Optimizing filter-bank canonical correlation analysis for fast response SSVEP Brain-Computer Interface (BCI)

• المؤلفون الرئيسيون: Phyo Wai, Aung Aung, Guo, Heng, Chi, Ying, Zhang, Lei, Hua, Xian-Sheng, Guan, Cuantai
• مؤلفون آخرون: School of Computer Science and Engineering
• التنسيق: Conference or Workshop Item
• اللغة: English
• منشور في: 2021
• الموضوعات: Engineering::Computer science and engineering; Steady State Visual Evoked Potentials; Filter Banks
• الوصول للمادة أونلاين: https://hdl.handle.net/10356/147516
• المؤسسة: Nanyang Technological University

Steady-State Visual Evoked Potential (SSVEP) BCI brings high accuracy and consistent performance across subjects at the expense of a long stimulus presentation time window. Several recent methods exploited subject-specific features to improve SSVEP recognition performance in a short time window less than 1s. Although the calibration process is tedious and causes inconvenience, small calibration data with short duration resulting in higher performance gains are worth considering. So we propose a method by optimizing Filter-Bank Canonical Correlation Analysis (FBCCA) with subjects' calibrated templates, subject-specific weights and multiple reference types. The proposed method, subject-calibration extended FBCCA (SCEF) leverages independent and distinct discrimination characteristics of multiple references with subject-specific weight-adjusted features to improve SSVEP recognition performance. We tested the proposed method with different parameters compared with FBCCA baseline and state-of-the-art calibration methods on forty targets SSVEP dataset using 0.2s to 4s time windows. Our evaluation results show SCEF with three reference templates and subject-specific weighted features perform significantly better than all FBCCA variants in 0.2 s to 1 s time window (p < 0.001). SCEF performs marginally, not statistically significant, better than existing methods about 2.69 ± 2.32% mean accuracy across time windows. Including multiple templates and subject-specific weight increases 15.73 ± 5.34% and 8.06± 2.06% in mean accuracy resulting the overall performance improvements in short time window. The proposed optimization only requires prior calibration data to create subject-specific templates and weights instead of learning features from calibration data every time. This enables not requiring to repeat the calibration step in every SSVEP session for the same subject while still maintaining accuracy similar to state-of-the-art calibration methods.