Still an Ineffective Method With supertrials/ERPs-Comments on "decoding Brain Representations by Multimodal Learning of Neural Activity and Visual Features"

A recent paper claims that a newly proposed method classifies EEG data recorded from subjects viewing ImageNet stimuli better than two prior methods. However, the analysis used to support that claim is based on confounded data. We repeat the analysis on a large new dataset that is free from that con...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:IEEE transactions on pattern analysis and machine intelligence 2023-11, Vol.45 (11), p.1-3
Hauptverfasser: Bharadwaj, Hari M, Wilbur, Ronnie B, Siskind, Jeffrey Mark
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:A recent paper claims that a newly proposed method classifies EEG data recorded from subjects viewing ImageNet stimuli better than two prior methods. However, the analysis used to support that claim is based on confounded data. We repeat the analysis on a large new dataset that is free from that confound. Training and testing on aggregated supertrials derived by summing trials demonstrates that the two prior methods achieve statistically significant above-chance accuracy while the newly proposed method does not.
ISSN:0162-8828
1939-3539
1939-3539
2160-9292
DOI:10.1109/TPAMI.2023.3292062