This review investigates the effects of psychological stress on the human body measured through biosignals. When a potentially threatening stimulus is perceived, a cascade of physiological processes occurs mobilizing the body and nervous system to confront the imminent threat and ensure effective adaptation. Biosignals that can be measured reliably in relation to such stressors include physiological (EEG, ECG, EDA, EMG) and physical measures (respiratory rate, speech, skin temperature, pupil size, eye activity). A fundamental objective in this area of psychophysiological research is to establish reliable biosignal indices that reveal the underlying physiological mechanisms of the stress response. Motivated by the lack of comprehensive guidelines on the relationship between the multitude of biosignal features used in the literature and their corresponding behaviour during stress, in this paper, the impact of stress to multiple bodily responses is surveyed. Emphasis is put on the efficiency, robustness and consistency of biosignal data features across the current state of knowledge in stress detection. It is also explored multimodal biosignal analysis and modelling methods for deriving accurate stress correlates. This paper aims to provide a comprehensive review on biosignal patterns caused during stress conditions and reliable practical guidelines towards more efficient detection of stress.

Eye behaviour provides valuable information revealing one's higher cognitive functions and state of affect. Although eye tracking is gaining ground in the research community, it is not yet a popular approach for the detection of emotional and cognitive states. In this paper, we present a review of eye and pupil tracking related metrics (such as gaze, fixations, saccades, blinks, pupil size variation, etc.) utilized towards the detection of emotional and cognitive processes, focusing on visual attention, emotional arousal and cognitive workload. Besides, we investigate their involvement as well as the computational recognition methods employed for the reliable emotional and cognitive assessment. The publicly available datasets employed in relevant research efforts were collected and their specifications and other pertinent details are described. The multimodal approaches which combine eye-tracking features with other modalities (e.g. biosignals), along with artificial intelligence and machine learning techniques were also surveyed in terms of their recognition/classification accuracy. The limitations, current open research problems and prospective future research directions were discussed for the usage of eye-tracking as the primary sensor modality. This study aims to comprehensively present the most robust and significant eye/pupil metrics based on available literature towards the development of a robust emotional or cognitive computational model.

Depression is a major cause of disability world-wide. The present paper reports on the results of our participation to the depression sub-challenge of the sixth Audio/Visual Emotion Challenge (AVEC 2016), which was designed to compare feature modalities (audio, visual, interview transcript-based) in gender-based and gender-independent modes using a variety of classification algorithms. In our approach, both high and low level features were assessed in each modality. Audio features were extracted from the low-level descriptors provided by the challenge organizers. Several visual features were extracted and assessed including dynamic characteristics of facial elements (using Landmark Motion History Histograms and Landmark Motion Magnitude), global head motion, and eye blinks. These features were combined with statistically derived features from pre-extracted features (emotions, action units, gaze, and pose). Both speech rate and word-level semantic content were also evaluated. Classification results are reported using four different classification schemes: i) gender-based models for each individual modality, ii) the feature fusion model, ii) the decision fusion model, and iv) the posterior probability classification model. Proposed approaches outperforming the reference classification accuracy include the one utilizing statistical descriptors of low-level audio features. This approach achieved f1-scores of 0.59 for identifying depressed and 0.87 for identifying not-depressed individuals on the development set and 0.52/0.81, respectively for the test set.

This paper studies the effect of stress/anxiety states on EEG signals during video sessions. The levels of arousal and valence that are induced to each subject while watching each video are self rated. These levels are mapped in stress and relaxed states and subjects that fufill criteria of adequate anxiety/stress scale were chosen leading to a subset of 18 subjects. Then, temporal, spectral and non linear EEG features are evaluated for being able to represent accurately states under investigation. Feature selection schemes choose the most significant of them in order to provide increased discrimination ability between relaxed and anxiety/stress states.