Biology subjects

Kleeva, D. F.

Publications and source records attributed to Kleeva, D. F..

4 recordsLinked to original sources

bioRxiv2025

Connecting a P300 speller to a large language model

The advent of large-language models (LLMs) offers a transformative approach for improving the performance of brain-computer interface (BCI) spellers. We propose a novel framework that leverages the contextual understanding of LLMs to compensate for imperfect BCI decoding. Using existing P300 speller data, we simulated a system where users select letters to form words, generating text with characteristic spelling errors. This output is then processed by an LLM, which corrects the errors - a task that becomes more effective when the model considers full-sentence context. Our findings suggest that this synergy can accelerate communication rates by relaxing the need for high single-character accuracy. Beyond speed, integrating an LLM transforms the BCI into an intelligent agent, capable of acting as a discussant and assistant, thereby enriching the user experience.

neuroscience↗

bioRxiv2025

Beyond Traditional Poincare Analysis: Second-Order Plots Reveal Respiratory Effects in Heart Rate Variability

Heart rate variability (HRV) is a non-invasive biomarker of autonomic nervous system activity, commonly analyzed using a Poincare plot. This plot visualizes correlations between successive heartbeats (RRi vs. RRi+1) and quantifies autonomic regulation through SD1 and SD2 parameters. We introduce a second-order Poincare plot, a natural extension that clarifies serial dependencies by plotting successive differences in RR intervals ({Delta}RRi vs. {Delta}RRi+1). Applied to a PhysioNet dataset of 20 healthy individuals, this technique filtered out the slow HRV baseline of traditional elliptical plots to reveal distinct higher-order dynamics. These included ring-shaped structures indicating cardiorespiratory synchronization. A coupled-oscillator model, developed to simulate respiratory modulation, confirmed that these patterns are dictated by the respiratory frequency to heart rate ratio: slower breathing produces positive serial correlations in {Delta}RR, while faster breathing induces negative ones. By visualizing serial dependencies that conventional HRV metrics miss, the second-order Poincare plot extends the classical analysis framework. This tool provides a refined method for uncovering subtle dynamical features in HRV across diverse physiological and clinical states. HighlightsO_LISecond-order Poincare plots, plotting successive differences of RR intervals ({Delta}RRi vs. {Delta}RRi+1), extend traditional Poincare analysis to reveal rapid HRV dynamics. C_LIO_LIIn a dataset of 20 healthy individuals, second-order plots filtered out slow HRV components, highlighting respiratory modulation. C_LIO_LIRing-shaped patterns in some participants indicated strong cardiorespiratory coupling, while others showed positive or negative serial correlations linked to breathing rate. C_LIO_LIA coupled-oscillator model confirmed that the ratio of respiratory to heart rate frequency determines serial correlation patterns. C_LIO_LIThis method offers a novel tool for analyzing HRV dynamics, with potential applications in physiological and clinical research. C_LI

physiology↗

bioRxiv2023

The olfactory-based neurofeedback of the EEG alpha rhythm

Neurofeedback (NFB) is a form of biofeedback that enables subjects to monitor and control their own brain activity. To communicate with the subject, modern NFB training methodologies utilize various signaling mechanisms, typically visual and/or auditory stimuli. Olfaction has not been explored yet as a way to deliver NFB. Here we developed an olfactory-based NFB system based on electroencephalographic (EEG) recordings in human participants. The system incorporates an EEG recording apparatus, a custom olfactory display for the automated delivery of Sniffin Sticks, and a Python application for the conversion of EEG rhythms into NFB signals and controlling behavioral tasks. We tested occipital alpha rhythm as the source of NFB. Fifteen healthy participants were randomly assigned to three groups: olfactory neurofeedback, auditory NFB, and mock-olfactory NFB. NFB training resulted in an increase of alpha power in the true NFB groups, but not in the mock NFB group where the alpha power decreased, probably because of fatigue and drowsiness. Based on these results, we conclude that olfactory NFB is feasible, and lay out a framework for its future development.

neuroscience↗

bioRxiv2022

EEG correlates of olfactory processing during an instructed-delay task

EEG correlates of olfaction are of fundamental and practical interest for many reasons. In the field of neural technologies, olfactory-based brain-computer interfaces (BCIs) represent an approach that could be useful for neurorehabilitation of anosmia, dysosmia and hyposmia. While the idea of a BCI that decodes neural responses to different odors and/or enables odor-based neurofeedback is appealing, the results of previous EEG investigations into the olfactory domain are rather inconsistent, particularly when non-primary processing of olfactory signals is concerned. Here we report the results from an EEG study where an olfaction-based instructed-delay task was implemented. We utilized an olfactory display and a sensor of respiration to present odors in a strictly controlled fashion. Spatio-spectral EEG properties were analyzed to assess the olfactory-related components. Under these experimental conditions, EEG components representing processing of olfactory information were found around channel C4, where changes were detected for the 12-Hz spectral component. Additionally, significant differences in EEG patterns were observed when responses to odors were compared to the responses to odorless stimuli. We conclude EEG recordings are suitable for detecting active processing of odors. As such they could be integrated in a BCI that strives to rehabilitate olfactory disabilities or uses odors for hedonistic purposes.

neuroscience↗