Assessment of linear and nonlinear/complex heartbeat dynamics in subclinical depression (dysphoria)

Objective: Depression is one of the leading causes of disability worldwide. Most previous studies have focused on major depression, and studies on subclinical depression, such as those on so-called dysphoria, have been overlooked. Indeed, dysphoria is associated with a high prevalence of somatic disorders, and a reduction of quality of life and life expectancy. In current clinical practice, dysphoria is assessed using psychometric questionnaires and structured interviews only, without taking into account objective pathophysiological indices. To address this problem, in this study we investigated heartbeat linear and nonlinear dynamics to derive objective autonomic nervous system biomarkers of dysphoria. Approach: Sixty undergraduate students participated in the study: according to clinical evaluation, 24 of them were dysphoric. Extensive group-wise statistics was performed to characterize the pathological and control groups. Moreover, a recursive feature elimination algorithm based on a K-NN classifier was carried out for the automatic recognition of dysphoria at a single-subject level. Main results: The results showed that the most significant group-wise differences referred to increased heartbeat complexity (particularly for fractal dimension, sample entropy and recurrence plot analysis) with regards to the healthy controls, confirming dysfunctional nonlinear sympatho-vagal dynamics in mood disorders. Furthermore, a balanced accuracy of 79.17% was achieved in automatically distinguishing dysphoric patients from controls, with the most informative power attributed to nonlinear, spectral and polyspectral quantifiers of cardiovascular variability. Significance: This study experimentally supports the assessment of dysphoria as a defined clinical condition with specific characteristics which are different both from healthy, fully euthymic controls and from full-blown major depression

Optimization of least squares support vector machine technique using genetic algorithm for electroencephalogram multi-dimensional signals

Human-computer intelligent interaction (HCII) is a rising field of science that aims to refine and enhance the interaction between computer and human. Since emotion plays a vital role in human daily life, the ability of computer to interpret and response to human emotion is a crucial element for future intelligent system.Accordingly, several studies have been conducted to recognise human emotion using different technique such as facial expression, speech, galvanic skin response (GSR), or heart rate (HR).However, such techniques have problems mainly in terms of credibility and reliability as people can fake their feeling and response. Electroencephalogram (EEG) on the other has shown to be a very effective way in recognising human emotion as this technique records the brain activity of human and they can hardly be deceived by voluntary control. Regardless the popularity of EEG in recognizing human emotion, this study field is relatively challenging as EEG signal is nonlinear, involves myriad factors and chaotic in nature.These issues have led to high dimensional problem and poor classification results.To address such problems, this study has proposed a novel computational model, which consist of three main stages, namely a) feature extraction; b) feature selection and c) classifier. Discrete wavelet packet transform (DWPT) has been used to extract EEG signals feature and ultimately 204,800 features from 32 subject-independent have been obtained. Meanwhile, Genetic Algorithm (GA) and Least squares support vector machine (LS-SVM) have been used as a feature selection technique and classifier respectively.This computational model is tested on the common DEAP pre-processed EEG dataset in order to classify three levels of valence and arousal.The empirical results have shown that the proposed GA-LSSVM, has improved the classification results to 49.22% and 54.83% for valence and arousal respectively, whereas is it observed that 46.33% of valence and 48.30% of arousal classification were achieved when no feature selection technique is applied on the identical classifier

SAFE: An EEG dataset for stable affective feature selection

An affective brain-computer interface (aBCI) is a direct communication pathway between human brain and computer, via which the computer tries to recognize the affective states of its user and respond accordingly. As aBCI introduces personal affective factors into human-computer interaction, it could potentially enrich the user’s experience during the interaction. Successful emotion recognition plays a key role in such a system. The state-of-the-art aBCIs leverage machine learning techniques which consist in acquiring affective electroencephalogram (EEG) signals from the user and calibrating the classifier to the affective patterns of the user. Many studies have reported satisfactory recognition accuracy using this paradigm. However, affective neural patterns are volatile over time even for the same subject. The recognition accuracy cannot be maintained if the usage of aBCI prolongs without recalibration. Existing studies have overlooked the performance evaluation of aBCI during long-term use. In this paper, we propose SAFE—an EEG dataset for stable affective feature selection. The dataset includes multiple recording sessions spanning across several days for each subject. Multiple sessions across different days were recorded so that the long-term recognition performance of aBCI can be evaluated. Based on this dataset, we demonstrate that the recognition accuracy of aBCIs deteriorates when re-calibration is ruled out during long-term usage. Then, we propose a stable feature selection method to choose the most stable affective features, for mitigating the accuracy deterioration to a lesser extent and maximizing the aBCI performance in the long run. We invite other researchers to test the performance of their aBCI algorithms on this dataset, and especially to evaluate the long-term performance of their methods

