Tack vare framsteg inom portabel elektroencefalografiteknologi (PEEGT) har det blivit lättare och billigare att studera hjärnans aktivitet i form av hjärnvågor. Syftet med det här arbetet var att studera skillnaden i hjärnaktivitet mellan musikaliska aktiviteter, genom att använda sig av PEEGT. Verktyget som användes till ändamålet är utvecklat av företaget EMOTIV och heter ”Insight”. Att använda sig av PEEGT för att uppskatta känslor och sinnestillstånd är ett relativt nytt forskningsområde med en lovande framtid. Det här arbetet motiveras av möjligheten att använda sig av sådan hjärnforskning för att leta efter svar på frågor kring undervisning och musicerande. Till studien valdes de två musikaliska aktiviteterna improvisation och avistaläsning. EMOTIVs Insight användes sedan till att samla data vid 10 olika tillfällen, som därefter analyserades med ett Wilcoxon signed rank test. Den kvantitativa datan triangulerades med anteckningar från en loggbok om subjektiva upplevelser vid de 10 mättillfällena. Resultatet visade ingen statistiskt signifikant skillnad mellan de musikaliska aktiviteterna. Som varandes ett självexperiment visade det sig ändå vara lärorikt för forskningssubjektet, framförallt vad gäller insikter om förhållandet mellan anteckningarna i loggboken och datan från EEG:en. Förhoppningsvis kan arbetet tjäna som inspiration till att göra liknande arbeten i framtiden. / Thanks to recent advances in the development of portable electroencephalografy technology (PEEGT), doing research on brain activity has become both cheaper and more convenient to do. The purpose of this study was to investigate the difference of the brain activity between musical activities, using the commercially available PEEGT “Insight”, developed by the tech company EMOTIV. The use of these PEEGT to estimate states of mind and emotions is a relatively new area of research, with a promising future. The study is motivated by the possibility to use research like this to address questions about education and musicmaking. Two musical activities were chosen for the study, improvisation and sight-reading. EMOTIVs Insight was used to collect data on 10 separate occasions, which later was analyzed with the Wilcoxon Signed Rank Test. The quantitative data was triangulated with notes from a logbook about the subjective experiences during the 10 occasions. No statistically significant difference was found between the activities. On the other hand, the study proved useful for the subject. Especially regarding insights on the relationship between the notes in the logbook, and the reported data from the EEG. Hopefully the work can also serve as an inspiration for future studies.

Today smartphones are everywhere and they ease the lives of millions of people every day. However there are people who, because of various reasons, are unable to receive the benefits of these devices because they are not able to interact with a smartphone in the intended way; using their hands. In this thesis we investigate an alternative method for interacting with a smartphone; using a commercially available electroencephalography (EEG) headset. EEG is a technique for measuring and recording brain activity, often through the use of sensors placed along the scalp of the user. We developed a prototype of a brain-computer interface (BCI) for use with android and the Emotiv Insight commercial EEG headset. The prototype allows the user to control an on-screen cursor in one dimension within an android application using the Emotiv Insight. We performed three case studies with one participant in each. The participants had no prior experience with EEG headsets or BCIs. We had them train to use the Emotiv Insight with our BCI prototype. After the training was completed they performed a series of tests in order to measure their ability to control an on-screen cursor in one dimension. Finally the participants filled out a questionnaire regarding their subjective experiences of using the Emotiv Insight. These case studies showed the inadequacies of the Emotiv Insight. All three participants had issues with training and using the headset. These issues are reflected in our tests, where 44 out of 45 attempts at moving the cursor to a specific area resulted in a failure. All participants also reported fatigue and headaches during the case studies. We also concluded that the Emotiv Insight provides a poor user experience because of fatigue in longer sessions and the amount of work needed to train the headset.

The Emotiv Insight is a commercially available, low-cost, mobile EEG device that is commonly overshadowed by its costlier counterpart, the Emotiv EPOC. The purpose of this report is to investigate if the Emotiv Insight is a suitable headset to be used as a controlling factor in conjunction with an Arduino microcontroller and various electrical components that are used towards the development of an open-sourced, affordable electric wheelchair with the primary goal of providing those who either do not have the financial resources or the physical capability to operate a traditional wheelchair due to their disability a viable option to improve their quality of life. All of the C++ code, STL files used to fabricate the 3d-printed components are uploaded to a GitHub repository as open sourced files to allow individuals with access to a 3d-printer to either build the open sourced wheelchair for their personal use, or refine the design to suit their needs.