Abstract
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.
Highlights
A major issue in subject-level and group-level EEG analysis is intra-subject and inter-subject variability across trials and sessions that originates from both endogenous factors and exogenous factors[1]
The reproducibility of time series was evaluated both in the time and frequency domains, and the corresponding scalp maps were compared for group task-related component analysis (TRCA) (gTRCA) and spatio-spectral decomposition (SSD)
We report that gTRCA enhanced the trial reproducibility and the frequency responses in comparison with single-channel EEG recorded at Oz and spatio-spectral decomposition (SSD)
Summary
A major issue in subject-level and group-level EEG analysis is intra-subject and inter-subject variability across trials and sessions that originates from both endogenous factors and exogenous factors[1]. Inter-subject variability includes anatomical differences across subjects such as head shapes, skull conductivity, and patterns of brain gyrification (i.e., folding of the cerebral cortex), in which genetic differences play a primary role These factors of variability consist of effects of non-interest and conceal an effect of interest related to a task. The proposed method is an extension of TRCA to find a spatial filter that achieves trial reproducibility and similarity maximization across trials from a group of subjects, thereby referred to as group TRCA (gTRCA) We think it beneficial to extract reproducible components for the purposes of understanding a common mechanism in neural processing and of constructing robust brain-computer interfaces under variabilities inherent in EEG signals. We will demonstrate that gTRCA provides a predictive filter for a new subject with only one trial
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