The peripheral psychophysiological signals (EMG, ECG and GSR) of 13 participants were recorded in the well planned Cognition and Robotics lab at BTH University and 9 participants data were taken for further processing. Thirty(30) pictures of IAPS were shown to each participant individually as stimuli, and each picture was displayed for five-second intervals. Signal preprocessing, feature extraction and selection, models, datasets formation and data analysis and interpretation were done. The correlation between a combination of EMG, ECG and GSR signal and emotional states were investigated. 2- Dimensional valence-arousal model was used to represent emotional states. Finally, accuracy comparisons among selected machine learning classification algorithms have performed. Context: Psychophysiological measurement is one of the recent and popular ways to identify emotions when using computers or robots. It can be done using peripheral signals: Electromyography (EMG), Electrocardiography (ECG) and Galvanic Skin Response (GSR). The signals from these measurements are considered as reliable signals and can produce the required data. It is further carried out by preprocessing of data, feature selection and classification. Classification of EMG, ECG and GSR data can be conducted with appropriate machine learning algorithms for better accuracy results. Objectives: In this study, we investigate and analyzed with psychophysiological (EMG, ECG and GSR) data to find best classifier algorithm. Our main objective is to classify those data with appropriate machine learning techniques. Classifications of psychophysiological data are useful in emotion recognition. Therefore, our ultimate goal is to provide validated classified psychological measures for the automated adoption of human robot performance. Methods: We conducted a literature review in order to answer RQ1. The sources used are Inspec/ Compendex, IEEE, ACM Digital Library, Google Scholar and Springer Link. This helps us to identify suitable features required for the classification after reading the articles and papers that are peer reviewed as well as lie relevant to the area. Similarly, this helps us to select appropriate machine learning algorithms. We conducted an experiment in order to answer RQ2 and RQ3. A pilot experiment, then after main experiment was conducted in the Cognition and Robotics lab at the university. An experiment was conducted to take measures from EMG, ECG and GSR signal. Results: We obtained different accuracy results using different sets of datasets. The classification accuracy result was best given by the Support Vector Machine algorithm, which gives up to 59% classified emotional states correctly. Conclusions: The psychophysiological signals are very inconsistent with individual participant for specific emotion. Hence, the result we got from the experiment was higher with a single participant than all participants were together. Although, having large number of instances are good to train the classifier well. / The thesis is focused to classify emotional states from physiological signals. Features extraction and selection of the physiological signal was done, which was used for dataset formation and then classification of those emotional states. IAPS pictures were used to elicit emotional/affective states. Experiment was conducted with 13 participants in cognition and Robotics lab using biosensors EMG, ECG and GSR at BTH University. Nine participants data were taken for further preprocessing. We observed in our thesis the classification of emotions which could be analyzed by a combination of psychophysiological signal as Model A and Model B. Since signals of subjects are different for same emotional state, the accuracy was better for single participant than all participants together. Classification of emotional states is useful for HCI and HRI to manufacture emotional intelligence robot. So, it is essential to provide best classifier algorithms which can be helpful to detect emotions for developing emotional intelligence robots. Our work contribution lies in providing best algorithms for emotion recognition for psychophysiological data and selected features. Most of the results showed that SVM performed best with classification accuracy up to 59 % for single participant and 48.05 % for all participants together. For a single dataset and single participant, we found 60.17 % accuracy from MLP but it consumed more time and memory than other algorithms during classification. The rest of the algorithms like BNT, Naive Bayes, KNN and J48 also gave competitive accuracy to SVM. We conclude that SVM algorithm for emotion recognition from a combination of EMG, ECG and GSR is capable of handling and giving better classification accuracy among others. Tally between IAPS pictures with SAM helped to remove less correlated signals and to obtain better accuracies. Still the obtained results are small in percentage. Therefore, more participants are probably needed to get a better accuracy result over the whole dataset. / amarehenry@gmail.com ; Mobile: 0767042234 amrit.pandey111@gmail.com ; Mobile : 0704763190

The objective of the thesis is to get familiar with the method of psychophysiological interactions and its common inplementation. It is explaining the usual methods of removing disruptive signals from the data processed in correlation analysis and presents the possibility of their implementation. In the practical part it is focused on cerating suggested program and its testing on the real data sets.

