Biology subjects

Makarova, A.

Publications and source records attributed to Makarova, A..

4 recordsLinked to original sources

bioRxiv2026

Neuronal avalanches in awake macaque V1 and V4

Neuronal avalanches are cascades of synchronised activity whose sizes follow power law. Analyses of avalanches shed light on cortical dynamics operating near a critical point. In awake monkeys, avalanches were found in motor and premotor cortex, where they are invariant to temporal binning, detection threshold and the number of electrodes employed. Whether awake primate early visual cortex, which receives strong structured afferent drive, shares these properties has not been tested. We analyzed a public 1,024-channel resting-state dataset from V1 and V4 of two macaques. In both animals avalanche sizes followed power laws over more than 10-fold range, the exponent scaled with bin width as alpha proportional to delta-t to the power beta (beta = -0.045 and -0.031), and the distributions ended at a cutoff proportional to the number of electrodes analysed. Raising the detection threshold from 1.5 to 4.5 standard deviations (SDs) removed over 99% of events but changed alpha by only 0.02-0.05 per SD, whereas randomly removing the same events destroyed the power law. Per-electrode shuffling abolished all heavy tails. Eye closure shifted alpha, beta and the branching ratio by less than predefined equivalence bounds, far below the reproducible differences between recording sites. Avalanche organisation therefore extends to awake early visual cortex.

neuroscience↗

bioRxiv2026

DualMyo: Multi-Channel Dual-Stream Transformer Architecture for EMG-to-Digit Classification

Surface electromyography (sEMG) is a robust non-invasive modality for human-machine interaction, yet its application remains largely limited to coarse motor tasks such as grasping or rotation. The decoding of fine motor skills, specifically handwriting, remains a challenging problem with potential relevance for prosthetic control and natural communication interfaces. In this work, we explore a Transformer-based alternative to classical signal-processing pipelines that treats multi-channel sEMG signals as complex time series. We introduce DualMyo, a specialized model integrating Patch Embeddings and Rotary Positional Embeddings (RoPE) to capture the intricate spatio-temporal dynamics of myoelectric activity. Our experimental results show strong intra-session performance. Furthermore, we address the inherent challenges of signal drift and sensor displacement in cross-session applications. We show that a lightweight fine-tuning strategy of 10 epochs enables DualMyo to effectively adapt to session variability, achieving approximately 91\% accuracy with two examples per digit. These findings provide a promising step toward adaptive sEMG-based handwriting interfaces, although further validation is required for real-time and multi-subject deployment and neuromuscular control.

neuroscience↗

bioRxiv2026

Data aggregation strategies for a P300 speller: decoding models, epoch averaging, cross-subject ensembles, and multi-channel models

Accurate detection of P300 event-related potentials from electroencephalography (EEG) re-mains challenging for small numbers of trials due to low signal-to-noise ratios and substantial inter-subject variability. This study presents a systematic comparison of data aggregation strate-gies for improving P300 classification, evaluated on a 10-subject dataset using two convolutional neural network architectures (EEGNet and BaseCNN) and a support vector machine (SVM). We compared: (1) subject-specific and pooled general models for single trials; (2) epoch aver-aging with 5 and 10 stimuli repetitions; (3) multi-channel models where subjects corresponded to different input channels; (4) cross-subject averaging; (5) mixed (uncontrolled) averaging; (6) a combined approach with K trials per subject across all participants; and (7) time-shifted channels from extended single-trial epochs. Decoding performance was quantified using the Information Transfer Rate (ITR), computed for binary classification accuracy. We found that single-trial ITR was unpractical (0.15-0.64 bits/trial), whereas controlled aggregation improved the performance. The combined cross-subject approach with K = 3 trials per participant (30 channels) achieves the highest ITR with multi-channel EEGNet: 0.95 bits/aggregated decision in the no-aperture recordings and 0.97 bits/aggregated decision on Aperture data, approaching the theoretical binary-classification limit for the aggregated decision. Controlled cross-subject averaging consistently outperformed random trial mixing, and multi-channel architectures out-performed simple averaging when inter-subject structure was preserved. These findings con-tribute to improving P300 decoding and implementing multi-subject brain-computer interfaces (BCIs).

neuroscience↗

bioRxiv2022

Speech decoding from a small set of spatially segregated minimally invasive intracranial EEG electrodes with a compact and interpretable neural network

AO_SCPLOWBSTRACTC_SCPLOWO_ST_ABSBackgroundC_ST_ABSSpeech decoding, one of the most intriguing BCI applications, opens up plentiful opportunities from rehabilitation of patients to direct and seamless communication between human species. Typical solutions rely on invasive recordings with a large number of distributed electrodes implanted through craniotomy. Here we explored the possibility of creating speech prosthesis in a minimally invasive setting with a small number of spatially segregated intracranial electrodes. MethodsWe collected one hour of data (from two sessions) in two patients implanted with invasive electrodes. We then used only the contacts that pertained to a single sEEG shaft or an ECoG stripe to decode neural activity into 26 words and one silence class. We employed a compact convolutional network-based architecture whose spatial and temporal filter weights allow for a physiologically plausible interpretation. ResultsWe achieved on average 55% accuracy using only 6 channels of data recorded with a single minimally invasive sEEG electrode in the first patient and 70% accuracy using only 8 channels of data recorded for a single ECoG strip in the second patient in classifying 26+1 overtly pronounced words. Our compact architecture did not require the use of pre-engineered features, learned fast and resulted in a stable, interpretable and physiologically meaningful decision rule successfully operating over a contiguous dataset collected during a different time interval than that used for training. Spatial characteristics of the pivotal neuronal populations corroborate with active and passive speech mapping results and exhibit the inverse space-frequency relationship characteristic of neural activity. Compared to other architectures our compact solution performed on par or better than those recently featured in neural speech decoding literature. ConclusionsWe showcase the possibility of building a speech prosthesis with a small number of electrodes and based on a compact feature engineering free decoder derived from a small amount of training data.

neuroscience↗