A large and rich EEG dataset for modeling human visual object recognition

Gifford, Alessandro Thomas; Dwivedi, Kshitij; Roig, Gemma; Cichy, Radoslaw Martin

doi:10.1101/2022.03.15.484473

Cited by 4 publications

(2 citation statements)

References 87 publications

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“…More broadly, THINGS-data reflects the core release of the THINGS initiative (https://things-initiative.org), a global initiative bringing together researchers around the world for multimodal and multispecies collection of neuroimaging, electrophysiological, and behavioral datasets based on THINGS objects. As part of the THINGS initiative, two electroencephalography (EEG) datasets have recently been made available 77,78 . In contrast to our temporally-spaced MEG dataset that offers non-overlapping and unobstructed responses to stimuli, these datasets used a rapid serial visual presentation design, which allows presenting more images in a shorter time window, yet which leads to a strong overlap in neighboring responses and interactions between stimuli that are known to affect high-level processing 79 .…”

Section: Discussionmentioning

confidence: 99%

THINGS-data: A multimodal collection of large-scale datasets for investigating object representations in human brain and behavior

Hebart

Contier

Teichmann

et al. 2022

Preprint

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Understanding object representations requires a broad, comprehensive sampling of the objects in our visual world with dense measurements of brain activity and behavior. Here we present THINGS-data, a multimodal collection of large-scale datasets comprising functional MRI, magnetoencephalographic recordings, and 4.70 million similarity judgments in response to thousands of photographic images for up to 1,854 object concepts. THINGS-data is unique in its breadth of richly-annotated objects, allowing for testing countless hypotheses at scale while assessing the reproducibility of previous findings. Beyond the unique insights promised by each individual dataset, the multimodality of THINGS-data allows combining datasets for a much broader view into object processing than previously possible. Our analyses demonstrate the high quality of the datasets and provide five examples of hypothesis-driven and data-driven applications. THINGS-data constitutes the core release of the THINGS initiative (https://things-initiative.org) for bridging the gap between disciplines and the advancement of cognitive neuroscience.

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Section: Discussionmentioning

confidence: 99%

THINGS-data: A multimodal collection of large-scale datasets for investigating object representations in human brain and behavior

Hebart

Contier

Teichmann

et al. 2022

Preprint

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“…For example, the THINGS database is accompanied by extensive sets of freely available neuroimaging and EEG data (e.g., Contier et al, 2021;Gifford, Dwivedi, Roig, & Cichy, 2022;Grootswagers et al, 2022), behavioral similarity judgments (Hebart, Zheng, Pereira & Baker, 2020), and memorability scores of the objects (Kramer, Hebart, Baker, & Bainbridge, 2022).…”

Section: Possible Applications Of Things and The Newly Collected Norm...mentioning

confidence: 99%

THINGSplus: New Norms and Metadata for the THINGS Database of 1,854 Object Concepts and 26,107 Natural Object Images

Stoinski¹,

Perkuhn²,

Hebart³

2022

Preprint

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To study visual object processing, the need for well-curated object concepts and images has grown significantly over the past years. To address this we have previously developed THINGS (Hebart et al., 2019), a large-scale database of 1,854 systematically sampled object concepts with 26,107 high-quality naturalistic images of these concepts. With THINGS+ we aim to extend THINGS by adding concept-specific and image-specific norms and metadata. Concept-specific norms were collected for all 1,854 object concepts for the object properties real-world size, manmadeness, preciousness, liveliness, heaviness, naturalness, ability to move, graspability, holdability, ability to be moved, pleasantness, and arousal. Further, we extended high-level categorization to 53 superordinate categories and collected typicality ratings for members of all 53 categories. Image-specific metadata includes measures of nameability and recognizability for objects in all 26,107 images. To this end, we asked participants to provide labels for prominent objects depicted in each of the 26,107 images and measured the alignment with the original object concept. Finally, to present example images in publications without copyright restrictions, we identified one new public domain image per object concept. In this study we demonstrate a high consistency of property (r = 0.92-0.99, M = 0.98, SD = 0.34) and typicality ratings (r = 0.88-0.98; M = 0.96, SD = 0.19), with arousal ratings as the only exception (r = 0.69). Correlations of our data with external norms were moderate to high for object properties (r = 0.44-0.95; M = 0.85, SD = 0.32) and typicality scores (r = 0.72-0.88; M = 0.79, SD = 0.18), again with the lowest validity for arousal (r = 0.30 - 0.52). To summarize, THINGS+ provides a broad, externally-validated extension to existing object norms and an important extension to THINGS as a general resource of object concepts, images, and category memberships. Our norms, metadata, and images provide a detailed selection of stimuli and control variables for a wide range of research interested in object processing and semantic memory.

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Generating realistic neurophysiological time series with denoising diffusion probabilistic models

Vetter,

Macke,

Gao

2023

Preprint

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In recent years, deep generative models have had a profound impact in engineering and sciences, revolutionizing domains such as image and audio generation, as well as advancing our ability to model scientific data. In particular, Denoising Diffusion Probabilistic Models (DDPMs) have been shown to accurately model time series as complex high-dimensional probability distributions. Experimental and clinical neuroscience also stand to benefit from this progress, since accurate modeling of neurophysiological time series, such as electroencephalography (EEG), electrocorticography (ECoG), and local field potential (LFP) recordings, and their synthetic generation can enable or improve a variety of neuroscience-specific applications. Here, we present a method for modeling multi-channel and densely sampled neurophysiological recordings using DDPMs, which can be flexibly applied to different recording modalities and experimental configurations. First, we show that DDPMs can generate realistic synthetic data for a variety of datasets including different recording techniques (LFP, ECoG, EEG) and species (rat, macaque, human). DDPM-generated time series accurately capture single- and multi-channel statistics such as frequency spectra and phase-amplitude coupling, as well as fine-grained and dataset-specific features such as sharp wave-ripples. In addition, synthetic time series can be generated based on additional information like experimental conditions or brain states. We demonstrate the utility and flexibility of DDPMs in several neuroscience-specific analyses, such as brain-state classification and imputation of missing channels to improve neural decoding. In summary, DDPMs can serve as accurate generative models of neurophysiological recordings, and have a broad utility in the probabilistic generation of synthetic time series for many applications relevant to neuroscience.

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A large and rich EEG dataset for modeling human visual object recognition

Cited by 4 publications

References 87 publications

THINGS-data: A multimodal collection of large-scale datasets for investigating object representations in human brain and behavior

THINGS-data: A multimodal collection of large-scale datasets for investigating object representations in human brain and behavior

THINGSplus: New Norms and Metadata for the THINGS Database of 1,854 Object Concepts and 26,107 Natural Object Images

Generating realistic neurophysiological time series with denoising diffusion probabilistic models

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