arXiv subjects

Nicolás J. Gallego-Molina

Publications and source records attributed to Nicolás J. Gallego-Molina.

3 recordsLinked to original sources

arXiv2026

EEG Entrainment to Non-Speech Rhythmic Patterns Associated with Dimensions of Language Performance

Developmental dyslexia (DLX) affects 5-12% of the global population and impairs learning to read. Its biological basis, particularly the difficulties observed in oral phonological tasks, remains debated. Entrainment, the coupling of brain oscillations to speech rhythms, is essential for encoding phonological representations. We investigated whether brain entrainment to non-speech rhythms could predict DLX in 48 participants listening to white noise amplitude-modulated at rates matching Spanish speech units. Phase-amplitude coupling (PAC) analysis of electroencephalography (EEG) signals yielded accurate DLX prediction for the 40 Hz stimulus using the Heights Ratio PAC measure (balanced accuracy 0.929, p = 0.0069, in the best case), indicating a stronger phonemic entrainment deficit in dyslexic individuals. Regression analysis in control (CTL) participants showed a strong dependence between reading performance scores and greater entrainment in language-associated brain regions. In particular, participants with poorer phonemic awareness, reading comprehension, or rapid automatized naming showed greater right-hemisphere entrainment to the 40 Hz stimulus. This finding adds evidence for the hemispheric compensatory mechanisms hypothesized in the literature. In conclusion, our methodology identifies dyslexia-specific entrainment patterns near language-associated brain regions, together with compensatory entrainment in right-hemisphere areas.

q-bio.NC↗

arXiv2026

DualFlexKAN: Dual-stage Kolmogorov-Arnold Networks with Independent Function Control

Multi-Layer Perceptrons (MLPs) rely on pre-defined, fixed activation functions, imposing a static inductive bias that forces the network to approximate complex topologies solely through increased depth and width. Kolmogorov-Arnold Networks (KANs) address this limitation through edge-centric learnable functions, yet their formulation suffers from quadratic parameter scaling and architectural rigidity that hinders the effective integration of standard regularization techniques. This paper introduces the DualFlexKAN (DFKAN), a flexible architecture featuring a dual-stage mechanism that independently controls pre-linear input transformations and post-linear output activations. This decoupling enables hybrid networks that optimize the trade-off between expressiveness and computational cost. Unlike standard formulations, DFKAN supports diverse basis function families, including orthogonal polynomials, B-splines, and radial basis functions, integrated with configurable regularization strategies that stabilize training dynamics. Comprehensive evaluations across regression benchmarks, physics-informed tasks, and function approximation demonstrate that DFKAN outperforms both MLPs and conventional KANs in accuracy, convergence speed, and gradient fidelity. The proposed hybrid configurations achieve superior performance with one to two orders of magnitude fewer parameters than standard KANs, effectively mitigating the parameter explosion problem while preserving KAN-style expressiveness. DFKAN provides a principled, scalable framework for incorporating adaptive non-linearities, proving particularly advantageous for data-efficient learning and interpretable function discovery in scientific applications.

cs.LG↗

arXiv2021

Complex network modelling of EEG band coupling in dyslexia: an exploratory analysis of auditory processing and diagnosis

Complex network analysis has an increasing relevance in the study of neurological disorders, enhancing the knowledge of brain's structural and functional organization. Network structure and efficiency reveal different brain states along with different ways of processing the information. This work is structured around the exploratory analysis of the brain processes involved in low-level auditory processing. A complex network analysis was performed on the basis of brain coupling obtained from Electroencephalography (EEG) data, while different auditory stimuli were presented to the subjects. This coupling is inferred from the Phase-Amplitude coupling (PAC) from different EEG electrodes to explore differences between controls and dyslexic subjects. Coupling data allows the construction of a graph, and then, graph theory is used to study the characteristics of the complex networks throughout time for controls and dyslexics. This results in a set of metrics including clustering coefficient, path length and small-worldness. From this, different characteristics linked to the temporal evolution of networks and coupling are pointed out for dyslexics. Our study revealed patterns related to Dyslexia as losing the small-world topology. Finally, these graph-based features are used to classify between controls and dyslexic subjects by means of a Support Vector Machine (SVM).

q-bio.NC↗