Enhancing SSVEP Identification with Less Individual Calibration Data Using Periodically Repeated Component Analysis

 Spatial filtering and template matching-based methods are commonly used to identify the stimulus frequency from multichannel EEG signals in steady-state visually evoked potentials (SSVEP)-based brain-computer interfaces (BCIs). However, these methods require sufficient calibration data to obtain reliable spatial filters and SSVEP templates, and they underperform in SSVEP identification with small-sample-size calibration data, especially when a single trial of data is available for each stimulus frequency. In contrast to the state-of-the-art task-related component analysis (TRCA)-based methods, which construct spatial filters and SSVEP templates based on the inter-trial task-related components in SSVEP, this study proposes a method called periodically repeated component analysis (PRCA), which constructs spatial filters to maximize the reproducibility across periods and constructs synthetic SSVEP templates by replicating the periodically repeated components (PRCs). We also introduced PRCs into two improved variants of TRCA. Performance evaluation was conducted using a self-collected 16-target dataset and a public 40-target dataset. The proposed methods show significant improvements with less training data and can achieve comparable performance to the baseline methods with 5 trials by using 2 or 3 training trials. Using a single trial of calibration data for each frequency, the PRCA-based methods achieved the highest average accuracies of over 95% and 90% with a 1-s data length and maximum average information transfer rates of 198.8±57.3 bits/min and 191.2±48.1 bits/min for the two data sets, respectively. Our results demonstrate the effectiveness and robustness of PRCA-based methods for SSVEP identification with reduced calibration effort and suggest its potential for practical applications of SSVEP-BCIs.