Mental Workload Estimation Using EEG

Pandey, Vishal; Choudhary, Dhirendra Kumar; Verma, V. K.; Sharma, Greeshma; Singh, Ram Sewak; Chandra, Sushil

doi:10.1109/icrcicn50933.2020.9296150

Cited by 13 publications

(7 citation statements)

References 12 publications

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“…For example, Jiao et al [ 242 ] have designed a novel CNN architecture with an average result accuracy of 90% in 15 participants. In reference [ 247 ], the MWL has been classified by using the KNN classifier, the LSTM classifier, and the CNN + LSTM network. The best performance (61.08%) was achieved for the LSTM classifier.…”

Section: Discussionmentioning

confidence: 99%

Neural Decoding of EEG Signals with Machine Learning: A Systematic Review

et al. 2021

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Electroencephalography (EEG) is a non-invasive technique used to record the brain’s evoked and induced electrical activity from the scalp. Artificial intelligence, particularly machine learning (ML) and deep learning (DL) algorithms, are increasingly being applied to EEG data for pattern analysis, group membership classification, and brain-computer interface purposes. This study aimed to systematically review recent advances in ML and DL supervised models for decoding and classifying EEG signals. Moreover, this article provides a comprehensive review of the state-of-the-art techniques used for EEG signal preprocessing and feature extraction. To this end, several academic databases were searched to explore relevant studies from the year 2000 to the present. Our results showed that the application of ML and DL in both mental workload and motor imagery tasks has received substantial attention in recent years. A total of 75% of DL studies applied convolutional neural networks with various learning algorithms, and 36% of ML studies achieved competitive accuracy by using a support vector machine algorithm. Wavelet transform was found to be the most common feature extraction method used for all types of tasks. We further examined the specific feature extraction methods and end classifier recommendations discovered in this systematic review.

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Section: Discussionmentioning

confidence: 99%

Neural Decoding of EEG Signals with Machine Learning: A Systematic Review

et al. 2021

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“…Therefore, as shown in Table 3, almost all of the previous methods do not apply to estimating the MWL of workers. In the dual-task estimation, the method proposed by Pandey et al (2020) was only marginally more accurate than random. In contrast, our method was able to achieve 82.78%, which is sufficient for use in realistic scenarios.…”

Section: Estimation Performancementioning

confidence: 96%

“…It is worth noting that no studies were conducted with either dualtask estimation (No Task vs. Task) or triple task estimation (Lo vs. Mi vs. Hi) subject-independent experiments simultaneously until now (Lim et al, 2018;Pandey et al, 2020).…”

Section: Subject-independent Experimentsmentioning

confidence: 99%

“…Table 3 summarized the comparative results regarding the average estimation accuracy under the subject-independent experiments setting. Pandey et al (2020) realized that handcrafted features would slow down the speed of computing and significantly increased the evaluation time, making it challenging to apply them in practical scenarios. Therefore, he used an end-to-end structure similar to ours to finish the dual-task estimation(No Task vs.…”

Section: Subject-independent Experimentsmentioning

confidence: 99%

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EEG-TNet: An End-To-End Brain Computer Interface Framework for Mental Workload Estimation

Fan

Huang

et al. 2022

Front. Neurosci.

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The mental workload (MWL) of different occupational groups' workers is the main and direct factor of unsafe behavior, which may cause serious accidents. One of the new and useful technologies to estimate MWL is the Brain computer interface (BCI) based on EEG signals, which is regarded as the gold standard of cognitive status. However, estimation systems involving handcrafted EEG features are time-consuming and unsuitable to apply in real-time. The purpose of this study was to propose an end-to-end BCI framework for MWL estimation. First, a new automated data preprocessing method was proposed to remove the artifact without human interference. Then a new neural network structure named EEG-TNet was designed to extract both the temporal and frequency information from the original EEG. Furthermore, two types of experiments and ablation studies were performed to prove the effectiveness of this model. In the subject-dependent experiment, the estimation accuracy of dual-task estimation (No task vs. TASK) and triple-task estimation (Lo vs. Mi vs. Hi) reached 99.82 and 99.21%, respectively. In contrast, the accuracy of different tasks reached 82.78 and 66.83% in subject-independent experiments. Additionally, the ablation studies proved that preprocessing method and network structure had significant contributions to estimation MWL. The proposed method is convenient without any human intervention and outperforms other related studies, which becomes an effective way to reduce human factor risks.

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“…Mental workload recognition from EEG signal using deep learning techniques was conducted in [16] and it achieved only 65% classification accuracy. Mental workload estimation using EEG was also introduced by Vishal Pandey et al in 2020 in an international conference with maximum 72% classification accuracy [17]. Cognitive Workload assessment on simultaneous tasks was proposed by Wonse Jo et al in 2022 with 74.68% [18].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Attention and Short-Term Memory Loss by EEG Workload Estimation

Islam¹,

Sarkar²,

Hossain³

et al. 2023

JBM

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Mental workload plays a vital role in cognitive impairment. The impairment refers to a person's difficulty in remembering, receiving new information, learning new things, concentrating, or making decisions that seriously affect everyday life. In this paper, the simultaneous capacity (SIMKAP) experimentbased EEG workload analysis was presented using 45 subjects for multitasking mental workload estimation with subject wise attention loss calculation as well as short term memory loss measurement. Using an open access preprocessed EEG dataset, Discrete wavelet transforms (DWT) was utilized for feature extraction and Minimum redundancy and maximum relevancy (MRMR) technique was used to select most relevance features. Wavelet decomposition technique was also used for decomposing EEG signals into five sub bands. Fourteen statistical features were calculated from each sub band signal to form a 5 × 14 window size. The Neural Network (Narrow) classification algorithm was used to classify dataset for low and high workload conditions and comparison was made using some other machine learning models. The results show the classifier's accuracy of 86.7%, precision of 84.4%, F1 score of 86.33%, and recall of 88.37% that crosses the state-of-the art methodologies in the literature. This prediction is expected to greatly facilitate the improved way in memory and attention loss impairments assessment.

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Mental Workload Estimation Using EEG

Cited by 13 publications

References 12 publications

Neural Decoding of EEG Signals with Machine Learning: A Systematic Review

Neural Decoding of EEG Signals with Machine Learning: A Systematic Review

EEG-TNet: An End-To-End Brain Computer Interface Framework for Mental Workload Estimation

Prediction of Attention and Short-Term Memory Loss by EEG Workload Estimation

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