ArticleFrontiers in neuroinformatics2020
NeuroRA: A Python Toolbox of Representational Analysis From Multi-Modal Neural Data.
Article in Frontiers in neuroinformatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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23 citing papers in PubMed.
- Achieving more human brain-like vision via human EEG representational alignment.Communications biology · 2026Article
- Decoding P300 as a shared neural mechanism for oddball target detection and working memory updating.iScience · 2026Article
- The Temporal Dynamics of the Labeling Algorithm During Natural Language Comprehension: Neural Evidence for Phrase Grammatical Type Generation.Neurobiology of language (Cambridge, Mass.) · 2026Article
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- An EEG Dataset of Exploring Navigation in Virtual Reality.Scientific data · 2025Article
- Teaching CORnet human fMRI representations for enhanced model-brain alignment.Cognitive neurodynamics · 2025Article
- Neurodynamic evidence reveals identity top-down influences emotional contagion of race.Cognitive neurodynamics · 2025Article
- Neural representation of mixed feelings during real-time processing of negative words in pun-humor.Communications biology · 2025Article
- Evaluating the temporal order of motor and auditory systems in speech production using intracranial EEG.Communications biology · 2025Article
- Unfolding spatiotemporal representations of 3D visual perception in the human brain.bioRxiv : the preprint server for biology · 2025Article
- Human EEG and artificial neural networks reveal disentangled representations and processing timelines of object real-world size and depth in natural images.bioRxiv : the preprint server for biology · 2025Article
- Decoding the Spatiotemporal Dynamics of Neural Response Similarity in Auditory Processing: A Multivariate Analysis Based on OPM-MEG.Human brain mapping · 2025Article
- Decoding in the Fourth Dimension: Classification of Temporal Patterns and Their Generalization Across Locations.Human brain mapping · 2025Article
- Neural Basis of Categorical Representations of Animal Body Silhouettes.Neuroscience bulletin · 2025Article
- Forming cognitive maps for abstract spaces: the roles of the human hippocampus and orbitofrontal cortex.Communications biology · 2024Article
- Article
- Bridging the gap between EEG and DCNNs reveals a fatigue mechanism of facial repetition suppression.iScience · 2023Article
- Representation of the inferred relationships in a map-like space.Human brain mapping · 2023Article
- Every individual makes a difference: A trinity derived from linking individual brain morphometry, connectivity and mentalising ability.Human brain mapping · 2023Article
- Making Sense of the Multiplicity and Dynamics of Navigational Codes in the Brain.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2022Review
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2 authors.
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Abstract
In studies of cognitive neuroscience, multivariate pattern analysis (MVPA) is widely used as it offers richer information than traditional univariate analysis. Representational similarity analysis (RSA), as one method of MVPA, has become an effective decoding method based on neural data by calculating the similarity between different representations in the brain under different conditions. Moreover, RSA is suitable for researchers to compare data from different modalities and even bridge data from different species. However, previous toolboxes have been made to fit specific datasets. Here, we develop NeuroRA, a novel and easy-to-use toolbox for representational analysis. Our toolbox aims at conducting cross-modal data analysis from multi-modal neural data (e.g., EEG, MEG, fNIRS, fMRI, and other sources of neruroelectrophysiological data), behavioral data, and computer-simulated data. Compared with previous software packages, our toolbox is more comprehensive and powerful. Using NeuroRA, users can not only calculate the representational dissimilarity matrix (RDM), which reflects the representational similarity among different task conditions and conduct a representational analysis among different RDMs to achieve a cross-modal comparison. Besides, users can calculate neural pattern similarity (NPS), spatiotemporal pattern similarity (STPS), and inter-subject correlation (ISC) with this toolbox. NeuroRA also provides users with functions performing statistical analysis, storage, and visualization of results. We introduce the structure, modules, features, and algorithms of NeuroRA in this paper, as well as examples applying the toolbox in published datasets.
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