Author

Maliheh Miri

Yazd UniversityORCID 0000-0002-2278-362XUnclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Assay:electronic health records· 2 Approach:graph neural network· 2 Approach:statistical model· 2 Research Focus:clustering· 1 Research Focus:dimensionality reduction· 1 Research Focus:drug response / drug discovery· 1 Assay:electrophysiology· 1 Organism:human· 1

Frequent Co-Authors

Dimitri Van De VilleiD· 3 shared Hamid BehjatiD· 2 shared Hamid Saeedi‐SourckiD· 2 shared Vahid AbootalebiiD· 2 shared Anders S OlseniD· 1 shared Enrico AmicoiD· 1 shared Harry H Behjat· 1 shared

Activity by Year

2023-2026 · peak 2/yr

Co-Author Collaboration Network

7 top collaborators · 13 mutual co-author links

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3 shared papersNeuroscience

Dimitri Van De VilleORCID

Mutual co-authors in cluster

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3 papers

Recent·Most cited

NeurosciencebioRxivPreprintNov 4, 2026

Clustering instantaneous phase-locking states with a rank-two Bures-Wasserstein distance

Maliheh Miri, Harry H Behjat, Dimitri Van De Ville, Anders S Olsen

This paper introduces a method to cluster instantaneous phase-locking states using the Bures-Wasserstein distance, improving sensitivity to phase-lag size and spatial patterns in EEG data.

DOI 10.64898/2026.11.03.764018

electrophysiologystatistical model

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NeuroscienceBiomedical Signal Processing and ControlPeer-reviewedOct 6, 2023

Spectral Representation of EEG Data using Learned Graphs with Application to Motor Imagery Decoding

Maliheh Miri, Vahid Abootalebi, Hamid Saeedi‐Sourck…·Hamid Behjat

This paper introduces a method to transform EEG data into a spectral representation using learned graphs for motor imagery decoding, showing improved performance over existing methods.

24 citations

electronic health recordsgraph neural network

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NeuroscienceEuropean Signal Processing Conference (EUSIPCO)Peer-reviewedSep 4, 2023

Brain fingerprinting using EEG graph inference

Maliheh Miri, Vahid Abootalebi, Enrico Amico…·Hamid Behjat

This paper introduces a method to estimate brain graphs using graph signal processing and demonstrates its superior performance over correlation-based functional connectomes in individual characterization.

5 citations

humanelectronic health records

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