This study examined individual variability in the strength of association between psychophysiological reactivity to trauma cues and clinician-rated PTSD symptoms in a sample of female survivors of sexual and non-sexual assault. PTSD is a heterogeneous disorder, and individual differences in symptom presentation and accompanying comorbidities may be accounted for by internalizing and externalizing latent temperament-based dimensions of psychopathology. The present study proposed that these dimensions may also account for heterogeneity in the association between psychophysiological reactivity and PTSD. Prior research has demonstrated that most individuals with PTSD display elevated psychophysiological reactivity when exposed to trauma reminders, although some do not. As well, research has shown that externalizing pathologies are typically associated with diminished psychophysiological reactivity to aversive cues whereas internalizing pathologies are associated with elevated psychophysiological reactivity. This study therefore employed structural equation modeling to test hypotheses that externalizing and internalizing pathologies would display mitigating and enhancing moderator effects, respectively, on the prediction of PTSD by psychophysiological reactivity. To that end, confirmatory factor analysis first established a viable internalizing and externalizing model based on an array of clinical measures in one participant subgroup (n = 329) and then affirmed the reliability of the model in a second subgroup (n = 245). Structural equation modeling in the latter subgroup, in which PTSD was regressed on Internalizing, Externalizing, and Psychophysiological Reactivity factors as well as Internalizing by Psychophysiological Reactivity and Externalizing by Psychophysiological Reactivity moderator terms, revealed a significant moderator effect for externalizing but not internalizing pathology. However, the nature of the externalizing moderator effect differed from the hypothesized direction, with higher levels of externalizing pathology strengthening the association between PTSD and psychophysiological reactivity rather than weakening it. It therefore appears that variability in the association between PTSD and psychophysiological reactivity may be partially accounted for by individual differences in the externalizing dimension of psychopathology. As well, the psychophysiology of the externalizing dimension may also be marked by heterogeneity, with externalizing pathology being linked with increased rather than decreased psychophysiological reactivity among women who have experienced sexual or non-sexual assault.

A spacewalk, or extravehicular activity (EVA), is one of the most mission critical and physically and cognitively challenging tasks that crewmembers complete. With next-generation missions to the Moon and Mars, exploration EVA will challenge crewmembers in partial gravity environments with increased frequency, duration, and autonomy of operations. Given the distance from Earth, associated communication delays, and durations of exploration missions, there is a monumental shift in responsibility and authority taking place in spaceflight; moving from Earth-dependent to crew self-reliant. For the safety, efficacy, and efficiency of future surface EVAs, there is a need to better understand crew health and performance. With this knowledge, technology and operations can be designed to better support future crew autonomy. The focus of this dissertation is to develop and evaluate a psychophysiological monitoring tool to classify cognitive workload during an operationally relevant EVA task. This was completed by compiling a sensor suite of commercial wearable devices to record physiological signals in two human research studies, one at Virginia Tech and one at NASA Johnson Space Center. The approach employs supervised machine learning to recognize patterns in psychophysiological features across different psychological states. This relies on the ability to simulate, or induce, cognitive workload in order to label data for training the model. A virtual reality (VR) Translation Task was developed to control and quantify cognitive demands during an immersive, ambulatory EVA scenario. Participants walked on a passive treadmill while wearing a VR headset to move along a virtual lunar surface. They walked with constraints on time and resources, while simultaneously identifying and recalling waypoints in the scene. Psychophysiological features were extracted and labeled according to the task demands, i.e. high or low cognitive workload, for the