Research on Channel Selection and Multi-Feature Fusion of EEG Signals for Mental Fatigue Detection

Liu, Quan; Yang, Liu; Chen, Kun; Wang, Lei; Li, Zhilei; Ai, Qingsong; Ma, Li

doi:10.3390/e23040457

Cited by 22 publications

(8 citation statements)

References 23 publications

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Section: Preprocessingmentioning

confidence: 94%

“…Fourth, since one of the objectives of this study is to use minimal EEG signal channels, it is necessary to identify the active electrodes to reduce the computational complexity. In accordance with [24,25,58,59], 12 electrodes, out of the 30 electrodes used for signal recording, were identified in the form of six active regions, on the basis of the electrode weights, for this purpose. Accordingly, only data from the 12 selected channels were used for the compression and data processing, and the rest of the channels were excluded from the processing.…”

Section: Preprocessingmentioning

confidence: 99%

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Developing a Deep Neural Network for Driver Fatigue Detection Using EEG Signals Based on Compressed Sensing

et al. 2022

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In recent years, driver fatigue has become one of the main causes of road accidents. As a result, fatigue detection systems have been developed to warn drivers, and, among the available methods, EEG signal analysis is recognized as the most reliable method for detecting driver fatigue. This study presents an automated system for a two-stage classification of driver fatigue, using a combination of compressed sensing (CS) theory and deep neural networks (DNNs), that is based on EEG signals. First, CS theory is used to compress the recorded EEG data in order to reduce the computational load. Then, the compressed EEG data is fed into the proposed deep convolutional neural network for automatic feature extraction/selection and classification purposes. The proposed network architecture includes seven convolutional layers together with three long short-term memory (LSTM) layers. For compression rates of 40, 50, 60, 70, 80, and 90, the simulation results for a single-channel recording show accuracies of 95, 94.8, 94.6, 94.4, 94.4, and 92%, respectively. Furthermore, by comparing the results to previous methods, the accuracy of the proposed method for the two-stage classification of driver fatigue has been improved and can be used to effectively detect driver fatigue.

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Section: Preprocessingmentioning

confidence: 94%

Section: Preprocessingmentioning

confidence: 99%

Developing a Deep Neural Network for Driver Fatigue Detection Using EEG Signals Based on Compressed Sensing

et al. 2022

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“…In a similar manner, we asked participants to complete a second CFS scale in 5 min to confirm their level of fatigue. Our previous studies [20] have demonstrated that the EEGs collected under both conditions represent both states of alertness and fatigue, as prior to the 2-back experiment, subjects' CFS scores were all less than 4, whereas after approximately 75 min of testing, subjects' CFS scores were all greater than 4 and a oneway analysis of variance (ANOVA) of the two score profiles yielded p = 5.42 × 10 −10 , implying that the scores for these two states were completely different. This is consistent with the experimentally recorded performance of the subjects on the 2-Back task: subjects gradually became fatigued as they completed the 2-Back task, taking longer to respond to the stimuli in order to resist the effects of fatigue, and the accuracy of task completion decreased as the experiment progressed.…”

Section: Self-designed Datasetmentioning

confidence: 97%

“…EEG data is commonly broken down into several rhythmic signals according to different frequency bands and these rhythmic signals alterations are analyzed when mental fatigue builds up. EEG signals typically contains the delta (1-4 Hz), theta (4-8 Hz), alpha (8)(9)(10)(11)(12)(13)(14), beta (14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30), and gamma (30-50 Hz) bands, each of which has a functional significance and is a reflection of various cognitive behavioral and physiological activities of the human brain [2]. Many studies show that the EEG is sensitive to fluctuations in mental alertness and that the persistence of mental fatigue is highly correlated with changes in EEG rhythms [3].…”

Section: Introductionmentioning

confidence: 99%

EEG-based mental fatigue detection using linear prediction cepstral coefficients and Riemann spatial covariance matrix

