Biology subjects

Esparza-Iaizzo, M.

Publications and source records attributed to Esparza-Iaizzo, M..

3 recordsLinked to original sources

bioRxiv2025

An artefact-resilient wide bandwidth bidirectional graphene neural interface

The ability to simultaneously record and modulate neural activity is critical for next-generation bidirectional neural interfaces aiming to enable adaptive neuromodulation therapies for neurological disorders. Active graphene transistor technologies are particularly promising for neural recordings, as they extend the ability to monitor brain activity at very low frequencies and support multiplexed operation for high-density neural interfaces. However, their limited charge injection capacity makes them unsuitable for stimulation. In this work, we present a bidirectional neural interface that combines nanoporous reduced graphene oxide (rGO) microelectrodes for high charge injection focal stimulation and graphene solution-gated field-effect transistors (gSGFETs) for brain activity monitoring, exploiting the advantages of both technologies in one single device. Using scalable cleanroom microfabrication techniques, we monolithically integrate these two graphene-based technologies into fully flexible probes. We evaluate the performance of the hybrid devices both in saline and in vivo, with a particular focus on transistor performance during stimulation. Our results demonstrate that the recording capability of this bidirectional neural interface, including the monitoring of infraslow and local field potential activity, is not compromised during stimulation. This work highlights the potential of this hybrid neural interface for both basic neurophysiological and clinical translation use.

neuroscience↗

bioRxiv2024

Automatic sleep scoring for real-time monitoring and stimulation in individuals with and without sleep apnea

Digital therapeutics, enabled by advanced machine learning algorithms and medical wearable devices, offer a promising approach to streamline diagnostics and improve access to healthcare. Within this framework, automatic sleep scoring can provide accurate and efficient sleep analysis from electrophysiological signals recorded with wearable sensors, such as electroencephalography (EEG). However, the optimal configuration and temporal dynamics of automatic sleep scoring systems remain unclear, especially concerning their performance across different population samples. This study systematically investigates the impact of electrode setup, temporal scope, and population characteristics on the performance of automatic sleep scoring algorithms. Utilizing a convolutional neural network (CNN) model, we analyzed various electrode configurations and temporal dynamics using datasets comprising both healthy participants and individuals with sleep apnea. Our findings reveal that sleep scoring based on a single frontal EEG channel demonstrates reliable congruency with human expert scorers, with minimal improvement observed with additional sensors. Moreover, we demonstrate that real-time sleep scoring can be achieved with comparable accuracy to offline methods, which rely on past and future information to classify a window of interest. Remarkably, a notable reduction in decoding accuracy is observed for individuals with sleep disorders compared to healthy participants, highlighting the challenges inherent in accurately assessing sleep stages in clinical populations. Digital solutions for automatic sleep scoring hold promise for facilitating timely diagnoses and personalized treatment plans, with applications extending beyond sleep analysis to include closed-loop neurostimulation interventions. Our findings provide valuable insights into the complexities of automatic sleep scoring and offer considerations for the development of effective and efficient sleep assessment tools in both clinical and research settings.

neuroscience↗

bioRxiv2022

Long-range alpha-synchronisation as control signal for BCI: A feasibility study

Shifts in spatial attention are associated with variations in alpha-band (, 8-14 Hz) activity, specifically in inter-hemispheric imbalance. The underlying mechanism is attributed to local -synchronisation, which regulates local inhibition of neural excitability, and fronto-parietal synchronisation reflecting long-range communication. The direction-specific nature of this neural correlate brings forward its potential as a control signal in brain-computer interfaces (BCI). In the present study, we explored whether long-range -synchronisation presents lateralised patterns dependent on voluntary attention orienting and whether these neural patterns can be picked up at a single-trial level to provide a control signal for active BCI. We collected electroencephalography (EEG) data from a cohort of healthy adults (n = 10) while performing a covert visuospatial attention (CVSA) task. The data shows a lateralised pattern of -band phase coupling between frontal and parieto-occipital regions after target presentation, replicating previous findings. This pattern, however, was not evident during the cue-to-target orienting interval, the ideal time window for BCI. Furthermore, decoding the direction of attention trial-by-trial from cue-locked synchronisation with support vector machines (SVM) was at chance-level. The present findings suggest EEG may not be capable of detecting long-range -synchronisation in attentional orienting on a single-trial basis and, thus, highlight the limitations of this metric as a reliable signal for BCI control. SIGNIFICANCE STATEMENTCognitive neuroscience advances should ideally have a real-world impact, with an obvious avenue for transference being BCI applications. The hope is to faithfully translate user-generated brain endogenous states into control signals to actuate devices. A paramount challenge for transfer is to move from group-level, multi-trial average approaches to single-trial level. Here, we evaluated the feasibility of single-trial estimation of phase synchrony across distant brain regions. Although many studies link attention to long-range synchrony modulation, this metric has never been used to control BCI. We present a first attempt of a synchrony-based BCI that, albeit unsuccessful, should help break new ground to map endogenous attention shifts to real-time control of brain-computer actuated systems.

neuroscience↗