novel Translation Task, as well as for the benchmark Multi-Attribute Task Battery (MATB). Predictive models were created using the K Nearest Neighbor (KNN) algorithm. The contributions of this dissertation span the simulation, characterization, and modeling of cognitive state. Ultimately, this work tests the limits of extending laboratory psychophysiological monitoring to more realistic environments using wearable devices, and of generalizing predictive models across participants, times, and tasks. This work paves the way for future field studies and real-time implementation to close the loop between human and automation. / Doctor of Philosophy / A spacewalk is one of the most important and physically and mentally challenging tasks that astronauts complete. With next-generation missions to the Moon and Mars, exploration spacewalks will challenge astronauts in reduced-weight environments (1/6 and 1/3 Earth's gravity) with longer, more frequent spacewalks and with less help from mission control. To keep astronauts safe while exploring there is a need to better understand astronaut health and performance (physical and mental) during spacewalks. With knowledge of how astronauts will respond to high workload and stressful events, we can plan missions and design tools that can best assist them during spacewalks on the Moon and Mars when help from Earth mission control is limited. Traditional tools of quantifying mental state are not suitable for real-time assessment during spacewalks. Current methods, including subjective surveys and performance-based computer tests, require time and attention to complete and cannot assess real-time operations. The focus of this dissertation is to create a psychophysiological monitoring tool to measure mental workload during a virtual reality (VR) spacewalk. Psychophysiological monitoring uses physiological measures, like heart rate and breathing rate, to predict psychological state, like high workload or stress. Physiological signals were recorded using commercial wearable devices in two human research studies, one at Virginia Tech and one at NASA Johnson Space Center. With machine learning, computer models can be trained to recognize patterns in physiological measures for different psychological states. Once a model is trained, it can be tested on new data to predict mental workload. To train and test the models, participants in the studies completed high and low workload versions of the VR task. The VR task was specifically designed for this study to simulate and measure performance during a mentally-challenging spacewalk scenario. The participants walked at their own pace on a treadmill while wearing a VR headset to move along a virtual lunar surface, while balancing their time and resources. They were also responsible for identifying and recalling flags along their virtual path. Ultimately, this work tests the limits of extending laboratory psychophysiological monitoring to more realistic environments using wearable devices, and of generalizing predictive models across participants, times, and tasks. This work paves the way for future field studies and real-time implementation to close the loop between human and automation.

本研究主要目的是想要探討當人們面對不同重要性的決策時，情緒對決策的影響是否也會隨之改變，研究者利用眼動及生理測量去量測在不同的實驗情境下，受測者眼動型態以及生理反應。本研究兩個實驗皆為受試者間設計，獨變項分別為情緒(快樂與悲傷)與決策重要程度(高與低)。在實驗一中，研究者給予受試者聆聽不同情緒的音樂，並且藉由指導語操弄決策重要性。另外，受試者被告知在每個嘗試次中必須要挑選出一台想要購買的筆電，以完成決策。根據實驗一的結果，研究者發現無論在眼動指標或者是生理指標皆有情緒與決策重要性的交互作用。然而因為實驗設計的因素，受試者訊息處理的方式並沒有辦法很清楚的被呈現，並且也沒有發現任何早期訊息的篩選過程。在實驗二中，為更進一步檢驗情緒與決策重要性交互作用的情況，實驗中增加了額外的捷思線索(Heuristic cue)。因為增加線索，在實驗二中，產品的訊息便增加了衝突性以及更能夠區別出不同訊息的特性(有用的與無用的訊息)。實驗二的結果呈現出早期訊息篩選的歷程，並且也顯現出決策的動態過程。早期的情緒與決策重要性的交互作用顯現出，快樂與悲傷的受試者在面對高重要性的決策時，的確會有早期的訊息篩選歷程。而在晚期的歷程中，則顯現出快樂的受試者在面對高重要性決策時，會花比較多的時間再評估對他們而言有用的訊息。另外，面對高重要性決策的悲傷受試者，會比面對低重要性決策的悲傷受試者更快完成決策。文中結果將根據Russo 與Leclerc’s(1994)的階段分析與Isen(1993)跟Forgas(1991)所提出的假設進行更詳盡的討論。 / In the present study, the researchers examined how the emotions influenced the decision process when the participants faced decisions with different importance. The eye movement patterns and psychophysiological effects were observed in four different conditions (high importance/positive; high importance/negative; low importance/positive; low importance/negative). In experiment 1, emotion was manipulated by listening music, and decision importance was manipulated by instruction. SCR, HR (heart rate), and eye movement measurement were recorded. The results showed the interaction of emotion and cognition, both in eye movement and psychophysiologcal effect. However, due to the experiment design, it was hard to tell whether the