Authors:

Aishwarya Rajan

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In the past decade, Multivariate Pattern Analysis (i.e., decoding) has become increasingly utilized in studies of neural processes. Identifying optimal methods in this domain thus has significant value in improving the sensitivity and power of future EEG neural decoding work. With this aim in mind, this study will investigate the influence of electrode count on the magnitude and reliability of neural decoding in electroencephalography (EEG) in the context of visual processing. Specifically, we will evaluate differences in the decoding of visual scene identity across five electrode montages, varying in number (64\32\16) and positioning of electrodes (distributed vs. high-density posterior). These montages will be created by subsetting 64-channel data recorded from 20 subjects viewing real-world scenes. We hypothesize that (1) the increase in spatial resolution from a higher number of electrodes, as well as (2) an increase in electrode density over posterior sites (which are more likely to record activity from the visual cortex), would enhance the reliability, and thus the observed effect size, of visual scene identity decoding from scalp-based EEG recordings. This investigation will provide valuable insights in identifying optimal balance points between reliable measurement and practical considerations such as cost, comfort, and setup time.  Evolving Minds: Contrasting Emotion Reasoning in Children and Adults Rachel Raju

Source:

UC Davis / Psychology / 2024

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Co-authors:

Aishwarya Rajan