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.