Comparison of an open-hardware electroencephalography amplifier with medical grade device in brain-computer interface applications

Brain-computer interfaces (BCI) are promising communication devices between humans and machines. BCI based on non-invasive neuroimaging techniques such as electroencephalography (EEG) have many applications , however the dissemination of the technology is limited, in part because of the price of the hardware. In this paper we compare side by side two EEG amplifiers, the consumer grade OpenBCI and the medical grade g.tec g.USBamp. For this purpose, we employed an original montage, based on the simultaneous recording of the same set of electrodes. Two set of recordings were performed. During the first experiment a simple adapter with a direct connection between the amplifiers and the electrodes was used. Then, in a second experiment, we attempted to discard any possible interference that one amplifier could cause to the other by adding "ideal" diodes to the adapter. Both spectral and temporal features were tested -- the former with a workload monitoring task, the latter with an visual P300 speller task. Overall, the results suggest that the OpenBCI board -- or a similar solution based on the Texas Instrument ADS1299 chip -- could be an effective alternative to traditional EEG devices. Even though a medical grade equipment still outperforms the OpenBCI, the latter gives very close EEG readings, resulting in practice in a classification accuracy that may be suitable for popularizing BCI uses.Comment: PhyCS - International Conference on Physiological Computing Systems, Jul 2016, Lisbon, Portugal. SCITEPRESS, 201

Impact of age, VR, immersion, and spatial resolution on classifier performance for a MI-based BCI

There are many factors outlined in the signal processing pipeline that impact brain–computer interface (BCI) performance, but some methodological factors do not depend on signal processing. Nevertheless, there is a lack of research assessing the effect of such factors. Here, we investigate the impact of VR, immersiveness, age, and spatial resolution on the classifier performance of a Motor Imagery (MI) electroencephalography (EEG)-based BCI in naïve participants. We found significantly better performance for VR compared to non-VR (15 electrodes: VR 77.48 ± 6.09%, non-VR 73.5 ± 5.89%, p = 0.0096; 12 electrodes: VR 73.26 ± 5.2%, non-VR 70.87 ± 4.96%, p = 0.0129; 7 electrodes: VR 66.74 ± 5.92%, non-VR 63.09 ± 8.16%, p = 0.0362) and better performance for higher electrode quantity, but no significant differences were found between immersive and non immersive VR. Finally, there was not a statistically significant correlation found between age and classifier performance, but there was a direct relation found between spatial resolution (electrode quantity) and classifier performance (r = 1, p = 0.0129, VR; r = 0.99, p = 0.0859, non-VR).info:eu-repo/semantics/publishedVersio

Is the timed-up and go test feasible in mobile devices? A systematic review

The number of older adults is increasing worldwide, and it is expected that by 2050 over 2 billion individuals will be more than 60 years old. Older adults are exposed to numerous pathological problems such as Parkinson’s disease, amyotrophic lateral sclerosis, post-stroke, and orthopedic disturbances. Several physiotherapy methods that involve measurement of movements, such as the Timed-Up and Go test, can be done to support efficient and effective evaluation of pathological symptoms and promotion of health and well-being. In this systematic review, the authors aim to determine how the inertial sensors embedded in mobile devices are employed for the measurement of the different parameters involved in the Timed-Up and Go test. The main contribution of this paper consists of the identification of the different studies that utilize the sensors available in mobile devices for the measurement of the results of the Timed-Up and Go test. The results show that mobile devices embedded motion sensors can be used for these types of studies and the most commonly used sensors are the magnetometer, accelerometer, and gyroscope available in off-the-shelf smartphones. The features analyzed in this paper are categorized as quantitative, quantitative + statistic, dynamic balance, gait properties, state transitions, and raw statistics. These features utilize the accelerometer and gyroscope sensors and facilitate recognition of daily activities, accidents such as falling, some diseases, as well as the measurement of the subject's performance during the test execution.info:eu-repo/semantics/publishedVersio

