Evaluating a Novel P300-Based Real-Time Image Ranking BCI - Sutaj, Ngadhnjim; Walchshofer, Martin; Schreiner, Leonhard; Turchet, Luca; Pretl, Harald; Guger, Christoph (Frontiers in Computer Science, 2021)
Brain-computer interfaces (BCI) establish communication between a human brain and a computer or external devices by translating the electroencephalography (EEG) signal into computer commands. After stimulating a sensory organ, a positive deflection of the EEG signal between 250 and 700 ms can be measured. This signal component of the event-related-potential (ERP) is called “P300”. Numerous studies have provided evidence that P300 amplitude and latency are linked to sensory perception, engagement, and cognition. Combining advances in technology, classification methods, and signal processing, we developed a novel image ranking system called Unicorn Blondy Check. In this study, the application was tested on 21 subjects using three different visual oddball paradigms. Two consisted of female faces and gray-scale images, while the third test paradigm consisted of familiar and unfamiliar faces. Images were displayed for a duration of 150ms in a randomized order. The system was trained using 50 trials and tested with 30 trials. The EEG data were acquired using the Unicorn Hybrid Black 8 channel BCI system. These synchronized recordings were analyzed, and the achieved classification accuracies were calculated. The EEG signal was averaged over all participants and for every paradigm separately. Analysis of the EEG data revealed a significant shift in P300 latency dependent on the paradigm and decreased amplitude for a lower target to non-target ratio. The image ranking application achieved a mean accuracy of 100% and 95.5% for ranking female faces above gray-scale images with ratios of 1:11 and 5:11, respectively. In the case of four familiar faces to 24 unfamiliar faces, 86.4% was reached. The obtained results illustrate this novel system’s functionality due to accuracies above chance levels for all subjects.