Bridging the gap between EEG and DCNNs reveals a fatigue mechanism of facial repetition suppression

Lu, Zitong; Ku, Yixuan

doi:10.1016/j.isci.2023.108501

Cited by 4 publications

(3 citation statements)

References 81 publications

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“…Moreover, our handbook still lacks coverage of many topics that may be of interest, such as brain connectivity analysis based on resting-state EEG, [22][23][24] image reconstruction from EEG data, [25][26][27][28][29] and research combining artificial neural networks with EEG data. [16,17,[30][31][32] We will add more content to the handbook to make this tutorial more comprehensive. We encourage users to suggest new analysis methods and algorithms via email or by submitting Issues via our GitHub project page.…”

Section: Discussionmentioning

confidence: 99%

“…We hope that everyone will learn how to conduct these analyses and understand why they should be done by exploring more literature. Moreover, our handbook still lacks coverage of many topics that may be of interest, such as brain connectivity analysis based on resting‐state EEG, [22–24] image reconstruction from EEG data, [25–29] and research combining artificial neural networks with EEG data [16,17,30–32] . We will add more content to the handbook to make this tutorial more comprehensive.…”

Section: Discussionmentioning

confidence: 99%

“…The section on advanced EEG analysis comprises the following four parts: batch processing for reading and storing demo data, classification-based decoding, [8,[15][16][17] RSA, [5,9,10,18,19] and IEM [14,20,21] (see GitHub website, Chapter 4: Advanced EEG Analysis).…”

Section: Advanced Eeg Analysismentioning

confidence: 99%

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An easy‐to‐follow handbook for electroencephalogram data analysis with Python

Lu,

Li,

Nie

et al. 2024

Brain-X

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This easy‐to‐follow handbook offers a straightforward guide to electroencephalogram (EEG) analysis using Python, aimed at all EEG researchers in cognitive neuroscience and related fields. It spans from single‐subject data preprocessing to advanced multisubject analyses. This handbook contains four chapters: Preprocessing Single‐Subject Data, Basic Python Data Operations, Multiple‐Subject Analysis, and Advanced EEG Analysis. The Preprocessing Single‐Subject Data chapter provides a standardized procedure for single‐subject EEG data preprocessing, primarily using the MNE‐Python package. The Basic Python Data Operations chapter introduces essential Python operations for EEG data handling, including data reading, storage, and statistical analysis. The Multiple‐Subject Analysis chapter guides readers on performing event‐related potential and time‐frequency analyses and visualizing outcomes through examples from a face perception task dataset. The Advanced EEG Analysis chapter explores three advanced analysis methodologies, Classification‐based decoding, Representational Similarity Analysis, and Inverted Encoding Model, through practical examples from a visual working memory task dataset using NeuroRA and other powerful packages. We designed our handbook for easy comprehension to be an essential tool for anyone delving into EEG data analysis with Python (GitHub website: https://github.com/ZitongLu1996/Python‐EEG‐Handbook; For Chinese version: https://github.com/ZitongLu1996/Python‐EEG‐Handbook‐CN).

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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An easy‐to‐follow handbook for electroencephalogram data analysis with Python

Lu,

Li,

Nie

et al. 2024

Brain-X

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Human EEG and artificial neural networks reveal disentangled representations of object real-world size in natural images

Lu,

Golomb

2023

Preprint

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Human brains have the ability to accurately perceive and process the real-world size of objects, despite vast differences in distance and perspective, which is a remarkable feat of cognitive processing. However, previous studies faced challenges distinguishing perceived real-world size from perceived real-world depth, muddying result interpretations. We disentangled representations of real-world size from visual size and perceived real-world depth in human brains and artificial neural networks using the THINGS EEG2 dataset, which offers more ecologically valid naturalistic stimuli compared to the use of object stimuli without backgrounds. Our EEG representational similarity results revealed a pure representation of object real-world size in human brains. A visual object processing timeline was uncovered: pixel-wise differences appeared first, then real-world depth and visual size, and finally, real-world size. Furthermore, representational comparisons with different artificial neural networks reveal real-world size as a stable and higher-level dimension in object space incorporating both visual and semantic information.

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Human EEG and artificial neural networks reveal disentangled representations of object real-world size in natural images

Lu,

Golomb

2024

Preprint

View full text Add to dashboard Cite

Remarkably, human brains have the ability to accurately perceive and process the real-world size of objects, despite vast differences in distance and perspective. While previous studies have delved into this phenomenon, distinguishing this ability from other visual perceptions, like depth, has been challenging. Using the THINGS EEG2 dataset with high time-resolution human brain recordings and more ecologically valid naturalistic stimuli, our study uses an innovative approach to disentangle neural representations of object real-world size from retinal size and perceived real-world depth in a way that was not previously possible. Leveraging this state-of-the-art dataset, our EEG representational similarity results reveal a pure representation of object real-world size in human brains. We report a representational timeline of visual object processing: object real-world depth appeared first, then retinal size, and finally, real-world size. Additionally, we input both these naturalistic images and object-only images without natural background into artificial neural networks. Consistent with the human EEG findings, we also successfully disentangled representation of object real-world size from retinal size and real-world depth in all three types of artificial neural networks (visual-only ResNet, visual-language CLIP, and language-only Word2Vec). Moreover, our multi-modal representational comparison framework across human EEG and artificial neural networks reveals real-world size as a stable and higher-level dimension in object space incorporating both visual and semantic information. Our research provides a detailed and clear characterization of the object processing process, which offers further advances and insights into our understanding of object space and the construction of more brain-like visual models.

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Bridging the gap between EEG and DCNNs reveals a fatigue mechanism of facial repetition suppression

Cited by 4 publications

References 81 publications

An easy‐to‐follow handbook for electroencephalogram data analysis with Python

An easy‐to‐follow handbook for electroencephalogram data analysis with Python

Human EEG and artificial neural networks reveal disentangled representations of object real-world size in natural images

Human EEG and artificial neural networks reveal disentangled representations of object real-world size in natural images

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