Classification of Mental Workload (MWL) using Support Vector Machines (SVM) and Convolutional Neural Networks (CNN)

Asgher, Umer; Khalil, Khurram; Ayaz, Yasar; Ahmad, Robiah; Khan, Muhammad Jawad

doi:10.1109/icomet48670.2020.9073799

Cited by 25 publications

(22 citation statements)

References 18 publications

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“…The rectangles represent the sources, and the circle represents the detector. The distance between the source and detector is a channel, and there are 12 channels in the fNIRS system (Asgher et al, 2020a). Participants A total of 16 subjects (11 males and 5 females) initially participated in this study with age ranging from 20 to 27 years, mean age of 23.5 years, and standard deviation of 5.5 years.…”

Section: Methodsmentioning

confidence: 99%

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Motor Training Using Mental Workload (MWL) With an Assistive Soft Exoskeleton System: A Functional Near-Infrared Spectroscopy (fNIRS) Study for Brain–Machine Interface (BMI)

et al. 2021

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Mental workload is a neuroergonomic human factor, which is widely used in planning a system's safety and areas like brain–machine interface (BMI), neurofeedback, and assistive technologies. Robotic prosthetics methodologies are employed for assisting hemiplegic patients in performing routine activities. Assistive technologies' design and operation are required to have an easy interface with the brain with fewer protocols, in an attempt to optimize mobility and autonomy. The possible answer to these design questions may lie in neuroergonomics coupled with BMI systems. In this study, two human factors are addressed: designing a lightweight wearable robotic exoskeleton hand that is used to assist the potential stroke patients with an integrated portable brain interface using mental workload (MWL) signals acquired with portable functional near-infrared spectroscopy (fNIRS) system. The system may generate command signals for operating a wearable robotic exoskeleton hand using two-state MWL signals. The fNIRS system is used to record optical signals in the form of change in concentration of oxy and deoxygenated hemoglobin (HbO and HbR) from the pre-frontal cortex (PFC) region of the brain. Fifteen participants participated in this study and were given hand-grasping tasks. Two-state MWL signals acquired from the PFC region of the participant's brain are segregated using machine learning classifier—support vector machines (SVM) to utilize in operating a robotic exoskeleton hand. The maximum classification accuracy is 91.31%, using a combination of mean-slope features with an average information transfer rate (ITR) of 1.43. These results show the feasibility of a two-state MWL (fNIRS-based) robotic exoskeleton hand (BMI system) for hemiplegic patients assisting in the physical grasping tasks.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Motor Training Using Mental Workload (MWL) With an Assistive Soft Exoskeleton System: A Functional Near-Infrared Spectroscopy (fNIRS) Study for Brain–Machine Interface (BMI)

et al. 2021

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“…3. A classification scheme was implemented using a support vector machine (SVM) and linear discriminant analysis (LDA) using the Python Scikit-learn 0.23.1 library [23]. We tested every possible combination of six features by selecting combinations that included from one up to six features.…”

Section: Feature Extraction and Classificationmentioning

confidence: 99%

“…Most BCI classifications have utilized machine learning techniques, such as linear discriminant analysis (LDA) [17][18][19], support vector machine (SVM) [20][21][22], deep learning-such as convolutional neural networks [23]-and long short-term memory [24] -and cascade CNN-LSTM [25]. In recent fNIRS-based BCI studies, mental state classification performance varied depending on the type of mental task and classifier, but its accuracy was typically in the 60 to 90% range.…”

Section: Introductionmentioning

confidence: 99%

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Effects of Daily Stress in Mental State Classification

Park

Dong

2020

IEEE Access

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An external stimulus, event, or environment that stresses an individual is called a stressor. Many mental stress detection studies have been focused on the discrimination of the mental state with and without the experimental stressor. However, the mental state in the absence of experimental stressors may not represent accurately the nonstress (baseline) state because people inherently experience considerable stress in their daily lives. Therefore, we assumed that stress detection could be improved more accurately by considering the daily stress. In this study, functional near-infrared spectroscopy (fNIRS) was measured in 41 healthy participants to quantify their prefrontal cortical oxygenation during performing a cognitive task as an experimental stressor, considering individual daily stress level based on the self-report. We then extracted six signal features, including the slope, mean, standard deviation, peak, skewness, and kurtosis of the oxygenated hemoglobin concentration. Using various feature combinations and time windows, we successfully managed to classify daily stress (high/low) and the mental state (task/rest) with support vector machine classifiers. Specifically, individual daily stress level can be easily discriminated with signal features from fNIRS. Moreover, mental state classification performance improved significantly when the daily stress level was handled separately. The findings of this study show the feasibility of the fNIRS-based daily stress classification and can be used in the future to design a robust mental stress management system for the assessment of daily stress in individuals.INDEX TERMS daily stress, functional near-infrared spectroscopy, stroop word color task, mental stress classification.

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Feature Fusion-Based Capsule Network for Cross-Subject Mental Workload Classification

2022

Brain Informatics

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In a complex human-computer interaction system, estimating mental workload based on electroencephalogram (EEG) plays a vital role in the system adaption in accordance with users' mental state. Compared to within-subject classification, cross-subject classification is more challenging due to larger variation across subjects. In this paper, we targeted the cross-subject mental workload classification and attempted to improve the performance. A capsule network capturing structural relationships between features of power spectral density and brain connectivity was proposed. The comparison results showed that it achieved a cross-subject classification accuracy of 45.11%, which was superior to the compared methods (e.g., convolutional neural network and support vector machine). The results also demonstrated feature fusion positively contributed to the cross-subject workload classification. Our study could benefit the future development of a real-time workload detection system unspecific to subjects.

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Classification of Mental Workload (MWL) using Support Vector Machines (SVM) and Convolutional Neural Networks (CNN)

Cited by 25 publications

References 18 publications

Motor Training Using Mental Workload (MWL) With an Assistive Soft Exoskeleton System: A Functional Near-Infrared Spectroscopy (fNIRS) Study for Brain–Machine Interface (BMI)

Motor Training Using Mental Workload (MWL) With an Assistive Soft Exoskeleton System: A Functional Near-Infrared Spectroscopy (fNIRS) Study for Brain–Machine Interface (BMI)

Effects of Daily Stress in Mental State Classification

Feature Fusion-Based Capsule Network for Cross-Subject Mental Workload Classification

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