simultaneous Prediction ◽  
And Performance

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.

Relation between capacity and performance in paediatric upper limb prosthesis users

2017 ◽  
Vol 42 (1) ◽  
pp. 14-20 ◽  
Author(s):  
Helen Lindner ◽  
Ayako Hiyoshi ◽  
Liselotte Hermansson
Keyword(s):  
Real Life ◽  
Myoelectric Control ◽  
International Classification Of Functioning ◽  
Upper Limb Prosthesis ◽  
Unit Increase ◽  
And Performance ◽  
The Individual ◽  
Adjusted Model ◽  
The Relationship

Background: The International Classification of functioning, disability and health refers capacity to what an individual can do in a standardised environment and describes performance as what an individual really does and whether the individual encounters any difficulty in the real-life environment. Measures of capacity and performance can help to determine if there is any gap between them that may restrict participation. The aim of this study was to explore the relationship between capacity scores obtained in a standardised clinical setting and proportional ease of performance obtained from a real-life environment. Methods: The Assessment of Capacity for Myoelectric Control and the Prosthetic Upper Extremity Functional Index were used to assess capacity and performance in 62 prosthetic users (age 3–17). Spearman coefficient and generalised linear model were used to examine the association between these measures. Results: A strong correlation (Spearman = 0.75) was found between the capacity scores and the ease of performance. In both unadjusted and adjusted models, capacity was significantly associated with proportional ease of performance. The adjusted model showed that, by 1 unit increase in the Assessment of Capacity for Myoelectric Control score, the ratio of proportional ease of performance increases by 45%. Conclusion: This implies that Assessment of Capacity for Myoelectric Control can be a predictor for ease of performance in real-life environment. Clinical relevance The ACMC scores may serve as an indicator to predict the difficulties that the children may encounter in their home environment. This prediction can help the clinician to make decisions, such that if the child requires more control training or is ready to move on to learn more complex tasks.

scholarly journals Modeling a deep transfer learning framework for the classification of COVID-19 radiology dataset

2021 ◽  
Vol 7 ◽  
pp. e614
Author(s):  
Michael Adebisi Fayemiwo ◽  
Toluwase Ayobami Olowookere ◽  
Samson Afolabi Arekete ◽  
Adewale Opeoluwa Ogunde ◽  
Mba Obasi Odim ◽  
...  
Keyword(s):  
Transfer Learning ◽  
Binary Classification ◽  
Real Life ◽  
Batch Size ◽  
Test Accuracy ◽  
X Ray ◽  
Image Dataset ◽  
Kappa Value ◽  
Positive Correlations

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-Coronavirus-2 or SARS-CoV-2), which came into existence in 2019, is a viral pandemic that caused coronavirus disease 2019 (COVID-19) illnesses and death. Research showed that relentless efforts had been made to improve key performance indicators for detection, isolation, and early treatment. This paper used Deep Transfer Learning Model (DTL) for the classification of a real-life COVID-19 dataset of chest X-ray images in both binary (COVID-19 or Normal) and three-class (COVID-19, Viral-Pneumonia or Normal) classification scenarios. Four experiments were performed where fine-tuned VGG-16 and VGG-19 Convolutional Neural Networks (CNNs) with DTL were trained on both binary and three-class datasets that contain X-ray images. The system was trained with an X-ray image dataset for the detection of COVID-19. The fine-tuned VGG-16 and VGG-19 DTL were modelled by employing a batch size of 10 in 40 epochs, Adam optimizer for weight updates, and categorical cross-entropy loss function. The results showed that the fine-tuned VGG-16 and VGG-19 models produced an accuracy of 99.23% and 98.00%, respectively, in the binary task. In contrast, in the multiclass (three-class) task, the fine-tuned VGG-16 and VGG-19 DTL models produced an accuracy of 93.85% and 92.92%, respectively. Moreover, the fine-tuned VGG-16 and VGG-19 models have MCC of 0.98 and 0.96 respectively in the binary classification, and 0.91 and 0.89 for multiclass classification. These results showed strong positive correlations between the models’ predictions and the true labels. In the two classification tasks (binary and three-class), it was observed that the fine-tuned VGG-16 DTL model had stronger positive correlations in the MCC metric than the fine-tuned VGG-19 DTL model. The VGG-16 DTL model has a Kappa value of 0.98 as against 0.96 for the VGG-19 DTL model in the binary classification task, while in the three-class classification problem, the VGG-16 DTL model has a Kappa value of 0.91 as against 0.89 for the VGG-19 DTL model. This result is in agreement with the trend observed in the MCC metric. Hence, it was discovered that the VGG-16 based DTL model classified COVID-19 better than the VGG-19 based DTL model. Using the best performing fine-tuned VGG-16 DTL model, tests were carried out on 470 unlabeled image dataset, which was not used in the model training and validation processes. The test accuracy obtained for the model was 98%. The proposed models provided accurate diagnostics for both the binary and multiclass classifications, outperforming other existing models in the literature in terms of accuracy, as shown in this work.