information was processed analytically or heuristically and no early information selective process revealed. In experiment 2, an additional cue was provided in order to examine the interaction between the emotion and decision importance with more differentiated and conflicting alternatives. The results of experiment 2 revealed the early information selective process and showed dynamic process in decision making. The early emotion and cognition interaction showed the happy and sad emotions with high decision importance did process the information selectively. While in the later interaction, it showed the happy emotions with high decision importance spent more time to evaluate the useful information. Moreover, the sad emotions with high decision importance completed the task faster than those with low decision importance. The results were discussed from Russo and Leclerc’s (1994) stage analysis and hypotheses proposed by Isen (1993) and Forgas’s (1991).

Post-exercise carbohydrate-protein consumption and tapering periods during training periodisation have been proposed as effective recovery strategies in several sports; however, limited attention has been given to judo. Apart from training and recovery, effective competition performance can also be influenced by several stimuli on the competition day, which may be manifested as distinct endocrine responses. The main objective of this thesis was to influence effective competition performance in judo, through examining strategies that can aid recovery from intense exercise/training and examining endocrine responses to competition. Three experimental studies on recovery were completed (chapters 3-5) followed by an observational study on a judo competition day (chapter 6) in elite, national level, male judo athletes. Studies 1 and 2 examined the effects 1000 ml of post-exercise chocolate milk (CM) consumption compared with water (W) following an intense judo training session (chapter 3) and five days of intense judo training with concomitant weight loss (chapter 4) on the recovery of salivary cortisol (sC), salivary testosterone (sT), salivary testosterone:cortisol (sT/C) ratio, salivary secretory IgA (SIgA) absolute concentrations and secretion rate, muscle soreness, mood state and judo-related performance. Study 1 (n=10) did not show any beneficial effects of acute CM consumption on aspects of recovery of any of the measured variables, except for a lower perception of soreness (p<0.05) and a tendency for better push-up performance (p=0.09). Study 2 (n=12) showed that post-exercise CM consumption resulted in significantly lower sC levels, a tendency for higher sT/C ratio (p=0.07), better judo-related performance, lower muscle soreness and reduced mood disturbance (p<0.05) with W. In addition, post-exercise consumption of CM resulted in a 1.1% decrease in body weight, indicating that CM is an effective recovery beverage during periods of intense judo training without affecting intentional weight loss. Study 3 (n=11) examined the effects of a 2-week exponential taper following 2 weeks of intense judo training on recovery of the aforementioned variables. Within 12 days of tapering there were evidence of enhanced performance, lower sC, higher sT and higher sT/C ratio, higher SIgA secretion rate, lower muscle soreness and reduced mood disturbance, indicating that a tapering period of ~10 days is an effective recovery strategy for optimising judo performance. Study 4 observed the responses of sC, sT, SIgA absolute concentrations and SIgA secretion rate and self-measured anxiety state in the winners (n=12) and losers (n=11) of a judo competition. Winners presented significantly higher morning sC levels and higher cognitive anxiety in anticipation of the competition, as well as a tendency for higher SIgA secretion rate (p=0.07) and significantly higher saliva flow rate mid-competition. These findings indicate that winners experienced higher arousal levels and that anticipatory sC might have some predictive value for winning performance in judo. This thesis concludes that nutrition and tapering are both important aspects of effective recovery; CM can be an effective nutritional recovery aid during periods of intense judo training and tapering for 7-12 days can optimise judo performance and can be implemented prior to competitions. In addition, elevated sC levels in anticipation of a judo competition and higher levels of arousal could have some predictive value for winning performance in judo. Further research could focus on strategies to increase levels of arousal in anticipation of competition.

