Related papers: Frequency Superposition -- A Multi-Frequency Stimu…
Stimulated Brillouin scattering (SBS) is an important nonlinear optical effect which can both enable and impede optical processes in guided wave systems. Highly multi-mode excitation of fibers has been proposed as a novel route towards…
A new concept and an online prototype of haptic BCI paradigm are presented. Our main goal is to develop a new, alternative and low cost paradigm, with open-source hardware and software components. We also report results obtained with the…
Achieving fast and reliable temporal signal encoding is crucial for low-power, always-on systems. While current spike-based encoding algorithms rely on complex networks or precise timing references, simple and robust encoding models can be…
Implantable Brain-machine interfaces (BMIs) are promising for motor rehabilitation and mobility augmentation, and they demand accurate and energy-efficient algorithms. In this paper, we propose a novel spiking neural network (SNN) decoder…
Spiking neurons can perform spatiotemporal feature detection by nonlinear synaptic and dendritic integration of presynaptic spike patterns. Multicompartment models of non-linear dendrites and related neuromorphic circuit designs enable…
Ensuring energy-efficient design in neuromorphic computing systems necessitates a tailored architecture combined with algorithmic approaches. This manuscript focuses on enhancing brain-inspired perceptual computing machines through a novel…
In this article we present the results of our research related to the study of correlations between specific visual stimulation and the elicited brain's electro-physiological response collected by EEG sensors from a group of participants.…
Using a realistic model of activity dependent dynamical synapses and a standard integrate and fire neuron model we study, both analytically and numerically, the conditions in which a postsynaptic neuron efficiently detects temporal…
Time-frequency domain dual-path models have demonstrated strong performance and are widely used in source separation. Because their computational cost grows with the number of frequency bins, these models often use the band-split (BS)…
Inherent fuzzy entropy is an objective measurement of electroencephalography (EEG) complexity, reflecting the robustness of brain systems. In this study, we present a novel application of multi-scale relative inherent fuzzy entropy using…
Intelligent metasurfaces are one of the favorite technologies for integrating sixth-generation (6G) networks, especially the reconfigurable intelligent surface (RIS) that has been extensively researched in various applications. In this…
Objective: Brain networks have gained increasing recognition as potential biomarkers in mental health studies, but there are limited approaches that can leverage complex brain networks for accurate classification. Our goal is to develop a…
Bayesian optimal experimental design (BOED) seeks to maximize the expected information gain (EIG) of experiments. This requires a likelihood estimate, which in many settings is intractable. Simulation-based inference (SBI) provides powerful…
We propose a fusion approach that combines features from simultaneously recorded electroencephalographic (EEG) and magnetoencephalographic (MEG) signals to improve classification performances in motor imagery-based brain-computer interfaces…
Discrete audio tokens derived from self-supervised learning models have gained widespread usage in speech generation. However, current practice of directly utilizing audio tokens poses challenges for sequence modeling due to the length of…
Riemannian geometry has been applied to Brain Computer Interface (BCI) for brain signals classification yielding promising results. Studying electroencephalographic (EEG) signals from their associated covariance matrices allows a mitigation…
Spiking neural networks (SNNs) are gaining popularity in the computational simulation and artificial intelligence fields owing to their biological plausibility and computational efficiency. This paper explores the historical development of…
Environmental Sound Classification (ESC) is a rapidly evolving field that recently demonstrated the advantages of application of visual domain techniques to the audio-related tasks. Previous studies indicate that the domain-specific…
Spiking neural networks (SNNs) are brain-inspired mathematical models with the ability to process information in the form of spikes. SNNs are expected to provide not only new machine-learning algorithms, but also energy-efficient…
Joint utilization of multiple discrete frequency bands can enhance the accuracy of delay estimation. Although some unique challenges of multiband fusion, such as phase distortion, oscillation phenomena, and high-dimensional search, have…