Previous studies reported mental stress as one of the major contributing factors leading to various diseases such as heart attack, depression and stroke. An accurate stress assessment method may thus be of importance to clinical intervention and disease prevention. We propose a joint independent component analysis (jICA) based approach to fuse simultaneous measurement of electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) on the prefrontal cortex (PFC) as a means of stress assessment. For the purpose of this study, stress was induced by using an established mental arithmetic task under time pressure with negative feedback. The induction of mental stress was confirmed by salivary alpha amylase test. Experiment results showed that the proposed fusion of EEG and fNIRS measurements improves the classification accuracy of mental stress by +3.4% compared to EEG alone and +11% compared to fNIRS alone. Similar improvements were also observed in sensitivity and specificity of proposed approach over unimodal EEG/fNIRS. Our study suggests that combination of EEG (frontal alpha rhythm) and fNIRS (concentration change of oxygenated hemoglobin) could be a potential means to assess mental stress objectively. References and links1. J. Decker, "The Stress Syndrome," Am. J. Nurs. 65(3), 97-99 (1965). 2. L. R. Murphy, "Stress management in work settings: a critical review of the health effects," Am. J. Health Promot. 11(2), 112-135 (1996). 3. B. Czéh, T. Michaelis, T. Watanabe, J. Frahm, G. de Biurrun, M. van Kampen, A. Bartolomucci, and E. Fuchs, "Stress-induced changes in cerebral metabolites, hippocampal volume, and cell proliferation are prevented by antidepressant treatment with tianeptine," Proc. Natl. Acad. Sci. U.S.A. 98(22), 12796-12801 (2001). 4. C. M. Vander Weele, C. Saenz, J. Yao, S. S. Correia, and K. A. Goosens, "Restoration of hippocampal growth hormone reverses stress-induced hippocampal impairment," Front. Behav. Neurosci. 7, 66 (2013). 5. A. Vyas, R. Mitra, B. S. Shankaranarayana Rao, and S. Chattarji, "Chronic stress induces contrasting patterns of dendritic remodeling in hippocampal and amygdaloid neurons," J. Neurosci. 22(15), 6810-6818 (2002). 6. B. S. McEwen, "Central effects of stress hormones in health and disease: Understanding the protective and damaging effects of stress and stress mediators," Eur. J. Pharmacol. 583(2-3), 174-185 (2008). 7. P. C. Strike and A. Steptoe, "Systematic review of mental stress-induced myocardial ischaemia," Eur. Heart J.24(8), 690-703 (2003). 8. A. Tsutsumi, K. Kayaba, and S. Ishikawa, "Impact of occupational stress on stroke across occupational classes and genders," Soc. Sci. Med. 72(10), 1652-1658 (2011). 9. R. A. Ajjan and P. J. Grant, "Cardiovascular disease prevention in patients with type 2 diabetes: The role of oral anti-diabetic agents," Diab. Vasc. Dis. Res. 3(3), 147-158 (2006). 10. C. Hammen, "Stress and depression," Annu. Rev. Clin. Psychol. 1(1), 293-319 (2005 532-545 (1972). 22. T. G. Vrijkotte, L. J. van Doornen, and E. ...

Driver drowsiness is a major cause of fatal accidents, injury, and property damage, and has become an area of substantial research attention in recent years. The present study proposes a method to detect drowsiness in drivers which integrates features of electrocardiography (ECG) and electroencephalography (EEG) to improve detection performance. The study measures differences between the alert and drowsy states from physiological data collected from 22 healthy subjects in a driving simulator-based study. A monotonous driving environment is used to induce drowsiness in the participants. Various time and frequency domain feature were extracted from EEG including time domain statistical descriptors, complexity measures and power spectral measures. Features extracted from the ECG signal included heart rate (HR) and heart rate variability (HRV), including low frequency (LF), high frequency (HF) and LF/HF ratio. Furthermore, subjective sleepiness scale is also assessed to study its relationship with drowsiness. We used paired t-tests to select only statistically significant features (p < 0.05), that can differentiate between the alert and drowsy states effectively. Significant features of both modalities (EEG and ECG) are then combined to investigate the improvement in performance using support vector machine (SVM) classifier. The other main contribution of this paper is the study on channel reduction and its impact to the performance of detection. The proposed method demonstrated that combining EEG and ECG has improved the system’s performance in discriminating between alert and drowsy states, instead of using them alone. Our channel reduction analysis revealed that an acceptable level of accuracy (80%) could be achieved by combining just two electrodes (one EEG and one ECG), indicating the feasibility of a system with improved wearability compared with existing systems involving many electrodes. Overall, our results demonstrate that the proposed method can be a viable solution for a practical driver drowsiness system that is both accurate and comfortable to wear.

This paper describes a discrete wavelet transform-based feature extraction scheme for the classification of EEG signals. In this scheme, the discrete wavelet transform is applied on EEG signals and the relative wavelet energy is calculated in terms of detailed coefficients and the approximation coefficients of the last decomposition level. The extracted relative wavelet energy features are passed to classifiers for the classification purpose. The EEG dataset employed for the validation of the proposed method consisted of two classes: (1) the EEG signals recorded during the complex cognitive task--Raven's advance progressive metric test and (2) the EEG signals recorded in rest condition--eyes open. The performance of four different classifiers was evaluated with four performance measures, i.e., accuracy, sensitivity, specificity and precision values. The accuracy was achieved above 98 % by the support vector machine, multi-layer perceptron and the K-nearest neighbor classifiers with approximation (A4) and detailed coefficients (D4), which represent the frequency range of 0.53-3.06 and 3.06-6.12 Hz, respectively. The findings of this study demonstrated that the proposed feature extraction approach has the potential to classify the EEG signals recorded during a complex cognitive task by achieving a high accuracy rate.

Mental stress has been identified as one of the major contributing factors that leads to various diseases such as heart attack, depression, and stroke. To avoid this, stress quantification is important for clinical intervention and disease prevention. This study aims to investigate the feasibility of exploiting electroencephalography (EEG) signals to discriminate between different stress levels. We propose a new assessment protocol whereby the stress level is represented by the complexity of mental arithmetic (MA) task for example, at three levels of difficulty, and the stressors are time pressure and negative feedback. Using 18-male subjects, the experimental results showed that there were significant differences in EEG response between the control and stress conditions at different levels of MA task with p values < 0.001. Furthermore, we found a significant reduction in alpha rhythm power from one stress level to another level, p values < 0.05. In comparison, results from self-reporting questionnaire NASA-TLX approach showed no significant differences between stress levels. In addition, we developed a discriminant analysis method based on multiclass support vector machine (SVM) with error-correcting output code (ECOC). Different stress levels were detected with an average classification accuracy of 94.79%. The lateral index (LI) results further showed dominant right prefrontal cortex (PFC) to mental stress (reduced alpha rhythm). The study demonstrated the feasibility of using EEG in classifying multilevel mental stress and reported alpha rhythm power at right prefrontal cortex as a suitable index.

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