Cognitive workload monitoring in virtual reality based rescue missions with drones

The use of drones in search and rescue (SAR) missions can be very cognitively demanding. Since high levels of cognitive workload can negatively affect human performance, there is a risk of compromising the mission and leading to failure with catastrophic outcomes. Therefore, cognitive workload monitoring is the key to prevent the rescuers from taking dangerous decisions. Due to the difficulties of gathering data during real SAR missions, we rely on virtual reality. In this work, we use a simulator to induce three levels of cognitive workload related to SAR missions with drones. To detect cognitive workload, we extract features from different physiological signals, such as electrocardiogram, respiration, skin temperature, and photoplethysmography. We propose a recursive feature elimination method that combines the use of both an eXtreme Gradient Boosting (XGBoost) algorithm and the SHapley Additive exPlanations (SHAP) score to select the more representative features. Moreover, we address both a binary and a three-class detection approaches. To this aim, we investigate the use of different machine-learning algorithms, such as XGBoost, random forest, decision tree, k-nearest neighbors, logistic regression, linear discriminant analysis, gaussian naïve bayes, and support vector machine. Our results show that on an unseen test set extracted from 24 volunteers, an XGBoost with 24 features for discrimination reaches an accuracy of 80.2% and 62.9% in order to detect two and three levels of cognitive workload, respectively. Finally, our results are open the doors to a fine grained cognitive workload detection in the field of SAR missions.

Boosting Pixel-based Classifiers for Face Verification

Sébastien Marcel, Yann Rodriguez

The performance of face verification systems has steadily improved over the last few years. State-of-the-art methods use the projection of the gray-scale face image into a Linear Discriminant subspace as input of a classifier such as Support Vector Machines or Multi-layer Perceptrons. Unfortunately, these classifiers involve thousands of parameters that are difficult to store on a smart-card for instance. Recently, boosting algorithms has emerged to boost the performance of simple (weak) classifiers by combining them iteratively. The famous AdaBoost algorithm have been proposed for object detection and applied successfully to face detection. In this paper, we investigate the use of AdaBoost for face verification to boost weak classifiers based simply on pixel values. The proposed approach is tested on a benchmark database, namely XM2VTS. Results show that boosting only hundreds of classifiers achieved near state-of-the-art results. Furthermore, the proposed approach outperforms similar work on face verification using boosting algorithms on the same database.

IDIAP2003

Boosting Pixel-based Classifiers for Face Verification

Sébastien Marcel, Yann Rodriguez

The performance of face authentication systems has steadily improved over the last few years. State-of-the-art methods use the projection of the gray-scale face image into a Linear Discriminant subspace as input of a classifier such as Support Vector Machines or Multi-layer Perceptrons. Unfortunately, these classifiers involve thousands of parameters that are difficult to store on a smart-card for instance. Recently, boosting algorithms has emerged to boost the performance of simple (weak) classifiers by combining them iteratively. The famous AdaBoost algorithm have been proposed for object detection and applied successfully to face detection. In this paper, we investigate the use of AdaBoost for face authentication to boost weak classifiers based simply on pixel values. The proposed approach is tested on a benchmark database, namely XM2VTS. Results show that boosting only hundreds of classifiers achieved near state-of-the-art results. Furthermore, the proposed approach outperforms similar work on face authentication using boosting algorithms on the same database.

Springer-Verlag2004

Quantifying the time for accurate EEG decoding of single value-based decisions

Ricardo Andres Chavarriaga Lozano, Athina Tzovara

Background Recent neuroimaging studies suggest that value-based decision-making may rely on mechanisms of evidence accumulation. However no studies have explicitly investigated the time when single decisions are taken based on such an accumulation process. New method Here, we outline a novel electroencephalography (EEG) decoding technique which is based on accumulating the probability of appearance of prototypical voltage topographies and can be used for predicting subjects’ decisions. We use this approach for studying the time-course of single decisions, during a task where subjects were asked to compare reward vs. loss points for accepting or rejecting offers. Results We show that based on this new method, we can accurately decode decisions for the majority of the subjects. The typical time-period for accurate decoding was modulated by task difficulty on a trial-by-trial basis. Typical latencies of when decisions are made were detected at ~500ms for ‘Easy’ vs. ~700ms for ‘Hard’ decisions, well before subjects’ response (~340 ms). Importantly, this decision time correlated with the drift rates of a diffusion model, evaluated independently at the behavioral level. Comparison with Existing Method(s) We compare the performance of our algorithm with logistic regression and support vector machine and show that we obtain significant results for a higher number of subjects than with these two approaches. We also carry out analyses at the average event-related potential level, for comparison with previous studies on decision-making. Conclusions We present a novel approach for studying the timing of value-based decision-making, by accumulating patterns of topographic EEG activity at single-trial level.

Elsevier2015