Human Factor Study for Maritime Simulator-Based Assessment of Cadets

2016 ◽  
Author(s):  
Yisi Liu ◽  
Xiyuan Hou ◽  
Olga Sourina ◽  
Dimitrios Konovessis ◽  
Gopala Krishnan
Keyword(s):  
Human Factors ◽  
Real Time ◽  
Stress Level ◽  
Mental Workload ◽  
Mental States ◽  
Traffic Load ◽  
Occupational Accidents ◽  
Brain State ◽  
Maritime Industry ◽  
Near Misses

Maritime accident statistics show that the majority of accidents/incidents are attributed to human errors as the initiating cause. Some studies put this as high as 95% of all accidents (collision, grounding, fire, occupational accidents, etc). The traditional way to investigate human factors in maritime industry is the statistical analysis of accident data. Although this analysis can provide key findings, it cannot capture the causal relationship between performance shaping factors and human performance in the everyday routine work, and is not suitable to be used in the individual assessment of cadets. To reveal the effects of human factors in maritime and assess the performance of cadets, a full-mission simulator is widely used. Different scenarios such as bad weather, day and night environment, different traffic load, etc. can be simulated. The fine details of the cadet performance can be recorded in the simulator during the assessment. As a result, other than performance failure, the near misses can also be detected. Additionally, a number of cadets can go through the same scenarios at the same time and between-subjects comparison is enabled. Besides the operations recording provided by the simulator, biosignal-based tools can additionally help in the human factors study in maritime. The existing methods include palmer perspiration, electrocardiography, etc. However, the psychophysiological states that can be recognized by these methods are limited. Electroencephalogram (EEG) biosignals can be used to directly assess the “inner” mental states of subjects. Nowadays, since the EEG devices become portable, easy to setup, and affordable in price, EEG-based tools can be used to assess psychophysiological state of subjects. Using the sensors during performing the task we can recognize the cadet/captain’s emotions, attentiveness/concentration, mental workload, and stress level in real time. In this work, we propose a real-time brain state recognition system using EEG biosignals to monitor mental workload and stress of cadets during simulator-based assessment. Currently, the proposed and implemented system includes stress and mental workload recognition algorithms. The EEG-based mental state monitoring can reflect the true “inner” feelings, stress level and workload of the cadets during the simulator-aided assessment. The time resolution is up to 0.03 second. As a result, we can analyze the recognized brain states and the corresponding performance and behavior recorded by the simulator to study how human factors affect the subject’s performance. For example, we can check is there any correlation of the cadet’s stress level and performance results. Finally, the proposed EEG-based system allows us to assess whether a cadet is ready to perform tasks on the bridge or needs more training in the simulator even if he/she navigated with few errors during the assessment.

scholarly journals A Systematic Review for Cognitive State-Based QoE/UX Evaluation

