El texto completo de este trabajo no está disponible en el Repositorio Académico UPC por restricciones de la casa editorial donde ha sido publicado. / The present work proposes an algorithm to detect and identify the artifact signals produced by the concrete gestural actions of jaw clench and eyebrows raising in the electroencephalography (EEG) signal. Artifacts are signals that manifest in the EEG signal but do not come from the brain but from other sources such as flickering, electrical noise, muscle movements, breathing, and heartbeat. The proposed algorithm makes use of concepts and knowledge in the field of signal processing, such as signal energy, zero crossings, and block processing, to correctly classify the aforementioned artifact signals. The algorithm showed a 90% detection accuracy when evaluated in independent ten-second registers in which the gestural events of interest were induced, then the samples were processed, and the detection was performed. The detection and identification of these devices can be used as commands in a brain–computer interface (BCI) of various applications, such as games, control systems of some type of hardware of special benefit for disabled people, such as a chair wheel, a robot or mechanical arm, a computer pointer control interface, an Internet of things (IoT) control or some communication system. / Revisión por pares

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International Telemetering Conference Proceedings / October 28-31, 1996 / Town and Country Hotel and Convention Center, San Diego, California / A ground based intelligent agent and operations network is being created to handle all aspects of spacecraft command and control. This system will have the dual purpose of enabling cost efficient operation of a number of small satellites and serving as a flexible testbed for the validation of space system autonomy strategies. The system is currently being targeted to include over a dozen globally distributed amateur radio ground stations and access to nearly ten spacecraft. The use of distributed computing systems and virtual interaction schemes are significantly contributing to the creation of this system. The Internet is used to link the network's control centers and ground stations. In addition, a World Wide Web (WWW) based user and operator interface is being developed to permit high level goal specification of spacecraft experiments and actions. This paper will describe the operating network being developed, the use of the Internet as an integral part of the system's architecture, the design of the WWW interface, and the future development of the system.

The growing use of information and communication technologies (ICTs) in college campuses has dramatically increased the potential for multitasking among students who have to juggle classes, school assignments, work, and recreational activities. These students believe that they have become more efficient by performing two or more tasks simultaneously. The use of technology, however, has changed the student’s ability to focus and attend to what they need to learn. Research has shown that multitasking divides students’ attention, which could have a negative impact on their cognition and learning. The purpose of this study was to examine the effects of distractive multitasking on students’ attention and academic performance in a classroom setting. Several studies in cognitive psychology have focused on individuals’ divided attention between simultaneously occurring tasks. Such research has found that, because human attention and capacity to process information are selective and limited, a performance decrement often results when task performance requires divided attention. Distractive tasks are defined as tasks or activities for which cognitive resources are used to process information that is not related to the course material. Multitasking is defined as the engagement in individual tasks that are performed in succession through a process of context switching. Using a non-experimental, correlational research design, the researcher examined the effects of distractive multitasking, with computer devices, during classroom lectures, on students’ academic performance. This study used a monitoring system to capture data that reflected actual multitasking behaviors from students who used computers while attending real-time classroom lectures. The findings showed that there was no statistically significant relationship between the frequency of distractive multitasking (predictor variable) and academic performance (criterion variable), as measured by the midterm and final evaluation scores. The results did not support the hypothesis that distractive computer-based multitasking could have a negative impact on academic performance.