Chen¹,

Liu²,

Liu³

et al. 2022

J. Neural Eng.

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Establishing a mental fatigue monitoring system is of great importance as for severe fatigue may cause unimaginable consequences. Electroencephalogram (EEG) is often utilized for mental fatigue detection because of its high temporal resolution and ease of use. However, many EEG-based approaches for detecting mental fatigue only take into account the feature extraction of a single domain and do not fully exploit the information that EEG may offer. In our work, we propose a new algorithm for mental fatigue detection based on multi-domain feature extraction and fusion. EEG components representing fatigue are closely related in the past and present because fatigue is a dynamic and gradual process. Accordingly, the idea of linear prediction is used to fit the current value with a set of sample values in the past to calculate the linear prediction cepstral coefficients (LPCCs) as the time domain feature. Moreover, in order to better capture fatigue-related spatial domain information, the spatial covariance matrix of the original EEG signal is projected into the Riemannian tangent space using the Riemannian geometric method. Then multi-domain features are fused to obtain comprehensive spatio-temporal information. Experimental results prove the suggested algorithm outperforms existing state-of-the-art methods, achieving an average accuracy of 87.10% classification on the public dataset SEED-VIG (three categories) and 97.40% classification accuracy (two categories) on the dataset made by self-designed experiments. These findings show that our proposed strategy perform more effectively for mental fatigue detection based on EEG.

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“…According to other papers as [ 33 ] that presents a new method for EEG channel selection optimisation based on relief function [ 34 ], the proposed approach aims to realise a data-driven procedure, considering every feature highly affected by channel selection and avoiding calculating the summation of features weight of each channel as in [ 33 ], in order to increase the accuracy performance.…”

Section: Introductionmentioning

confidence: 99%

Comparing between Different Sets of Preprocessing, Classifiers, and Channels Selection Techniques to Optimise Motor Imagery Pattern Classification System from EEG Pattern Recognition

et al. 2021

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The ability to control external devices through thought is increasingly becoming a reality. Human beings can use the electrical signals of their brain to interact or change the surrounding environment and more. The development of this technology called brain-computer interface (BCI) will increasingly allow people with motor disabilities to communicate or use assistive devices to walk, manipulate objects and communicate. Using data from the PhysioNet database, this study implemented a pattern classification system for use in a BCI on 109 healthy volunteers during real movement activities and motor imagery recorded by 64-channels electroencephalography (EEG) system. Different classifiers such as Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Decision Trees (TREE) were applied on different combinations of EEG channels. Starting from two channels (C3, C4 and CP3 and CP4) positioned on the contralateral and ipsilateral sensorimotor cortex, the Region of Interest (RoI) centred on C3/Cp3 and C4/Cp4 and, finally, a data-driven automatic channels selection was tested to explore the best channel combination able to increase the classification accuracy. The results showed that the proposed automatic channels selection was able to significantly improve the performance of each classifier achieving 98% of accuracy for classification of real and imagined hand movement (sensitivity = 97%, specificity = 99%, AUC = 0.99) by SVM. While the accuracy of the classification between the imagery of hand and foot movements was 91% (sensitivity = 87%, specificity = 86%, AUC = 0.93) also with SVM. In the proposed approach, the data-driven automatic channels selection outperforms classical a priori channel selection models such as C3/C4, Cp3/Cp4, or RoIs around those channels with the utmost accuracy to help remove the boundaries of human communication and improve the quality of life of people with disabilities.

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Research on Channel Selection and Multi-Feature Fusion of EEG Signals for Mental Fatigue Detection

Cited by 22 publications

References 23 publications

Developing a Deep Neural Network for Driver Fatigue Detection Using EEG Signals Based on Compressed Sensing

Developing a Deep Neural Network for Driver Fatigue Detection Using EEG Signals Based on Compressed Sensing

EEG-based mental fatigue detection using linear prediction cepstral coefficients and Riemann spatial covariance matrix

Comparing between Different Sets of Preprocessing, Classifiers, and Channels Selection Techniques to Optimise Motor Imagery Pattern Classification System from EEG Pattern Recognition

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