ABSTRACT
Mental fatigue is closely related to our daily life and work, a considerable number of studies have achieved good results in quantifying and predicting them. Although some studies have achieved a high accuracy by using only a single channel, and a few have explored the optimal solution for feature and channel selection. However, detailed research of optimally setting the electrodes position and determining the number channels are rarely seen. In this study, by designing a novel genetic operator and applying the GA-SVM model, we compared the maximum number of optimal channels and their distributions. The result suggests that the classification accuracy almost reaches its optimum (94.0±5.3 %) when the maximum number of channels reaches 5, and is not affected by the epoch length. The whole brain optimal channels topographic map analysis shows that the optimal channels are mainly distributed in the prefrontal, occipital and temporal lobes, while hardly any is located in the parietal lobe, which indicates that the mental fatigue induced by visual search task characterized similarly among different individuals and highly task-related.
- S. M. Marcora, W. Staiano, and V. Manning, "Mental fatigue impairs physical performance in humans," J. Appl. Physiol., vol. 106, no. 3, pp. 857--864, Mar. 2009.Google ScholarCross Ref
- M. A. S. Boksem and M. Tops, "Mental fatigue: Costs and benefits," Brain Research Reviews, vol. 59, no. 1. Elsevier, pp. 125--139, Nov-2008.Google ScholarCross Ref
- R. Hockey, The psychology of fatigue: Work, effort and control. Cambridge: Cambridge University Press, 2011.Google Scholar
- D. F. DINGES, "An overview of sleepiness and accidents," J. Sleep Res., vol. 4, pp. 4--14, Dec. 1995.Google ScholarCross Ref
- J.-X. Ma, L.-C. Shi, and B.-L. Lu, "An EOG-based Vigilance Estimation Method Applied for Driver Fatigue Detection," Neurosci. Biomed. Eng., vol. 2, no. 1, pp. 41--51, 2015.Google ScholarCross Ref
- R. FU and H. WANG, "Detection of Driving Fatigue By Using Noncontact Emg and Ecg Signals Measurement System," Int. J. Neural Syst., vol. 24, no. 03, p. 1450006, May 2014.Google ScholarCross Ref
- T. Nguyen, S. Ahn, H. Jang, S. C. Jun, and J. G. Kim, "Utilization of a combined EEG/NIRS system to predict driver drowsiness," Sci. Rep., vol. 7, no. 1, p. 43933, Dec. 2017.Google ScholarCross Ref
- H. Wang, "Detection and Alleviation of Driving Fatigue Based on EMG and EMS/EEG Using Wearable Sensor," in Proceedings of the 5th EAI International Conference on Wireless Mobile Communication and Healthcare - "Transforming healthcare through innovations in mobile and wireless technologies," 2015, pp. 155--157. Google ScholarDigital Library
- Chai, R. et al. 2017. Driver Fatigue Classification with Independent Component by Entropy Rate Bound Minimization Analysis in an EEG-Based System. IEEE Journal of Biomedical and Health Informatics. 21, 3 (May 2017), 715--724.Google Scholar
- Dey, I. et al. 2018. Automatic detection of drowsiness in EEG records based on time analysis. 2017 Innovations in Power and Advanced Computing Technologies, i-PACT 2017. 2017-Janua, 2(Feb. 2018), 1--5.Google Scholar
- Kar, S. et al. 2010. EEG signal analysis for the assessment and quantification of driver's fatigue. Transportation Research Part F: Traffic Psychology and Behaviour. 13, 5 (Sep. 2010), 297--306.Google Scholar
- Mu, Z. et al. 2016. Driving Fatigue Detecting Based on EEG Signals of Forehead Area. International Journal of Pattern Recognition and Artificial Intelligence. 31, 5 (May 2016), 1750011.Google Scholar
- Wang, H. et al. 2018. A novel real-time driving fatigue detection system based on wireless dry EEG. Cognitive Neurodynamics. 12, 4 (2018), 365--376.Google Scholar
- Yin, J. et al. 2017. Developing and evaluating a mobile driver fatigue detection network based on electroencephalograph signals. Healthcare Technology Letters. 4, 1 (Feb. 2017), 34--38.Google Scholar
- E. Demandt, C. Mehring, K. Vogt, A. Schulze-Bonhage, A. Aertsen, and T. Ball, "Reaching movement onset- and end-related characteristics of EEG spectral power modulations," Front. Neurosci., vol. 6, no. MAY, p. 65, May 2012.Google ScholarCross Ref
- C. T. Lin et al., "Wireless and wearable EEG system for evaluating driver vigilance," IEEE Trans. Biomed. Circuits Syst., vol. 8, no. 2, pp. 165--176, 2014.Google ScholarCross Ref
