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Principal component analysis (PCA) has been widely employed for dimensionality reduction prior to multivariate pattern classification (decoding) in EEG research. The goal of the present study was to provide an evaluation of the effectiveness of PCA on decoding accuracy (using support vector machines) across a broad range of experimental paradigms. We evaluated several different PCA variations, including group-based and subject-based component decomposition and the application of Varimax rotation or no rotation. We also varied the numbers of PCs that were retained for the decoding analysis. We evaluated the resulting decoding accuracy for seven common event-related potential components (N170, mismatch negativity, N2pc, P3b, N400, lateralized readiness potential, and error-related negativity). We also examined more challenging decoding tasks, including decoding of face identity, facial expression, stimulus location, and stimulus orientation. The datasets also varied in the number and density of electrode sites. Our findings indicated that none of the PCA approaches consistently improved decoding performance related to no PCA, and the application of PCA frequently reduced decoding performance. Researchers should therefore be cautious about using PCA prior to decoding EEG data from similar experimental paradigms, populations, and recording setups.

In this paper, we provide guidance for the organization and implementation of EEG studies. This work was inspired by our experience conducting a large-scale, multi-site study, but many elements could be applied to any EEG project. Section 1 focuses on study activities that take place before data collection begins. Topics covered include: establishing and training study teams, considerations for task design and piloting, setting up equipment and software, development of formal protocol documents, and planning communication strategy with all study team members. Section 2 focuses on what to do once data collection has already begun. Topics covered include: (1) how to effectively monitor and maintain EEG data quality, (2) how to ensure consistent implementation of experimental protocols, and (3) how to develop rigorous preprocessing procedures that are feasible for use in a large-scale study. Links to resources are also provided, including sample protocols, sample equipment and software tracking forms, sample code, and tutorial videos (to access resources, please visit: https://osf.io/wdrj3/).

The study of deaf users of signed languages, who often experience delays in primary language (L1) acquisition, permits a unique opportunity to examine the effects of aging on the processing of an L1 acquired under delayed or protracted development. A cohort of 107 congenitally deaf adult signers ages 45-85 years who were exposed to American Sign Language (ASL) either in infancy, early childhood, or late childhood were tested using an ASL sentence repetition test. Participants repeated 20 sentences that gradually increased in length and complexity. Logistic mixed-effects regression with the variables of chronological age (CA) and age of acquisition (AoA) was used to assess sentence repetition accuracy. Results showed that CA was a significant predictor, with increased age being associated with decreased likelihood to reproduce a sentence correctly (odds ratio [OR] = 0.56, p = .010). In addition, effects of AoA were observed. Relative to native deaf signers, those who acquired ASL in early childhood were less likely to successfully reproduce a sentence (OR = 0.42, p = .003), as were subjects who learned ASL in late childhood (OR = 0.27, p < .001). These data show that aging affects verbatim recall in deaf users of ASL and that the age of sign language acquisition has a significant and lasting effect on repetition ability, even after decades of sign language use. These data show evidence for life-span continuity of early life effects. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Deaf and hearing readers have different access to spoken phonology which may affect the representation and recognition of written words. We used ERPs to investigate how a matched sample of deaf and hearing adults (total n = 90) responded to lexical characteristics of 480 English words in a go/no-go lexical decision task. Results from mixed effect regression models showed (a) visual complexity produced small effects in opposing directions for deaf and hearing readers, (b) similar frequency effects, but shifted earlier for deaf readers, (c) more pronounced effects of orthographic neighbourhood density for hearing readers, and (d) more pronounced effects of concreteness for deaf readers. We suggest hearing readers have visual word representations that are more integrated with phonological representations, leading to larger lexically-mediated effects of neighbourhood density. Conversely, deaf readers weight other sources of information more heavily, leading to larger semantically-mediated effects and altered responses to low-level visual variables. [ABSTRACT FROM AUTHOR]

Principal component analysis (PCA) has been widely employed for dimensionality reduction prior to multivariate pattern classification (decoding) in EEG research. The goal of the present study was to provide an evaluation of the effectiveness of PCA on decoding accuracy (using support vector machines) across a broad range of experimental paradigms. We evaluated several different PCA variations, including group-based and subject-based component decomposition and the application of Varimax rotation or no rotation. We also varied the numbers of PCs that were retained for the decoding analysis. We evaluated the resulting decoding accuracy for seven common event-related potential components (N170, mismatch negativity, N2pc, P3b, N400, lateralized readiness potential, and error-related negativity). We also examined more challenging decoding tasks, including decoding of face identity, facial expression, stimulus location, and stimulus orientation. The datasets also varied in the number and density of electrode sites. Our findings indicated that none of the PCA approaches consistently improved decoding performance related to no PCA, and the application of PCA frequently reduced decoding performance. Researchers should therefore be cautious about using PCA prior to decoding EEG data from similar experimental paradigms, populations, and recording setups., Competing Interests: Declaration of competing interest None., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)