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

Plechawska–Wójcik, Małgorzata; Tokovarov, Mikhail; Kaczorowska, Monika; Zapała, Dariusz

doi:10.3390/app9245340

Cited by 50 publications

(24 citation statements)

References 60 publications

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“…In the previous studies, for example in [1 ], CA of 80–90% was achieved for the highest to lowest workload while using spectral EEG power and ERP. In [4 ], CA of 91% was achieved using the k‐nearest neighbour classifier while estimating cognitive overload. A recent study conducting prediction of human cognition ability reports an R 2 value of 0.72 [5 ].…”

Section: Resultsmentioning

confidence: 99%

“…Influence on task performance through frontal and parietal lobe signals was studied with memory load tasks. Machine learning‐based cognitive workload level estimating method was reported in [4 ]. In this work, the arithmetic task was used as a cognitive workload.…”

Section: Introductionmentioning

confidence: 99%

“…Another recent study reported improved cognitive workload assessment technique using L Jaya optimisation‐based channel selection in EEG [6 ]. These studies [1–5 ] utilised EEG‐based brain–computer interface (BCI) systems to assess or classify workload in participants. However, the evaluation of cognitive tasks based on a single modality has demonstrated limited performance in BCI applications.…”

Section: Introductionmentioning

confidence: 99%

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Classification of working memory loads using hybrid EEG and fNIRS in machine learning paradigm

2020

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Single modality brain–computer interface (BCI) systems often mislabel the electroencephalography (EEG) signs as a command, even though the participant is not executing some task. In this Letter, the classification of different working memory load levels is presented using a hybrid BCI system. N‐back cognitive tasks such as 0‐back, 2‐back, and 3‐back are used to create working memory load on participants while recording EEG and functional near‐infrared spectroscopy (fNIRS) signals simultaneously. A combination of statistically significant features obtained from EEG and fNIRS corresponding to frontal region channels are used to classify different N‐back commands. Kernel‐based support vector machine (SVM) classifiers are employed with and without cross‐validation schemes. Classification accuracy of 100% is achieved for binary classification of 0‐back against 2‐back and 0‐back against 3‐back using linear SVM, quadratic SVM, and cubic SVM under holdout data division protocol.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Classification of working memory loads using hybrid EEG and fNIRS in machine learning paradigm

2020

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“…Recently, brain activity measurement has been verified as an efficient method in the imaging of emotional stress changes [10]. NIRS and fMRI measure brain activity using blood in the brain.…”

Section: Introductionmentioning

confidence: 99%

An Effective Mental Stress State Detection and Evaluation System Using Minimum Number of Frontal Brain Electrodes

Attallah

2020

Diagnostics

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Currently, mental stress is a common social problem affecting people. Stress reduces human functionality during routine work and may lead to severe health defects. Detecting stress is important in education and industry to determine the efficiency of teaching, to improve education, and to reduce risks from human errors that might occur due to workers’ stressful situations. Therefore, the early detection of mental stress using machine learning (ML) techniques is essential to prevent illness and health problems, improve quality of education, and improve industrial safety. The human brain is the main target of mental stress. For this reason, an ML system is proposed which investigates electroencephalogram (EEG) signal for thirty-six participants. Extracting useful features is essential for an efficient mental stress detection (MSD) system. Thus, this framework introduces a hybrid feature-set that feeds five ML classifiers to detect stress and non-stress states, and classify stress levels. To produce a reliable, practical, and efficient MSD system with a reduced number of electrodes, the proposed MSD scheme investigates the electrodes placements on different sites on the scalp and selects that site which has the higher impact on the accuracy of the system. Principal Component analysis is employed also, to reduce the features extracted from such electrodes to lower model complexity, where the optimal number of principal components is examined using sequential forward procedure. Furthermore, it examines the minimum number of electrodes placed on the site which has greater impact on stress detection and evaluation. To test the effectiveness of the proposed system, the results are compared with other feature extraction methods shown in literature. They are also compared with state-of-the-art techniques recorded for stress detection. The highest accuracies achieved in this study are 99.9%(sd = 0.015) and 99.26% (sd = 0.08) for identifying stress and non-stress states, and distinguishing between stress levels, respectively, using only two frontal brain electrodes for detecting stress and non-stress, and three frontal electrodes for evaluating stress levels respectively. The results show that the proposed system is reliable as the sensitivity is 99.9(0.064), 98.35(0.27), specificity is 99.94(0.02), 99.6(0.05), precision is 99.94(0.06), 98.9(0.23), and the diagnostics odd ratio (DOR) is ≥ 100 for detecting stress and non-stress, and evaluating stress levels respectively. This shows that the proposed framework has compelling performance and can be employed for stress detection and evaluation in medical, educational and industrial fields. Finally, the results verified the efficiency and reliability of the proposed system in predicting stress and non-stress on new patients, as the accuracy achieved 98.48% (sd = 1.12), sensitivity = 97.78% (sd = 1.84), specificity = 97.75% (sd = 2.05), precision = 99.26% (sd = 0.67), and DOR ≥ 100 using only two frontal electrodes.

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“…Many machine learning methods have been applied to cognitive load detection, as shown in Table 1 [ 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 ]. The main idea of these methods is as follows.…”

Section: Introductionmentioning

confidence: 99%

Pattern Recognition of Cognitive Load Using EEG and ECG Signals

Xiong

Kong

Yang

et al. 2020

Sensors

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The matching of cognitive load and working memory is the key for effective learning, and cognitive effort in the learning process has nervous responses which can be quantified in various physiological parameters. Therefore, it is meaningful to explore automatic cognitive load pattern recognition by using physiological measures. Firstly, this work extracted 33 commonly used physiological features to quantify autonomic and central nervous activities. Secondly, we selected a critical feature subset for cognitive load recognition by sequential backward selection and particle swarm optimization algorithms. Finally, pattern recognition models of cognitive load conditions were constructed by a performance comparison of several classifiers. We grouped the samples in an open dataset to form two binary classification problems: (1) cognitive load state vs. baseline state; (2) cognitive load mismatching state vs. cognitive load matching state. The decision tree classifier obtained 96.3% accuracy for the cognitive load vs. baseline classification, and the support vector machine obtained 97.2% accuracy for the cognitive load mismatching vs. cognitive load matching classification. The cognitive load and baseline states are distinguishable in the level of active state of mind and three activity features of the autonomic nervous system. The cognitive load mismatching and matching states are distinguishable in the level of active state of mind and two activity features of the autonomic nervous system.

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

Cited by 50 publications

References 60 publications

Classification of working memory loads using hybrid EEG and fNIRS in machine learning paradigm

Classification of working memory loads using hybrid EEG and fNIRS in machine learning paradigm

An Effective Mental Stress State Detection and Evaluation System Using Minimum Number of Frontal Brain Electrodes

Pattern Recognition of Cognitive Load Using EEG and ECG Signals

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