- D. de Waard and K. A. Brookhuis, "Assessing driver status: A demonstration experiment on the road," Accid. Anal. Prev., vol. 23, no. 4, pp. 297--307, Aug. 1991.Google ScholarCross Ref
- B. T. Jap, S. Lal, P. Fischer, and E. Bekiaris, "Using EEG spectral components to assess algorithms for detecting fatigue," Expert Syst. Appl., vol. 36, no. 2 PART 1, pp. 2352--2359, Mar. 2009. Google ScholarDigital Library
- J. Min, P. Wang, and J. Hu, "Driver fatigue detection through multiple entropy fusion analysis in an EEG-based system," PLoS One, vol. 12, no. 12, p. e0188756, Dec. 2017.Google ScholarCross Ref
- K. Q. Shen, X. P. Li, C. J. Ong, S. Y. Shao, and E. P. V Wilder-Smith, "EEG-based mental fatigue measurement using multi-class support vector machines with confidence estimate," Clin. Neurophysiol., vol. 119, no. 7, pp. 1524--1533, Jul. 2008.Google ScholarCross Ref
- A. Turnip and D. Soetraprawata, "The Performance of EEG-P300 Classification using Backpropagation Neural Networks," J. Mechatronics, Electr. Power, Veh. Technol., vol. 4, no. 2, p. 81, Dec. 2013.Google ScholarCross Ref
- C. Zhao, C. Zheng, M. Zhao, and J. Liu, "Physiological Assessment of Driving Mental Fatigue Using Wavelet Packet Energy and Random Forests," Am. J. Biomed. Sci., vol. 2010, no. 3, pp. 262--274, 2010.Google ScholarCross Ref
- O. AlZoubi, R. A. Calvo, and R. H. Stevens, "Classification of EEG for affect recognition: An adaptive approach," in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 5866 LNAI, Springer, Berlin, Heidelberg, 2009, pp. 52--61. Google ScholarDigital Library
- R. Chai et al., "Improving EEG-based driver fatigue classification using sparse-deep belief networks," Front. Neurosci., vol. 11, no. MAR, p. 103, Mar. 2017.Google ScholarCross Ref
- J. Hu and J. Min, "Automated detection of driver fatigue based on EEG signals using gradient boosting decision tree model," Cogn. Neurodyn., vol. 12, no. 4, pp. 431--440, Aug. 2018.Google ScholarCross Ref
- J. Chen, H. Wang, and C. Hua, "Electroencephalography based fatigue detection using a novel feature fusion and extreme learning machine," Cogn. Syst. Res., vol. 52, pp. 715--728, Dec. 2018.Google ScholarDigital Library
- Y. Xiong, J. Gao, Y. Yang, X. Yu, and W. Huang, "Classifying driving fatigue based on combined entropy measure using EEG signals," Int. J. Control Autom., vol. 9, no. 3, pp. 329--338.Google ScholarCross Ref
- J. H. (John H. Holland, Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence. MIT Press, 1992. Google ScholarCross Ref
- L. He, Y. Hu, Y. Li, and D. Li, "Channel selection by Rayleigh coefficient maximization based genetic algorithm for classifying single-trial motor imagery EEG," Neurocomputing, vol. 121, pp. 423--433, Dec. 2013. Google ScholarDigital Library
- R. Harikumar, S. Raghavan, and R. Sukanesh, "Genetic algorithm for classification of epilepsy risk levels from EEG signals," in IEEE Region 10 Annual International Conference, Proceedings/TENCON, 2007, vol. 2007, pp. 1585--1589.Google Scholar
- X. Fan, Q. Zhou, Z. Liu, and F. Xie, "Electroencephalogram assessment of mental fatigue in visual search," Biomed. Mater. Eng., vol. 26, no. s1, pp. S1455--S1463, Aug. 2015.Google Scholar
Index Terms
- The Optimal Number and Distribution of Channels in Mental Fatigue Classification Based on GA-SVM
Recommendations
Wavelet Packet Entropy Analysis of Resting State Electroencephalogram in Sleep Deprived Mental Fatigue State
Augmented CognitionAbstractIn order to explore the characteristics of the complexity of resting electroencephalogram (EEG) in mental fatigue state after sleep deprivation, 36 healthy subjects were recruited to participate in the 30 h complete sleep deprivation test, and the ...
Effect of Mental Fatigue on Visual Selective Attention
Engineering Psychology and Cognitive ErgonomicsAbstractTo explore the effect of human mental fatigue on their visual selective attention, and carry out ergonomic design in monitoring operation system. Thirty-two men participated the experiment, 140 min digital 2-back task was used to simulate the ...
An Effective Classification Method for BCI Based on Optimized SVM by GA
GCIS '12: Proceedings of the 2012 Third Global Congress on Intelligent SystemsThis paper proposed an effective method for EEG data classification in a Brain-Computer Interfacing system. We use Principal Component Analysis for feature extracting, then use an optimized Support Vector Machine for classification. The SVM's parameters ...
Comments