A subject-independent SSVEP-based BCI target detection system based on fuzzy ordering of EEG task-related components. (January 2023) - Tabanfar, Zahra; Ghassemi, Farnaz; Hassan Moradi, Mohammad
Highlights: SSVEP-related sources' temporal activities are estimated using the TRCA method. The criteria are defined based on the task protocol. The membership function is defined based on the SSVEP physiological knowledge. Using optimization to calculate the final weights of the membership function. Introducing the isolated groups to determine the target stimulus frequency. Abstract: BCI systems are recognized as a means of interaction between the brain and the computer. In this research, a subject-independent BCI target detection system based on SSVEP was developed using the information in the task-related components and their fuzzy memberships to the regarding task. To assess the proposed system, an online available SSVEP data with four stimulus frequencies of 5, 6, 7 and 8 Hz from five participants were used. First, the task-related components of the SSVEP data were extracted using the TRCA algorithm. Then the membership of each task-related component was determined to each stimulus frequency, and the components were ordered fuzzily based on these membership values. Finally, a new concept named " Isolated Group " was defined and the final data label was decided by the identification of the isolated groups. Based on the results, observation of the detected isolated groups, showed that the isolated groups' components were mostly related to the stimulus frequency, based on their FFT plots. The classification results obtained using Leave-One-Subject-Out method were 100 %, 79.17 %, 70.83 %, 70.83 % and 95.83 % (mean: 83.33 %) for five participants, respectively. In summary, the fuzzy ordering of task-related components was used to pick the components with the most important information for target detection. Although the detection of stimulus frequency in BCI applications has been widely studied, most current target detection systems require an intensive training process for every individual. In contrast, in this research's method, only a concise training process for determining criteria weights in terms of an optimization step, is needed.