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Personality traits are fundamental parameters to characterize an individual’s behavior. Recently physiological signals are used to recognize personality traits in response to emotional videos, but gives good accuracies only for few traits. In this study, personality traits are statistically analyzed and recognized using physiological signals (electroencephalography (EEG), galvanic skin response (GSR), and photoplethysmogram (PPG)) in response to public speaking. The proposed framework consists of data acquisition, pre-processing, feature extraction, feature selection, and classification stages. The subject is labeled into two classes of each personality trait based on the mean value of big five personality trait questionnaire score. The physiological signals of twenty-eight participants are recorded while performing a public speaking task in front of a real audience. These signals are statistically analyzed to examine the behavior of different personality traits. Time domain features from GSR and PPG data and frequency domain features from EEG data are extracted. A wrapper based method for feature selection is applied to select an optimum set of features from each modality, which are then fused for personality trait recognition. An average F1-score of 0.86, 0.96, 0.64, 0.75, and 0.86 is achieved by the proposed framework for openness to experience, extroversion, neuroticism, conscientiousness, and agreeableness traits respectively. It is observed that the personality trait recognition using physiological signals in response to public speaking task outperforms the existing methods. [ABSTRACT FROM AUTHOR]

Human stress is a serious health concern, which must be addressed with appropriate actions for a healthy society. This paper presents an experimental study to ascertain the appropriate phase, when electroencephalography (EEG) based data should be recorded for classification of perceived mental stress. The process involves data acquisition, pre-processing, feature extraction and selection, and classification. The stress level of each subject is recorded by using a standard perceived stress scale questionnaire, which is then used to label the EEG data. The data are divided into two (stressed and non-stressed) and three (non-stressed, mildly stressed, and stressed) classes. The EEG data of 28 participants are recorded using a commercially available four channel Muse EEG headband in two phases i.e., pre-activity and post-activity. Five feature groups, which include power spectral density, correlation, differential asymmetry, rational asymmetry, and power spectrum are extracted from five bands of each EEG channel. We propose a new feature selection algorithm, which selects features from appropriate EEG frequency band based on classification accuracy. Three classifiers i.e., support vector machine, the Naive Bayes, and multi-layer perceptron are used to classify stress level of the participants. It is evident from our results that EEG recording during the pre-activity phase is better for classifying the perceived stress. An accuracy of $\text{92.85}\%$ and $\text{64.28}\%$ is achieved for two- and three-class stress classification, respectively, while utilizing five groups of features from theta band. Our proposed feature selection algorithm is compared with existing algorithms and gives better classification results. [ABSTRACT FROM AUTHOR]

The problem addressed in this paper is the detection of drowsiness based on signals acquired using non-invasive and wearable sensors. A combination of signals from electroencephalography (EEG) electrodes, accelerometer, and gyroscope is acquired through a commercially available off-the-shelf headband to achieve this goal. The aim is to substitute the use of intrusive sensors and cameras by extracting both physiological and behavioral features for drowsiness detection using a lightweight wearable headband. A dataset is acquired from 50 volunteers using the MUSE headband for both fresh and drowsy behavior in the resting state, where subjects stare at a slow moving object on a screen and no external stimuli is used. The drowsy state is induced by sleep deprivation. The data are labeled subjectively using the Karolinska sleepiness scale. Complementary features extracted from spectral signals, head movement, and blink analysis are combined to form a feature vector for classifiers to improve the robustness and detection accuracy of the system. The implemented system is validated on the data collected in fresh and drowsy states using a leave-one-out cross-subject and 25% hold-out level testing. A detection accuracy of 92% is achieved using selected features and a linear support vector machine classifier. The results show that the accuracy of drowsiness detection is improved by using a hybrid feature vector acquired from data using multiple sensors. [ABSTRACT FROM AUTHOR]