A hybrid P300-SSVEP brain-computer interface speller with a frequency enhanced row and column paradigm

• Published in: Frontiers in neuroscience Vol. 17; p. 1133933
• Main Authors: Bai, Xin, Li, Minglun, Qi, Shouliang, Ng, Anna Ching Mei, Ng, Tit, Qian, Wei
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Research Foundation 15.03.2023; Frontiers Media S.A
• Subjects: Brain; brain-computer interface; Computer applications; Correlation analysis; EEG; Electroencephalography; Event-related potentials; Experimental methods; Implants; machine learning; Neural coding; Neural decoding; Neuroscience; Research methodology; speller; Spelling; speller; electroencephalography; Neural decoding; machine learning; brain-computer interface
• ISSN: 1662-453X; 1662-4548; 1662-453X
• DOI: 10.3389/fnins.2023.1133933

This study proposes a new hybrid brain-computer interface (BCI) system to improve spelling accuracy and speed by stimulating P300 and steady-state visually evoked potential (SSVEP) in electroencephalography (EEG) signals. A frequency enhanced row and column (FERC) paradigm is proposed to incorporate the frequency coding into the row and column (RC) paradigm so that the P300 and SSVEP signals can be evoked simultaneously. A flicker (white-black) with a specific frequency from 6.0 to 11.5 Hz with an interval of 0.5 Hz is assigned to one row or column of a 6 × 6 layout, and the row/column flashes are carried out in a pseudorandom sequence. A wavelet and support vector machine (SVM) combination is adopted for P300 detection, an ensemble task-related component analysis (TRCA) method is used for SSVEP detection, and the two detection possibilities are fused using a weight control approach. The implemented BCI speller achieved an accuracy of 94.29% and an information transfer rate (ITR) of 28.64 bit/min averaged across 10 subjects during the online tests. An accuracy of 96.86% is obtained during the offline calibration tests, higher than that of only using P300 (75.29%) or SSVEP (89.13%). The SVM in P300 outperformed the previous linear discrimination classifier and its variants (61.90-72.22%), and the ensemble TRCA in SSVEP outperformed the canonical correlation analysis method (73.33%). The proposed hybrid FERC stimulus paradigm can improve the performance of the speller compared with the classical single stimulus paradigm. The implemented speller can achieve comparable accuracy and ITR to its state-of-the-art counterparts with advanced detection algorithms.