Reconnaissance de l'émotion thermique

Pour améliorer les interactions homme-ordinateur dans les domaines de la santé, de l'e-learning et des jeux vidéos, de nombreux chercheurs ont étudié la reconnaissance des émotions à partir des signaux de texte, de parole, d'expression faciale, de détection d'émotion ou d'électroencéphalographie (EEG). Parmi eux, la reconnaissance d'émotion à l'aide d'EEG a permis une précision satisfaisante. Cependant, le fait d'utiliser des dispositifs d'électroencéphalographie limite la gamme des mouvements de l'utilisateur. Une méthode non envahissante est donc nécessaire pour faciliter la détection des émotions et ses applications. C'est pourquoi nous avons proposé d'utiliser une caméra thermique pour capturer les changements de température de la peau, puis appliquer des algorithmes d'apprentissage machine pour classer les changements d'émotion en conséquence. Cette thèse contient deux études sur la détection d'émotion thermique avec la comparaison de la détection d'émotion basée sur EEG. L'un était de découvrir les profils de détection émotionnelle thermique en comparaison avec la technologie de détection d'émotion basée sur EEG; L'autre était de construire une application avec des algorithmes d'apprentissage en machine profonds pour visualiser la précision et la performance de la détection d'émotion thermique et basée sur EEG. Dans la première recherche, nous avons appliqué HMM dans la reconnaissance de l'émotion thermique, et après avoir comparé à la détection de l'émotion basée sur EEG, nous avons identifié les caractéristiques liées à l'émotion de la température de la peau en termes d'intensité et de rapidité. Dans la deuxième recherche, nous avons mis en place une application de détection d'émotion qui supporte à la fois la détection d'émotion thermique et la détection d'émotion basée sur EEG en appliquant les méthodes d'apprentissage par machine profondes - Réseau Neuronal Convolutif (CNN) et Mémoire à long court-terme (LSTM). La précision de la détection d'émotion basée sur l'image thermique a atteint 52,59% et la précision de la détection basée sur l'EEG a atteint 67,05%. Dans une autre étude, nous allons faire plus de recherches sur l'ajustement des algorithmes d'apprentissage machine pour améliorer la précision de détection d'émotion thermique.To improve computer-human interactions in the areas of healthcare, e-learning and video games, many researchers have studied on recognizing emotions from text, speech, facial expressions, emotion detection, or electroencephalography (EEG) signals. Among them, emotion recognition using EEG has achieved satisfying accuracy. However, wearing electroencephalography devices limits the range of user movement, thus a noninvasive method is required to facilitate the emotion detection and its applications. That’s why we proposed using thermal camera to capture the skin temperature changes and then applying machine learning algorithms to classify emotion changes accordingly. This thesis contains two studies on thermal emotion detection with the comparison of EEG-base emotion detection. One was to find out the thermal emotional detection profiles comparing with EEG-based emotion detection technology; the other was to implement an application with deep machine learning algorithms to visually display both thermal and EEG based emotion detection accuracy and performance. In the first research, we applied HMM in thermal emotion recognition, and after comparing with EEG-base emotion detection, we identified skin temperature emotion-related features in terms of intensity and rapidity. In the second research, we implemented an emotion detection application supporting both thermal emotion detection and EEG-based emotion detection with applying the deep machine learning methods – Convolutional Neutral Network (CNN) and LSTM (Long- Short Term Memory). The accuracy of thermal image based emotion detection achieved 52.59% and the accuracy of EEG based detection achieved 67.05%. In further study, we will do more research on adjusting machine learning algorithms to improve the thermal emotion detection precision

A method for sleep quality analysis based on CNN ensemble with implementation in a portable wireless device

The quality of sleep can be affected by the occurrence of a sleep related disorder and, among these disorders, obstructive sleep apnea is commonly undiagnosed. Polysomnography is considered to be the gold standard for sleep analysis. However, it is an expensive and labor-intensive exam that is unavailable to a large group of the world population. To address these issues, the main goal of this work was to develop an automatic scoring algorithm to analyze the single-lead electrocardiogram signal, performing a minute-by-minute and an overall estimation of both quality of sleep and obstructive sleep apnea. The method employs a cross-spectral coherence technique which produces a spectrographic image that fed three one-dimensional convolutional neural networks for the classification ensemble. The predicted quality of sleep was based on the electroencephalogram cyclic alternating pattern rate, a sleep stability metric. Two methods were developed to indirectly evaluate this metric, creating two sleep quality predictions that were combined with the sleep apnea diagnosis to achieve the final global sleep quality estimation. It was verified that the quality of sleep of the nineteen tested subjects was correctly identified by the proposed model, advocating the significance of clinical analysis. The model was implemented in a non-invasive and simple to self-assemble device, producing a tool that can estimate the quality of sleep and diagnose the obstructive sleep apnea at the patient’s home without requiring the attendance of a specialized technician. Therefore, increasing the accessibility of the population to sleep analysis.info:eu-repo/semantics/publishedVersio

An Empirical Study Comparing Unobtrusive Physiological Sensors for Stress Detection in Computer Work.

Several unobtrusive sensors have been tested in studies to capture physiological reactions to stress in workplace settings. Lab studies tend to focus on assessing sensors during a specific computer task, while in situ studies tend to offer a generalized view of sensors' efficacy for workplace stress monitoring, without discriminating different tasks. Given the variation in workplace computer activities, this study investigates the efficacy of unobtrusive sensors for stress measurement across a variety of tasks. We present a comparison of five physiological measurements obtained in a lab experiment, where participants completed six different computer tasks, while we measured their stress levels using a chest-band (ECG, respiration), a wristband (PPG and EDA), and an emerging thermal imaging method (perinasal perspiration). We found that thermal imaging can detect increased stress for most participants across all tasks, while wrist and chest sensors were less generalizable across tasks and participants. We summarize the costs and benefits of each sensor stream, and show how some computer use scenarios present usability and reliability challenges for stress monitoring with certain physiological sensors. We provide recommendations for researchers and system builders for measuring stress with physiological sensors during workplace computer use

A portable wireless device for cyclic alternating pattern estimation from an EEG monopolar derivation

