Mental Workload Estimation from EEG Signals Using Machine Learning Algorithms

Cheema, Baljeet Singh; Samima, Shabnam; Sarma, Monalisa; Samanta, Debasis

doi:10.1007/978-3-319-91122-9_23

Cited by 14 publications

(8 citation statements)

References 16 publications

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“…As Figure 9 demonstrates, the use of SVM resulted in a wide range of performance in tasks including ER, MWL, and SD, when frequency-domain and time-frequency domain feature extraction methods, such as FFT and WT were chosen. RF had competitive performance in tasks that included ER, MWL, MI, SD, and SS, particularly in references [ 59 , 104 , 119 , 185 , 211 ]. However, in the domain associated with ND tasks, the RF algorithm did not perform as well as in other tasks.…”

Section: Resultsmentioning

confidence: 99%

“…Industrial sectors, including transportation, military, and aviation, require individuals to perform multiple tasks simultaneously (i.e., multitasking); operators in these settings must maintain constant vigilance to perform various tasks efficiently and effectively. MWL involves human factors that determine what resources may be required to perform a specific task [ 211 ]. Moreover, prior studies have accepted the role of human mental activities (i.e., human behavior) in human cognitive abilities such as working memory [ 231 , 232 , 233 ].…”

Section: Discussionmentioning

confidence: 99%

“…Our investigation revealed that FFT, PCA, WT, and AR were the most widely used and effective methods for feature extraction among the reviewed articles in this domain; these methods were reported as superior 31%, 19%, 15%, and 15% of the time, respectively. For example, FFT has been implemented by researchers in several published studies [ 81 , 196 , 211 , 239 , 240 , 241 , 242 ] to extract features on the basis of the frequency of the EEG signals; however, another study has confirmed AR as one of the most reliable methods [ 243 ]. We also identified several essential feature extraction techniques that were less frequently applied in MWL tasks, including entropy and HHT, which elicited features of both nonlinear and non-stationary signals.…”

Section: Discussionmentioning

confidence: 99%

“…The results indicated the highest accuracy with KNN, at 99.42% and 90.5%, respectively. In addition to KNN, the application of ANNs [ 81 ], RF [ 211 , 240 ], and XGBoost classifiers [ 32 ] has been reported to identify individual intelligence quotients, mental work estimation, mental state, and task complexity. DL-based models have been applied in MWL tasks and have shown competitive performance in classification.…”

Section: Discussionmentioning

confidence: 99%

“…The best performance (61.08%) was achieved for the LSTM classifier. Given the proposed ML and DL algorithms, the FFT model has been implemented in several studies [ 81 , 211 , 240 , 242 ] to extract frequency domain features from different EEG bands.…”

Section: Discussionmentioning

confidence: 99%

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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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