Objective: Brain-computer interface (BCI) technologies have been the subject of study for the past decades to help restore functions for people with severe motor disabilities and to improve their quality of life. BCI research can be generally categorized by control signals (invasive/non-invasive) or applications (e.g. neuroprosthetics/brain-actuated wheelchairs), and efforts have been devoted to better understand the characteristics and possible uses of brain signals. The purpose of this research is to explore the feasibility of a non-invasive BCI system with the combination of unique sensorimotor-rhythm (SMR) features. Specifically, a 2D virtual wheelchair control BCI is implemented to extend the application of previously designed 2D cursor control BCI, and the feasibility of the prototype is tested in electroencephalography (EEG) experiments; guidance on enhancing system performance is provided by a simulation incorporating intelligent control approaches under different EEG decoding accuracies; pattern recognition methods are explored to provide optimized classification results; and a hybrid BCI system is built to enhance the usability of the wheelchair BCI system. Methods: In the virtual wheelchair control study, a creative and user friendly control strategy was proposed, and a paradigm was designed in Matlab, providing a virtual environment for control experiments; five subjects performed physical/imagined left/right hand movements or non-control tasks to control the virtual wheelchair to move forward, turn left/right or stop; 2-step classification methods were employed and the performance was evaluated by hit rate and control time. Feature analysis and time-frequency analysis were conducted to examine the spatial, temporal and frequency properties of the utilized SMR features, i.e. event-related desynchronization (ERD) and post-movement event-related synchronization (ERS). The simulation incorporated intelligent control methods, and evaluated navigation and positioning performance with/without obstacles under different EEG decoding accuracies, to better guide optimization. Classification methods were explored considering different feature sets, tuned classifier parameters and the simulation results, and a recommendation was provided to the proposed system. In the steady state visual evoked potential (SSVEP) system for hybrid BCI study, a paradigm was designed, and an electric circuit system was built to provide visual stimulus, involving SSVEP as another type of signal being used to drive the EEG BCI system. Experiments were conducted and classification methods were explored to evaluate the system performance. Results: ERD was observed on both hemispheres during hand's movement or motor imagery; ERS was observed on the contralateral hemisphere after movement or motor imagery stopped; five subjects participated in the continuous 2D virtual wheelchair control study and 4 of them hit the target with 100% hit rate in their best set with motor imagery. The simulation results indicated that the average hit rate with 10 obstacles can get above 95% for pass-door tests and above 70% for positioning tests, with EEG decoding accuracies of 70% for Non-Idle signals and 80% for idle signals. Classification methods showed that with properly tuned parameters, an average of about 70%-80% decoding accuracy for all the classifiers could be reached, which reached the requirements set by the simulation test. Initial test on the SSVEP BCI system exhibited high classification accuracy, which may extend the usability of the wheelchair system to a larger population when finally combined with ERD/ERS BCI system. Conclusion: This research investigated the feasibility of using both ERD and ERS associated with natural hand's motor imagery, aiming to implement practical BCI systems for the end users in the rehabilitation stage. The simulation with intelligent controls provided guides and requirements for EEG decoding accuracies, based on which pattern recognition methods were explored; properly selected features and adjusted parameters enabled the classifiers to exhibit optimal performance, suitable for the proposed system. Finally, to enlarge the population for which the wheelchair BCI system could benefit for, a SSVEP system for hybrid BCI was designed and tested. These systems provide a non-invasive, practical approach for BCI users in controlling assistive devices such as a virtual wheelchair, in terms of ease of use, adequate speed, and sufficient control accuracy.

This study aims to explore whether human intentions to move or cease to move right and left hands can be decoded from spatiotemporal features in non-invasive electroencephalography (EEG) in order to control a discrete two-dimensional cursor movement for a potential multi-dimensional Brain-Computer interface (BCI). Five naïve subjects performed either sustaining or stopping a motor task with time locking to a predefined time window by using motor execution with physical movement or motor imagery. Spatial filtering, temporal filtering, feature selection and classification methods were explored. The performance of the proposed BCI was evaluated by both offline classification and online two-dimensional cursor control. Event-related desynchronization (ERD) and post-movement event-related synchronization (ERS) were observed on the contralateral hemisphere to the hand moved for both motor execution and motor imagery. Feature analysis showed that EEG beta band activity in the contralateral hemisphere over the motor cortex provided the best detection of either sustained or ceased movement of the right or left hand. The offline classification of four motor tasks (sustain or cease to move right or left hand) provided 10-fold cross-validation accuracy as high as 88% for motor execution and 73% for motor imagery. The subjects participating in experiments with physical movement were able to complete the online game with motor execution at the average accuracy of 85.5±4.65%; Subjects participating in motor imagery study also completed the game successfully. The proposed BCI provides a new practical multi-dimensional method by noninvasive EEG signal associated with human natural behavior, which does not need long-term training.

Advances in Human Computer Interface (HCI) have made this area of research important for improving the standard of living for people with disabilities. An eye blink system is presented to allow people with disabilities to control a standard computer mouse. This system is designed for people who are paralytic with no control over their arms, speech, and anyone who is restricted to only the control of eye and head movements. This system is based on infrared reflectivity to capture and analyze real time eye blink signal of the user. It uses simple economical hardware electronics to emulate the functionality of computer mouse click based on user eye blinks. Informal tests show that the system can successfully distinguish between voluntary and involuntary eye blinks and can emulate user mouse clicks. This interface offers an economical, non-invasive, hands-free, plug and play device that provides the disabled with flexibility to improve their quality of life.

