EEG-TCF2Net: A Novel Deep Interval Type-2 Fuzzy Model for Decoding SSVEP in Brain-Computer Interfaces

The Steady-State Visual Evoked Potential (SSVEP) is a robust method for creating a fast Brain-Computer Interface (BCI); however, the time window of Electroencephalography (EEG) trials has to be reduced to improve the BCI's speed. This reduction leads to a decrease in the Signal-to-noise ratio (SNR), making it more difficult to classify these signals accurately. Conversely, combining Fuzzy Neural Block (FNB) that includes Type-l Fuzzy (T1F) in deep learning architecture has improved classification accuracy over data obtained in noisy environments. However, T1F has limitations in accurately modeling uncertainty and handling complex systems compared to Interval Type-2 Fuzzy (IT2F), which is particularly suitable for applications where robustness, adaptability, and accuracy are crucial. In this work, we proposed a deep learning framework that integrates the FNB using IT2F called FNB- IT2F. It is included parallel to the linear and final layers to assess their effectiveness. Thus, this study presents a unification of EEG- TCNet-LSTM with FNB-IT2F, which we call EEG- TCNet-LSTM-FNB-IT2F (i.e. EEG- TCF2Net). Our results reported a maximum recog-nition accuracy of 51.0% to 76.5% using the proposed method of EEG- TCF2N et in a subject-independent classification across all 10 subjects for 0.2 to 0.5 s time window. Overall, including FNB- IT2F in this deep learning architecture outperformed those without it, as well as baseline methods such as Filter-Bank Canonical Correlation Analysis (FBCCA) [1] and Task-related component analysis (TRCA) [2].