BCI (Brain Computer Interface) för användning i spel har börjat dyka upp på konsument-marknaden. Dessa använder ofta EEG för att mäta spelarnas avslappningsnivåer och den spelare som lyckas nå högsta meditativa tillstånd samlar poäng. I detta arbete undersöks huruvida det går att använda BCI för mer direkt kontroll av en specifik spelmekanik i kombination med konventionella spelkontroller. För undersökningen har två versioner av ett spel skapats med avsikten att mäta skillnaden i hjärnaktiviteten hos spelare som bara spelar, jämfört med spelare som aktivt försöker åstadkomma ett specifikt sinnestillstånd för att direkt kontrollera en funktion i spelet. På grund av studiens begränsade omfattning kunde inga definitiva slutsatser dras men det finns indikationer på att MindWave, som var utrustningen som användes i denna studie, inte är lämplig som direkt kontrollmetod tillsammans med andra kontroller. Studien skulle kunna fungera som en pilotstudie för en mer omfattande undersökning inom ämnet.

Studien implementerade ett hjärna-dator-gränssnitt med hjälp av EEG-instrumentet MindWave Mobile Headset. Vi undersökte om det var möjligt att utföra fyra operationer genom att använda tankemönster. Fyra försökspersoner deltog i studien. Deras uppgift var att tänka i två tankemönster i följd som resulterade i en operation. EEG-signalen förbehandlas så att en mönsterigenkänningsmetod (k-NN) lättare kunde urskilja två tankemönster ur signalen. Denna undersökning har till vår vetskap inte tidigare utförts och är därmed kunskapsluckan vi ämnar fylla. Att fylla denna kunskapslucka är av intresse för bland annat användargrupperna: rörelsehindrade, spelintresserade och Virtual Reality-användare. Vi tog fram en modell som modellerade det bästa möjliga utfallet av metodiken i föreliggande studie. Undersökningens resultat kunde inte användas för att göra slutsatser angående frågeställningen då detta skulle vara att post hoc-teoretisera. I modellen visades dock tre av fyra operationer vara genomförbara, med en indikation om att även den fjärde var möjlig att utföra. Resultatet indikerar att det finns anledning att utföra en fortsatt studie. Den föreslagna fortsatta studien bör innefatta nya mätningar som testas av modellen för att fullt ut besvara problemformuleringen. / This study implements a Brain-Computer-Interface using the EEG-instrument MindWave Mobile Headset. We studied the feasibility of performing four operations using thought patterns. Four test subjects participated in the study. Their task was to think in two subsequent thought patterns that resulted in an operation. The EEG-signal was pre-processed in such a way that a pattern recognition algorithm (k-NN) more easily could recognize two thought patterns in the signal. This study has to our knowledge not been done before and thus aims to fill this lack of knowledge in the scientific community. User groups that have an interest in filling this gap are, amongst others; disabled people, gamers, and Virtual Reality users. We created a model that modeled the best possible outcome of the method used in this study. Conclusions drawn from the result can not be used to fully answer the problem statement, since it would be to post hoc-theorize. However, three out of four operations were possible to perform in the model, with an indication that the fourth also was possible to perform. These results indicate that there are grounds to continue this study. The proposed continued study should include new measurements that are tested by the model to determine if it is feasible to distinguish all four operations.

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