The use of monitoring and intrusion detection tools are common in today's network security architecture. The combination of tools generates an abundance of data which can result in cognitive overload of those analyzing the data. ID analysts initially review alerts generated by intrusion detection systems to determine the validity of the alerts. Since a large number of alerts are false positives, analyzing the data can severely reduce the number of unnecessary and unproductive investigations. The problem remains that this process is resource intensive. To date, very little research has been done to clearly determine and document the process of intrusion detection. In order to rectify this problem, research was conducted which involved several phases. Fifteen individuals were selected to participate in a cognitive task analysis. The results of the cognitive task analysis were used to develop a prototype interface which was tested by the participants. A test of the participants' knowledge after the use of the prototype revealed an increase in both effectiveness and efficiency in analyzing alerts. Specifically, the findings revealed an increase in effectiveness as 72% of the participants made better determinations using the prototype interface. The results also showed an increase in efficiency when 72% of the participants analyzed and validated alerts in less time while using the prototype interface. These findings, based on empirical data, showed that the use of the task diagram and prototype interface helped to reduce the amount of time it previously took to analyze alerts generated by intrusion detection systems.

This research examined the application of hypervariate display principles to human-computer interfaces with the intent of reducing the cognitive load placed on the operator during high-intensity activity. This research extended the existing body of knowledge relevant to reducing the cognitive load using human-computer interfaces. Existing research has explored the application of techniques that, when used in isolation, contribute to a computer operator's understanding of the data or efficiency in execution of tasks. This research studied the collaborative use of proven display techniques to improve a computer operator's ability to understand large amounts of data more rapidly and react to that data more effectively. These techniques, including the display of multiple variables in a single window, use of preattentive factors in the display, and the severing of geospatial dependencies on data significantly contributed to the reduction of cognitive burdens placed on a user in environments that are typically overwhelming. Experiments performed on 18 volunteer participants conclusively proved that the hypervariate display improved the participants' ability to handle increased workload, comprehend complex situations quickly and completely, and efficiently respond to the situation in an effective manner. This research has significant value and broad application to user communities where computers are used to control high-intensity operations such as military and law enforcement environments.

This thesis focuses on assistive and rehabilitation technology for restoring the function of the hand. It presents three different approaches to assistive technology: one in the form of an orthosis, one in the form of a brain-computer interface combined with functional electrical stimulation and finally one totally aiming at rehabilitating the nervous system by restoring brain function using the concept of neuroplasticity. The thesis also includes an epidemiological study based on statistics from the Swedish Hospital Discharge Register and a review on different methods for assessment of hand function. A novel invention of an orthosis in form of a light weight glove, the SEM (Soft Extra Muscle) glove, is introduced and described in detail. The SEM glove is constructed for improving the grasping capability of a human independently of the particular task being performed. A key feature is that a controlling and strengthening effect is achieved without the need for an external mechanical structure in the form of an exoskeleton. The glove is activated by input from tactile sensors in its fingertips and palm. The sensors react when the applied force is larger than 0.2 N and feed a microcontroller of DC motors. These pull lines, which are attached to the fingers of the glove and thus work as artificial tendons. A clinical study on the feasibility of the SEM glove to improve hand function on a group of patients with varying degree of disability has been made. Assessments included passive and active range of finger motion, flexor muscle strength according to the Medical Research Council (MRC) 0-5 scale, grip strength using the Grippit hand dynamometer, fine motor skills according to the Nine Hole Peg test and hand function in common activities by use of the Sollerman test. Participants rated the potential benefit on a Visual Analogue Scale. A prototype for a system for combining BCI (Brain-Computer Interface) and FES (Functional Electrical Stimulation) is described. The system is intended to be used during the first period of recovery from a TBI (Traumatic Brain Injury) or stroke that have led to paresis in the hand, before deciding on a permanent system, thus allowing the patients to get a quick start on the motor relearning. The system contains EEG recording electrodes, a control unit and a power unit. Initially the patients will practice controlling the movement of a robotic hand and then move on to controlling pulses being sent to stimulus electrodes placed on the paretic muscle. An innovative electrophysiological device for rehabilitation of brain lesions is presented, consisting of a portable headset with electrodes on both sides adapted on the localization of treatment area. The purpose is to receive the outgoing signal from the healthy side of the brain and transfer that signal to the injured and surrounding area of the remote side, thereby having the potential to facilitate the reactivation of the injured brain tissue. The device consists of a control unit as well as a power unit to activate the circuit electronics for amplifying, filtering, AD-converting, multiplexing and switching the outgoing electric signals to the most optimal ingoing signal for treatment of the injured and surrounding area. / <p>QC 20130403</p>