Submitted by Automa??o e Estat?stica (sst@bczm.ufrn.br) on 2017-10-02T22:27:19Z No. of bitstreams: 1 GabrielAlvesVasiljevicMendes_DISSERT.pdf: 3791566 bytes, checksum: e847396390a6b6ca2128eefd4423f561 (MD5) / Approved for entry into archive by Arlan Eloi Leite Silva (eloihistoriador@yahoo.com.br) on 2017-10-06T23:26:09Z (GMT) No. of bitstreams: 1 GabrielAlvesVasiljevicMendes_DISSERT.pdf: 3791566 bytes, checksum: e847396390a6b6ca2128eefd4423f561 (MD5) / Made available in DSpace on 2017-10-06T23:26:09Z (GMT). No. of bitstreams: 1 GabrielAlvesVasiljevicMendes_DISSERT.pdf: 3791566 bytes, checksum: e847396390a6b6ca2128eefd4423f561 (MD5) Previous issue date: 2017-07-31 / Conselho Nacional de Desenvolvimento Cient?fico e Tecnol?gico (CNPq) / Brain-computer interfaces (BCIs) are specialized systems that allow users to control a computer or a machine using their brain waves. BCI systems allow patients with severe physical impairments, such as those suffering from amyotrophic lateral sclerosis, cerebral palsy and locked-in syndrome, to communicate and regain physical movements with the help of specialized equipment. With the development of BCI technology in the second half of the 20th century and the advent of consumer-grade BCI devices in the late 2000s, brain-controlled systems started to find applications not only in the medical field, but in areas such as entertainment. One particular area that is gaining more evidence due to the arrival of consumer-grade devices is the field of computer games, which has become increasingly popular in BCI research as it allows for more user-friendly applications of BCI technology in both healthy and unhealthy users. However, numerous challenges are yet to be overcome in order to advance in this field, as the origins and mechanics of the brain waves and how they are affected by external stimuli are not yet fully understood. In this sense, a systematic literature review of BCI games based on consumer-grade technology was performed. Based on its results, two BCI games, one using attention and the other using meditation as control signals, were developed in order to investigate key aspects of player interaction: the influence of graphical elements on attention and control; the influence of auditory stimuli on meditation and work load; and the differences both in performance and multiplayer game experience, all in the context of neurofeedback-based BCI games.

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