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3439
Author(s):  
Edgar Bañuelos-Lozoya ◽  
Gabriel González-Serna ◽  
Nimrod González-Franco ◽  
Olivia Fragoso-Diaz ◽  
Noé Castro-Sánchez
Keyword(s):  
Systematic Review ◽  
User Experience ◽  
Mental Workload ◽  
Cognitive State ◽  
Sources Of Information ◽  
Cognitive States ◽  
Starting Point ◽  
The Relationship

Traditional evaluation of user experience is subjective by nature, for what is sought is to use data from physiological and behavioral sensors to interpret the relationship that the user’s cognitive states have with the elements of a graphical interface and interaction mechanisms. This study presents the systematic review that was developed to determine the cognitive states that are being investigated in the context of Quality of Experience (QoE)/User Experience (UX) evaluation, as well as the signals and characteristics obtained, machine learning models used, evaluation architectures proposed, and the results achieved. Twenty-nine papers published in 2014–2019 were selected from eight online sources of information, of which 24% were related to the classification of cognitive states, 17% described evaluation architectures, and 41% presented correlations between different signals, cognitive states, and QoE/UX metrics, among others. The amount of identified studies was low in comparison with cognitive state research in other contexts, such as driving or other critical activities; however, this provides a starting point to analyze and interpret states such as mental workload, confusion, and mental stress from various human signals and propose more robust QoE/UX evaluation architectures.

Animal Recognition and Eye Movements

2010 ◽  
Vol 57 (2) ◽  
pp. 117-125 ◽  
Author(s):  
Toby J. Lloyd-Jones ◽  
Juergen Gehrke ◽  
Jason Lauder
Keyword(s):  
Eye Movements ◽  
Response Times ◽  
Real Life ◽  
Object Orientation ◽  
Decision Task ◽  
Line Drawings ◽  
Object Decision ◽  
Individual Features ◽  
And Performance

We assessed the importance of outline contour and individual features in mediating the recognition of animals by examining response times and eye movements in an animal-object decision task (i.e., deciding whether or not an object was an animal that may be encountered in real life). There were shorter latencies for animals as compared with nonanimals and performance was similar for shaded line drawings and silhouettes, suggesting that important information for recognition lies in the outline contour. The most salient information in the outline contour was around the head, followed by the lower torso and leg regions. We also observed effects of object orientation and argue that the usefulness of the head and lower torso/leg regions is consistent with a role for the object axis in recognition.

Breast Cancer Detection and Classification using Traditional Computer Vision Techniques: A Comprehensive Review

2020 ◽  
Vol 16 ◽  
Author(s):  
Saliha Zahoor ◽  
Ikram Ullah Lali ◽  
Muhammad Attique Khan ◽  
Kashif Javed ◽  
Waqar Mehmood
Keyword(s):  
Breast Cancer ◽  
Computer Aided Diagnosis ◽  
Diagnose Breast Cancer ◽  
Initial Stage ◽  
Computer Aided ◽  
Diagnosis And Classification ◽  
And Performance ◽  
Tools And Techniques ◽  
Aided Diagnosis

: Breast Cancer is a common dangerous disease for women. In the world, many women died due to Breast cancer. However, in the initial stage, the diagnosis of breast cancer can save women's life. To diagnose cancer in the breast tissues there are several techniques and methods. The image processing, machine learning and deep learning methods and techniques are presented in this paper to diagnose the breast cancer. This work will be helpful to adopt better choices and reliable methods to diagnose breast cancer in an initial stage to survive the women's life. To detect the breast masses, microcalcifications, malignant cells the different techniques are used in the Computer-Aided Diagnosis (CAD) systems phases like preprocessing, segmentation, feature extraction, and classification. We have been reported a detailed analysis of different techniques or methods with their usage and performance measurement. From the reported results, it is concluded that for the survival of women’s life it is essential to improve the methods or techniques to diagnose breast cancer at an initial stage by improving the results of the Computer-Aided Diagnosis systems. Furthermore, segmentation and classification phases are challenging for researchers for the diagnosis of breast cancer accurately. Therefore, more advanced tools and techniques are still essential for the accurate diagnosis and classification of breast cancer.

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