Brain-computer interface for workload estimation: Assessment of mental efforts in learning processes

Zammouri, Amin; Moussa, Abdelaziz Ait; Mebrouk, Yassine

doi:10.1016/j.eswa.2018.06.027

Cited by 40 publications

(25 citation statements)

References 29 publications

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“…This measure allowed to decrease the number of features down to 570, which was still high. In order to further decrease the feature number, the next step was taken: The power of the frequency components were averaged within the following intervals: Delta(1-3 Hz), theta (4-8 Hz), alpha (8-13 Hz), beta1 (13)(14)(15)(16)(17)(18)(19)(20), and beta2 (20-30 Hz) for 19 electrodes, which led to the total feature number equaling 95.…”

Section: Data Processingmentioning

confidence: 99%

“…It was determined as a θ(Fz)/α(Pz) ratio, where Fz and Pz are electrodes. In [19] the mental state of individuals was analyzed on the basis of changes in EEG power spectral density, especially in the theta and alpha bands, where the average classification accuracy reached, respectively, 79% and 78%. The classifier applied was based on SVM.…”

Section: Introductionmentioning

confidence: 99%

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A Three-Class Classification of Cognitive Workload Based on EEG Spectral Data

et al. 2019

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Evaluation of cognitive workload finds its application in many areas, from educational program assessment through professional driver health examination to monitoring the mental state of people carrying out jobs of high responsibility, such as pilots or airline traffic dispatchers. Estimation of multilevel cognitive workload is a task usually realized in a subject-dependent way, while the present research is focused on developing the procedure of subject-independent evaluation of cognitive workload level. The aim of the paper is to estimate cognitive workload level in accordance with subject-independent approach, applying classical machine learning methods combined with feature selection techniques. The procedure of data acquisition was based on registering the EEG signal of the person performing arithmetical tasks divided into six intervals of advancement. The analysis included the stages of preprocessing, feature extraction, and selection, while the final step covered multiclass classification performed with several models. The results discussed show high maximal accuracies achieved: ~91% for both the validation dataset and for the cross-validation approach for kNN model.

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Section: Data Processingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Three-Class Classification of Cognitive Workload Based on EEG Spectral Data

et al. 2019

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“…Therefore, human mental workload -often referred to as cognitive load -can be intuitively defined as the amount of mental work necessary for a person to complete a task over a given period of time [3,4]. However, nowadays human mental workload is more generally defined as the measurement of the amount of mental resources involved in a cognitive task [5].…”

Section: Introductionmentioning

confidence: 99%

Developing an Objective Indicator of Fatigue: An Alternative Mobile Version of the Psychomotor Vigilance Task (m-PVT)

Evans

Harborne

Smith

2019

Communications in Computer and Information Science

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Approximately 20% of the working population report symptoms of feeling fatigued at work. The aim of the study was to investigate whether an alternative mobile version of the 'gold standard' Psychomotor Vigilance Task (PVT) could be used to provide an objective indicator of fatigue in staff working in applied safety critical settings such as train driving, hospital staffs, emergency services, law enforcements, etc., using different mobile devices. 26 participants mean age 20 years completed a 25-minute reaction time study using an alternative mobile version of the Psychomotor Vigilance Task (m-PVT) that was implemented on either an Apple iPhone 6s Plus or a Samsung Galaxy Tab 4. Participants attended two sessions: a morning and an afternoon session held on two consecutive days counterbalanced. It was found that the iPhone 6s Plus generated both mean speed responses (1/RTs) and mean reaction times (RTs) that were comparable to those observed in the literature while the Galaxy Tab 4 generated significantly lower 1/RTs and slower RTs than those found with the iPhone 6s Plus. Furthermore, it was also found that the iPhone 6s Plus was sensitive enough to detect lower mean speed of responses (1/RTs) and significantly slower mean reaction times (RTs) after 10-minutes on the m-PVT. In contrast, it was also found that the Galaxy Tab 4 generated mean number of lapses that were significant after 5-minutes on the m-PVT. These findings seem to indicate that the m-PVT could be used to provide an objective indicator of fatigue in staff working in applied safety critical settings such as train driving, hospital staffs, emergency services, law enforcements, etc.

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“…Zammouri et al designed an EEG workload classifier combining power spectral density (PSD) analysis and statistical criteria. Based on this, a brain–computer interface was developed for real-time workload evaluation in cognitive learning [19]. McDonald et al proposed machine learning approaches for detecting driver distraction, with a training set of 21 algorithms [20].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Neuro-Fuzzy Fusion of Multi-Sensor Data for Monitoring a Pilot’s Workload Condition

Zhang

Sun

Qiu

et al. 2019

Sensors

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To realize an early warning of unbalanced workload in the aircraft cockpit, it is required to monitor the pilot’s real-time workload condition. For the purpose of building the mapping relationship from physiological and flight data to workload, a multi-source data fusion model is proposed based on a fuzzy neural network, mainly structured using a principal components extraction layer, fuzzification layer, fuzzy rules matching layer, and normalization layer. Aiming at the high coupling characteristic variables contributing to workload, principal component analysis reconstructs the feature data by reducing its dimension. Considering the uncertainty for a single variable to reflect overall workload, a fuzzy membership function and fuzzy control rules are defined to abstract the inference process. An error feedforward algorithm based on gradient descent is utilized for parameter learning. Convergence speed and accuracy can be adjusted by controlling the gradient descent rate and error tolerance threshold. Combined with takeoff and initial climbing tasks of a Boeing 737–800 aircraft, crucial performance indicators—including pitch angle, heading, and airspeed—as well as physiological indicators—including electrocardiogram (ECG), respiration, and eye movements—were featured. The mapping relationship between multi-source data and the comprehensive workload level synthesized using the NASA task load index was established. Experimental results revealed that the predicted workload corresponding to different flight phases and difficulty levels showed clear distinctions, thereby proving the validity of data fusion.

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Brain-computer interface for workload estimation: Assessment of mental efforts in learning processes

Cited by 40 publications

References 29 publications

A Three-Class Classification of Cognitive Workload Based on EEG Spectral Data

A Three-Class Classification of Cognitive Workload Based on EEG Spectral Data

Developing an Objective Indicator of Fatigue: An Alternative Mobile Version of the Psychomotor Vigilance Task (m-PVT)

Adaptive Neuro-Fuzzy Fusion of Multi-Sensor Data for Monitoring a Pilot’s Workload Condition

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