Biology subjects

Miri, M.

Publications and source records attributed to Miri, M..

4 recordsLinked to original sources

bioRxiv2026

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

Instantaneous phase-locking (iPL) matrices, defined by the cosines of pairwise phase differences between band-limited signals, underpin widely used methods for time-resolved functional connectivity, including Leading Eigenvector Dynamics Analysis (LEiDA). Each iPL matrix has rank at most two and is fully characterized by its two nonzero eigenvalues and corresponding eigenvectors. Yet, existing methods retain different parts of this decomposition and compare them using distances that do not reflect the geometry of the underlying sample space. We show that weighting the two eigenvectors by the square roots of their eigenvalues recovers each channel's phase relative to the mean phase, and that optimal orthogonal Procrustes alignment of these weighted eigenvector pairs yields the Bures-Wasserstein distance between the corresponding iPL matrices. In EEG, where most channels oscillate nearly in phase, existing distances become insensitive to phase-lag size, whereas the Bures-Wasserstein distance preserves this sensitivity. In simulations, clustering with this distance recovered states differing in both lag size and spatial pattern that existing distances failed to distinguish.In resting-state EEG, it resolved distinct phase-gradient states that other methods merged into a single global in-phase state.

neuroscience↗

bioRxiv2023

Sparse Representation for High-dimensional Multiclass Microarray Data Classification

Sparse representation of signals has achieved satisfactory results in classification applications compared to the conventional methods. Microarray data, which are obtained from monitoring the expression levels of thousands of genes simultaneously, have very high dimensions in relation to the small number of samples. This has led to the weaknesses of state-of-the-art classifiers to cope with the microarray data classification problem. The ability of the sparse representation to represent the signals as a linear combination of a small number of training data and to provide a brief description of signals led to reducing computational complexity as well as increasing classification accuracy in many applications. Using all training samples in the dictionary imposes a high computational burden on the sparse coding stage of high dimensional data. Proposed solutions to solve this problem can be roughly divided into two categories: selection of a subset of training data using different criteria, or learning a concise dictionary. Another important factor in increasing the speed and accuracy of a sparse representation-based classifier is the algorithm which is used to solve the related {ell}1 -norm minimization problem. In this paper, different sparse representation-based classification methods are investigated in order to tackle the problem of 14-Tumors microarray data classification. Our experimental results show that good performances are obtained by selecting a subset of the original atoms and learning the associated dictionary. Also, using SL0 sparse coding algorithm increases speed, and in most cases, accuracy of the classifiers.

bioengineering↗

bioRxiv2023

Brain fingerprinting using EEG graph inference

Taking advantage of the human brain functional connectome as an individuals fingerprint has attracted great research in recent years. Conventionally, Pearson correlation between regional time-courses is used as a pairwise measure for each edge weight of the connectome. Building upon recent advances in graph signal processing, we propose here to estimate the graph structure as a whole by considering all time-courses at once. Using data from two publicly available datasets, we show the superior performance of such learned brain graphs over correlation-based functional connectomes in characterizing an individual.

neuroscience↗

bioRxiv2022

EEG-based Motor Imagery Decoding via Graph Signal Processing on Smooth Learned Graphs

Electroencephalography (EEG) data entail a complex spatiotemporal structure that reflects ongoing organization of brain activity. Characterization of the spatial patterns is an indispensable step in numerous EEG processing pipelines within the setting of brain-computer interface systems as well as cognitive neuroscience. We present an approach for transforming EEG data into a spectral representation by using the harmonic basis of a graph structure that is learned from the data. The harmonic basis is obtained by integrating principles from graph learning and graph signal processing (GSP). First, we learn subject-specific graphs from each subjects EEG data. Second, by eigendecomposition of the normalized Laplacian matrix of each subjects graph, an orthonormal basis is obtained onto which each EEG map can be decomposed, providing a spectral representation of the data. We show that energy of the EEG maps is strongly associated with low frequency components of the learned basis, reflecting the smooth topography of EEG maps as expected. As a proof-of-concept for this alternative view of EEG data, we consider the task of decoding two-class motor imagery (MI) data. To this aim, the spectral representations are first mapped into a discriminative subspace for differentiating two-class data using a projection matrix obtained by the Fukunaga-Koontz transform (FKT), providing a minimal subspace from which features are extracted. An SVM classifier is then trained and tested on the resulting features to differentiate MI classes. The proposed method is evaluated on Dataset IVa of the BCI Competition III and its performance is compared to using features extracted from a subject-specific functional connectivity matrix and four state-of-the-art alternative methods. Experimental results indicate the superiority of the proposed method over alternative approaches, reflecting the added benefit of i) decomposing EEG data using data-driven, subject-specific harmonic bases, and ii) accounting for class-specific temporal variations in spectral profiles via the FKT. The proposed method and results underline the importance of integrating spatial and temporal characteristics of EEG signals in extracting features that can more powerfully differentiate MI classes.

neuroscience↗