Quality of sleep can be assessed by analyzing the cyclic alternating pattern, a long-lasting periodic activity that is composed of two alternate electroencephalogram patterns, which is considered to be a marker of sleep instability. Experts usually score this pattern through a visual examination of each one-second epoch of an electroencephalogram signal, a repetitive and time-consuming task that is prone to errors. To address these issues, a home monitoring device was developed for automatic scoring of the cyclic alternating pattern by analyzing the signal from one electroencephalogram derivation. Three classifiers, specifically, two recurrent networks (long short-term memory and gated recurrent unit) and one one-dimension convolutional neural network, were developed and tested to determine which was more suitable for the cyclic alternating pattern phase’s classification. It was verified that the network based on the long short-term memory attained the best results with an average accuracy, sensitivity, specificity and area under the receiver operating characteristic curve of, respectively, 76%, 75%, 77% and 0.752. The classified epochs were then fed to a finite state machine to determine the cyclic alternating pattern cycles and the performance metrics were 76%, 71%, 84% and 0.778, respectively. The performance achieved is in the higher bound of the experts’ expected agreement range and considerably higher than the inter-scorer agreement of multiple experts, implying the usability of the device developed for clinical analysis.info:eu-repo/semantics/publishedVersio

Augmented Human Assistance (AHA)

Aging and sedentarism are two main challenges for social and health systems in modern societies. To face these challenges a new generation of ICT based solutions is being developed to promote active aging, prevent sedentarism and find new tools to support the large populations of patients that suffer chronic conditions as result of aging. Such solutions have the potential to transform healthcare by optimizing resource allocation, reducing costs, improving diagno ses and enabling novel therapies, thus increasing quality of life. The primary goal of the “AHA: Augmented Human Assistance” project is to de velop novel assistive technologies to promote exercise among the elderly and patients of motor disabilities. For exercise programs to be effective, it is essential that users and patients comply with the prescribed schedule and perform the ex ercises following established protocols. Until now this has been achieved by hu man monitoring in rehabilitation and therapy session, where the clinicians or therapists permanently accompany users or patient. In many cases, exercises are prescribed for home performance, in which case it is not possible to validate their execution. In this context, the AHA project is an integrative and cross-discipli nary approach of 4 Portuguese universities, the CMU, and 2 Portuguese industry partners, that combines innovation and fundamental research in the areas of hu man-computer interaction, robotics, serious games and physiological computing (see partner list in Appendix A). In the project, we capitalize on recent innova tions and aim at enriching the capabilities and range of application of assistive devices via the combination of (1) assistive robotics; (2) technologies that use well-understood motivational techniques to induce people to do their exercises in the first place, and to do them correctly and completely; (3) tailored and relevant guidance in regard to health care and social support and activities; and (4) tech nologies to self-monitoring and sharing of progress with health-care provider enabling clinicians to fine-tune the exercise regimen to suit the participant’s ac tual progress. We highlight the development of a set of exergames (serious games controlled by the movement of the user’s body limbs) specifically designed for the needs of the target population according to best practices in sports and human kinetics sciences. The games can be adapted to the limitations of the users (e.g. to play in a sitting position) so a large fraction of the population can benefit from them. The games can be executed with biofeedback provided from wearable sensors, to pro duce more controlled exercise benefits. The games can be played in multi-user settings, either in cooperative or competitive mode, to promote the social rela tions among players. The games contain regional motives to trigger memories from the past and other gamification techniques that keep the users involved in the exercise program. The games are projected in the environment through aug mented reality techniques that create a more immersive and engaging experience than conventional displays. Virtual coach techniques are able to monitor the cor rectness of the exercise and provide immediate guidance to the user, as well as providing reports for therapists. A socially assistive robot can play the role of the coach and provide an additional socio-cognitive dimension to the experience to complement the role of the therapist. A web service that records the users’ per formances and allows the authorized therapists to access and configure the exer cise program provides a valuable management tool for caregivers and clinical staff. It can also provide a social network for players, increasing adherence to the therapies. We have performed several end-user studies that validate the proposed ap proaches. Together, or in isolation, these solutions provide users, caregivers, health professionals and institutions, valuable tools for health promotion, disease monitoring and prevention.info:eu-repo/semantics/publishedVersio

Subjective and objective measures

One of the greatest challenges in the study of emotions and emotional states is their measurement. The techniques used to measure emotions depend essentially on the authors’ definition of the concept of emotion. Currently, two types of measures are used: subjective and objective. While subjective measures focus on assessing the conscious recognition of one’s own emotions, objective measures allow researchers to quantify and assess the conscious and unconscious emotional processes. In this sense, when the objective is to evaluate the emotional experience from the subjective point of view of an individual in relation to a given event, then subjective measures such as self-report should be used. In addition to this, when the objective is to evaluate the emotional experience at the most unconscious level of processes such as the physiological response, objective measures should be used. There are no better or worse measures, only measures that allow access to the same phenomenon from different points of view. The chapter’s main objective is to make a survey of the main measures of evaluation of the emotions and emotional states more relevant in the current scientific panorama.info:eu-repo/semantics/acceptedVersio