In neuroscience and biomedical engineering fields, one of the most important issues nowadays is finding a relationship between different brain regions when it is stimulated. Connectivity is an important research area in neuroscience which tries to determine the relationship between different brain region when the brain is stimulated externally or internally. Three main type of connectivity are discussed in this field: Anatomical, Functional and Effective connectivity. Importance of effective connectivity is its ability to detect brain disorders in early stages. Some brain disorders are Schizophrenia, MS and Major Depression disease. Comparing the effective connectivity between a healthy and unhealthy brain will help to diagnose brain disorder. In this master study, two methods named Dynamic Causal Modeling (DCM) and Psychophysiological Interaction (PPI) are used to compare effective connectivity and neuronal activity between different regions of brain when there are three different stimulations. Since the neural activity is latent in fMRI data, there is a need to a model which is able to transfer data from neuronal level to a visible data like Blood-Oxygen level dependent (BOLD) signal. DCM uses a haemodynamic balloon model (HD) to represent this data transfer. The hemodynamic model must be so that the parameters of neural and BOLD signal be the same. It should be noted that what is looked for is not the BOLD signal but the neuronal activity. In this study, as the first step, we did preprocessing of MR images and after ROI`s are created using the program MARSBAR. Ten ROIs, which are thought to have connections between them are selected by considering the stimulations used in the experiments in obtaining the data used in this thesis. The data used contains fMRI images of 11 healthy subjects. Stimulations of experiment are applied to images got from group analysis of 11 healthy subjects. These Stimulations are then used in preparing the design matrix and the parameters related to DCM. These parameters are the values related to connection matrices defining bilinear dynamic model on ROI. Bayesian method is used to select best model between all these models. Another method of PPI is also applied to analyze effective connectivity between 10 ROIs. This method considers two issues of physiological and psychological effects. Like DCM, the preprocessing steps and ROI selection is done for PPI and hemodynamic model is designed for this method. Neural and hemodynamic responses of ROIs are compared using this method.

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<p>Repetitive and monotonous work is frequently associated with neck and shoulder pain and negative psychosocial factors inducing stress reactions. The present thesis concerns the relations between psychophysiological and subjective stress reactions, muscle activity measured by surface electromyography (SEMG) in the trapezius muscle, and neck and shoulder pain in women performing repetitive and monotonous work. In Study I cardiovascular and subjective stress reactions were investigated during computer work in a laboratory setting. The findings indicated that heart rate variability is a more sensitive and selective measure of mental stress compared with blood pressure recordings. Study II explored the relations between stress reactions and muscle activity during supermarket work. The results showed that perceived negative stress reactions may have a specific influence on muscle activity in the neck and shoulder region, which can be of importance for work-related musculoskeletal disorders in repetitive and monotonous work. In Study III the association between SEMG activity patterns and neck and shoulder pain was investigated during cash register work. It was found that pain-afflicted women had a different muscle activation pattern (more static, more co-contraction, less muscle rest) compared with pain-free women. Study IV was a follow-up study evaluating the introduction of job rotation among female cashiers. The results indicated positive effects on diastolic blood pressure, muscle activity, and partly on neck and shoulder pain, although perceived stress was unchanged. It was concluded that job rotation seems to have a limited effect on chronic neck and shoulder pain, but may be an effective preventive measure. The empirical findings are particularly relevant for women who, compared with men, more often perform repetitive and monotonous work and are also more often affected by neck and shoulder pain.</p>