An EEG- and ERP-based image ranking application

An electroencephalogram (EEG) is a measurement to record the electrical potentials in the brain, also referred to as brain activity. For some time now, researchers use this EEG signal to create a brain-computer interfaces (BCI), which allows users to manipulate the computer just with their thoughts. This application ranks images of a picture set via their elicit responses in the EEG signal. The goal of this thesis is to check the functionality of the image ranking application with 2 different BCI devices and optimize the application run for image ranking. The first part includes criteria about signal processing, e.g. sample rate. Here, the timing in which the EEG signal is sampled and sent to the application is analysed. After, the reliability of the recorded EEG signal the interstimulus interval (ISI) can be optimized. The ISI consist of three parameters: the time an image is displayed on the screen, the time between two images and the number of times each image (flashes) has to be shown. This three parameters have to be tuned in a way that the accuracy is increased and the time for one application run is decreased. Additionally, a ranking with different subjects should be created to depict if certain images are always ranked in the first few positions and are independent of the subject. The results show that the interaction between application and the two BCI devices work as expected, short of some minor issues. Thus, the applicability of the application was given and the tuning of the ISI parameters could be started. For the ISI the best accuracy was achieved when the images were displayed on the screen for 100 ms and 75 ms was used between two images. Furthermore, each image was flashed 20 or 5 times for the classifier or ranking, respectively. For the group rankings, the results indicated, that images of faces rank in average higher than the rest. Finally, the median of all rankings for one image should be used as the ranking parameter.