English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

It is argued in [1] that [2] was able to classify EEG responses to visualstimuli solely because of the temporal correlation that exists in all EEG dataand the use of a block design. We here show that the main claim in [1] isdrastically overstated and their other analyses are seriously flawed by wrongmethodological choices. To validate our counter-claims, we evaluate theperformance of state-of-the-art methods on the dataset in [2] reaching about50% classification accuracy over 40 classes, lower than in [2], but stillsignificant. We then investigate the influence of EEG temporal correlation onclassification accuracy by testing the same models in two additionalexperimental settings: one that replicates [1]'s rapid-design experiment, andanother one that examines the data between blocks while subjects are shown ablank screen. In both cases, classification accuracy is at or near chance, incontrast to what [1] reports, indicating a negligible contribution of temporalcorrelation to classification accuracy. We, instead, are able to replicate theresults in [1] only when intentionally contaminating our data by inducing atemporal correlation. This suggests that what Li et al. [1] demonstrate is thattheir data are strongly contaminated by temporal correlation and lowsignal-to-noise ratio. We argue that the reason why Li et al. [1] observe suchhigh correlation in EEG data is their unconventional experimental design andsettings that violate the basic cognitive neuroscience design recommendations,first and foremost the one of limiting the experiments' duration, as insteaddone in [2]. Our analyses in this paper refute the claims of the "perils andpitfalls of block-design" in [1]. Finally, we conclude the paper by examining anumber of other oversimplistic statements, inconsistencies, misinterpretationof machine learning concepts, speculations and misleading claims in [1].

Correct block-design experiments mitigate temporal correlation bias in EEG classification