Towards Confident Body Sensor Networking

With the recent technology advances of wireless communication and lightweight low-power sensors, Body Sensor Network (BSN) is made possible. More and more researchers are interested in developing numerous novel BSN applications, such as remote health/fitness monitoring, military and sport training, interactive gaming, personal information sharing, and secure authentication. Despite the unstable wireless communication, various confidence requirements are placed on the BSN networking service. This thesis aims to provide Quality of Service (QoS) solutions for BSN communication, in order to achieve the required confidence goals.;We develop communication quality solutions to satisfy confidence requirements from both the communication and application levels, in single and multiple BSNs. First, we build communication QoS, targeting at providing service quality guarantees in terms of throughput and time delay on the communication level. More specifically, considering the heterogeneous BSN platform in a real deployment, we develop a radio-agnostic solution for wireless resource scheduling in the BSN. Second, we provide a QoS solution for both inter- and intra-BSN communications when more than one BSNs are involved. Third, we define application fidelity for two neurometric applications as examples, and bridge a connection between the communication QoS and application QoS

Mental task classification using single-electrode brain computer interfaces

In the recent years, the field of Human-Computer Interaction (HCI) has greatly evolved to involve new and exciting interaction paradigms that allow users to interact with their environment and with technology in a more intuitive and ergonomic way. These interaction paradigms include voice, touch, virtual reality, and more recently, the brain. A brain-computer interface (BCI) is a an interface system allowing users to control devices without using the normal output pathways of peripherals, instead, by using neural activity generated in the brain. BCIs have a huge potential in a multitude of fields, all the way from providing users with severe motor disabilities with means for interaction with the external world, to entertainment, gaming, user state monitoring, and self-tracking systems. The potentials of BCI have sparked the interest of researchers, gaming markets and healthcare providers more and more in the recent years. The is due to the emergence of new commercial lightweight, low cost Electroencephalograph (EEG) equipment that made it possible to create more portable and usable BCI systems and expanded their fields of application. This Master thesis aims to explore the state of the art commercial BCI as well as the uses and challenges related to them. Commercially available EEG equipment, namely the Neurosky Brainband and Neurosky Mindset, will be investigated. User tests will be carried out to investigate whether such equipment with low accuracy and low cost can be used to recognize various mental activities. This would be performed by first collecting a dataset of brain signals during performing a set of mental tasks, which is one of the contributions of this project, followed by applying a set of signal processing algorithms, then exploring various classification techniques to classify the collected signals

User Preference Extraction from Bio-Signals: An Experimental Study

Abstract User Preference Extraction from Bio-Signals: An Experimental Study Golam Mohammad Moshiuddin Aurup The purpose of this study is to extract user preferences about a product from emotional responses. Literature on psychology reveals that human preferences are related to their emotions. In addition, literature on emotion recognition reveals that emotion can be extracted from physiological signals like the heart rate, skin conductance, brain signal etc. In this study, these two streams were brought together and a new approach was proposed to extract preference of users through the analysis of emotional responses from physiological signals. Brain signal (electroencephalography or EEG) was chosen in this regard for its relevance in emotion recognition literature. For the experimental study, Thought Technology’s biofeedback system was used in order to capture and process the users’ EEG signals while users are exposed to various images where each image represents a possible feature of a product. Experiments were performed in two phases. In the first phase, proposed method, hardware setting, and the hypothesis relating user preference and EEG values were tested on an image library. In the second phase, images relating to a product (automobile in our case) design or features were used to get preferences between competing products. In the first phase, International Affective Picture System (IAPS) library images were used to develop experiments. This library provides a large database for emotion affecting images and is widely accepted in the emotion detection literature. Three participants and three two-image sets were used in this study. The relationship between preference and extracted values were established through graph plot and trend line analysis and effects of repetition of experiments or images were identified. Results supported that analysis of EEG signals can distinguish pleasant and unpleasant feeling about images. A maximum of 80% accuracy was obtained in establishing relationship between preference and signal values. Left frontal side of the brain provided with the best results and was utilized in the rest of the study. Possibility to use different frequency bands of EEG signal was also studied in this phase. In the second phase of experiments, 8 image-sets relating to automobile design and features were used for a group of 11 participants. 60% of the participants responded with 70% or more accuracy. It was found that the cognitive preference of a participant was stronger than the aesthetic preference whenever there was a conflict between the two. Accuracy rate showed by participants varied with the quality of the tests; i.e. with the factors like image resolution, clarity, composition, subject, and background of images; and with the capability of the participant to identify the images properly. Literature on brain activity reports that, for some people, the left side of the brain is more active than the right one. The opposite is true for others. The hypothesis relating preference and extracted values was corrected in this regard. The corrected hypothesis was termed the reverse hypothesis. At the beginning of phase 2 experiments, 4 experiments were developed with IAPS images to identify if the participant followed the preliminary hypothesis or the reversed one. The results showed that most participants performed better in the experiments with product images than the experiments with standard IAPS images