Cross-correlation task-related component analysis (xTRCA) for enhancing evoked and induced responses of event-related potentials

Abstract

We propose an analysis method that extracts trial-reproducible (i.e., recurring) event-related spatiotemporal EEG patterns by optimizing a spatial filter as well as trial timings of task-related components in the time domain simultaneously in a unified manner. Event-related responses are broadly categorized into evoked and induced responses, but those are analyzed commonly in the time and the time-frequency domain, respectively. To facilitate a comparison of evoked and induced responses, a unified method for analyzing both evoked and induced responses is desired. Here we propose a method of cross-correlation task-related component analysis (xTRCA) as an extension of our previous method. xTRCA constructs a linear spatial filter and then optimizes trial timings of single trials based on trial reproducibility as an objective function. The spatial filter enhances event-related responses, and the temporal optimization compensates trial-by-trial latencies that are inherent to ERPs. We first applied xTRCA to synthetic data of induced responses whose phases varied from trial to trial, and found that xTRCA could realign the induced responses by compensating the phase differences. We then demonstrated with mismatch negativity data that xTRCA enhanced the event-related-potential waveform observed at a single channel. Finally, a classification accuracy was improved when trial timings were optimized by xTRCA, suggesting a practical application of the method for a brain computer interface. We conclude that xTRCA provides a unified framework to analyze and enhance event-related evoked and induced responses in the time domain by objectively maximizing trial reproducibility.