No abstract

Vigilance level assessment is of prime importance to avoid life-threatening human error. Critical working environments such as air traffic control, driving, or military surveillance require the operator to be alert the whole time. The electroencephalogram (EEG) is a very common modality that can be used in assessing vigilance. Unfortunately, EEG signals are prone to artifacts due to eye movement, muscle contraction, and electrical noise. Mitigating these artifacts is important for an accurate vigilance level assessment. Independent Component Analysis (ICA) is an effective method and has been extensively used in the suppression of EEG artifacts. However, in vigilance assessment applications, it was found to suffer from leakage of the cerebral activity into artifacts. In this work, we show that the wavelet ICA (wICA) method provides an alternative for artifact reduction, leading to improved vigilance level assessment results. We conducted an experiment in nine human subjects to induce two vigilance states, alert and vigilance decrement, while performing a Stroop Color–Word Test for approximately 45 min. We then compared the performance of the ICA and wICA preprocessing methods using five classifiers. Our classification results showed that in terms of features extraction, the wICA method outperformed the existing ICA method. In the delta, theta, and alpha bands, we obtained a mean classification accuracy of 84.66% using the ICA method, whereas the mean accuracy using the wICA methodwas 96.9%. However, no significant improvement was observed in the beta band. In addition, we compared the topographical map to show the changes in power spectral density across the brain regions for the two vigilance states. The proposed method showed that the frontal and central regions were most sensitive to vigilance decrement. However, in this application, the proposed wICA shows a marginal improvement compared to the Fast-ICA.

Mathematical modeling has been used to simulate the interaction of chemotherapy and immunotherapy drugs intervention with the dynamics of tumor cells growth. This work studies the interaction of cells in the immune system, such as the natural killer, dendritic, and cytotoxic CD8+ T cells, with chemotherapy. Four different cases were considered in the simulation: no drug intervention, independent interventions (either chemotherapy or immunotherapy), and combined interventions of chemotherapy and immunotherapy. The system of ordinary differential equations was initially solved using the Runge-Kutta method and compared with two additional methods: the Explicit Euler and Heun’s methods. Results showed that the combined intervention is more effective compared to the other cases. In addition, when compared with Runge-Kutta, the Heun’s method presented a better accuracy than the Explicit Euler technique. The proposed mathematical model can be used as a tool to improve cancer treatments and targeted therapy.

Vigilance is the capacity to remain alert for an extended time while performing a task. Staying alert is obligatory in many jobs, particularly those that involve monitoring, such as surveillance tasks, security monitoring, and air traffic control. These monitoring tasks require a specific level of arousal to maintain an adequate level of cognitive efficiency. In this study, we investigate the possibility of assessing the vigilance levels using a fusion of electroencephalography (EEG) and eye tracking data. Vigilance levels were established by performing a modified version of the Stroop color word task (SCWT) for 30 minutes. Feature-level fusion based on the canonical correlation analysis (CCA) was employed to each brain region to improve the classification accuracy of vigilance level assessment. Results obtained using support vector machines (SVM) classifier show that fusion of EEG+eye tracking modalities has improved the classification accuracy compared to individual modality. The EEG+Eye tracking fusion on the right central brain region achieved the highest classification accuracy of 97.4 ± 1.3%, compared to the individual Beta EEG with 92.0± 7.3% and Eye tracking with 76.8± 8.4%, respectively. Likewise, EEG and Eye tracking fusion on the right frontal region showed classification accuracy of 96.9 ± 1.1% for both the Alpha and Beta bands. Meanwhile, when all brain regions were utilized, the highest classification accuracy of EEG+Eye tracking was 96.8 ± 0.6% using Delta band compared to the EEG alone with 88.18 ± 8.5% and eye tracking alone with 76.8 ± 8.4 %, respectively. The overall results showed that vigilance is a brain region specific and the fusion of EEG+ and Eye tracking data using CCA has significantly improved the classification accuracy of vigilance levels assessment.