Single-Trial Analysis

In modern neuroscience, accurate estimations of single-trial parameters in event-related brain responses have been set as a highly desirable goal, since the traditionally used across-trial averaging approach could lead to the loss of the information concerning across-trial variability of both phase-locked ERP and non-phase-locked ERS/ERD responses. In this chapter, we provided the technical details of single-trial analysis methods both in the time domain and the time-frequency domain to enhance the signal-to-noise ratio of event-related brain responses and estimate their single-trial parameters (e.g., latency and amplitude of ERP peaks, as well as latency, frequency, and magnitude of EEG oscillatory features). These methods included probabilistic independent component analysis and common spatial pattern for spatial filtering, continuous wavelet transform for time-frequency filtering, as well as multiple linear regression without/with a dispersion term for feature extraction. Finally, we emphasized the importance of single-trial analysis and discussed its promising applications in basic studies and clinical practice.

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