Group task-related component analysis (gTRCA): a multivariate method for inter-trial reproducibility and inter-subject similarity maximization for EEG data analysis

• Published in: Scientific reports Vol. 10; no. 1; p. 84
• Main Author: Tanaka, Hirokazu
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 09.01.2020; Nature Publishing Group
• Subjects: 631/378/116/2393; 631/378/116/2394; 639/166/985; Algorithms; Brain - physiology; Brain Mapping - methods; Brain-Computer Interfaces; Cognitive ability; Data analysis; EEG; Electroencephalography - methods; Evoked Potentials, Visual; Group Processes; Humanities and Social Sciences; Humans; Information processing; Interfaces; multidisciplinary; Occipital lobe; Reproducibility; Reproducibility of Results; Science; Science (multidisciplinary); Signal Processing, Computer-Assisted; Task Performance and Analysis; Visual evoked potentials
• DOI: 10.1038/s41598-019-56962-2
• ISSN: 2045-2322

EEG is known to contain considerable inter-trial and inter-subject variability, which poses a challenge in any group-level EEG analyses. A true experimental effect must be reproducible even with variabilities in trials, sessions, and subjects. Extracting components that are reproducible across trials and subjects benefits both understanding common mechanisms in neural processing of cognitive functions and building robust brain-computer interfaces. This study extends our previous method (task-related component analysis, TRCA) by maximizing not only trial-by-trial reproducibility within single subjects but also similarity across a group of subjects, hence referred to as group TRCA (gTRCA). The problem of maximizing reproducibility of time series across trials and subjects is formulated as a generalized eigenvalue problem. We applied gTRCA to EEG data recorded from 35 subjects during a steady-state visual-evoked potential (SSVEP) experiment. The results revealed: (1) The group-representative data computed by gTRCA showed higher and consistent spectral peaks than other conventional methods; (2) Scalp maps obtained by gTRCA showed estimated source locations consistently within the occipital lobe; And (3) the high-dimensional features extracted by gTRCA are consistently mapped to a low-dimensional space. We conclude that gTRCA offers a framework for group-level EEG data analysis and brain-computer interfaces alternative in complement to grand averaging.