Biology subjects

Bezsudnova, Y.

Publications and source records attributed to Bezsudnova, Y..

7 recordsLinked to original sources

bioRxiv2026

Modular Arrays for High Precision Wearable MEG

Optically pumped magnetometers (OPMs) can be used for magnetoencephalography (MEG) with equivalent or improved signal to noise ratio, relative to cryogenic MEG, when sensors are placed close to the scalp. OPM-based MEG can also be used in mobile contexts if sensors are placed in lightweight, wearable arrays. Individually tailored, rigid helmets known as scannercasts are currently the only method capable of achieving on-scalp, mobile recordings with high precision. However, these scannercasts are expensive to produce, require structural imaging in advance of the experiment, and can incur lengthy downtime while sensors are transferred between scannercasts. Here, we introduce a solution to these challenges that retains the advantages of scannercasts. We provide detailed steps for constructing a modular, cap-based design, suitable for all head sizes. Using simulations, we compare the leadfield power of this array against an idealised array and a commercially available mobile solution. We then validate our proposed solution empirically, in five participants, and provide a complete data preparation and analysis pipeline. Our design expands the accessibility of OPM-based MEG, and increases participant throughput to levels comparable to other imaging modalities. Crucially, it removes the trade-off between signal quality, mobility and practicality, promoting the unique potential of OPM-based MEG as a tool for studying naturalistic behaviour, and clinical assessment with high precision.

neuroscience↗

bioRxiv2026

Magnetoencephalography Without a Shielded Room

Magnetoencephalography (MEG) offers non-invasive neuroimaging with high temporal and spatial precision - but its adoption is hampered by the prohibitive cost and infrastructure burden of a magnetically shielded room. We have overcome that burden and present a lightweight, low-cost, multichannel magnetoencephalography system that can image brain activity without needing a magnetically shielded room. The multichannel nature of the system facilitates not just detection but also localization of brain signals that are over 300 million times smaller than environmental interference, without requiring passive shielding. Our system weighs less than 75kg, more than 100 times lighter than a typical shielded room. This is made possible through low-cost active shielding and software-based spatial filtering. We also show that the signal to noise ratio of our in-vivo recordings is comparable to what can be obtained from a conventional cryogenically-cooled MEG system sited within a shielded room. This demonstration is a crucial step towards democratizing magnetoencephalography and making it a globally accessible neuroimaging technology for healthcare and discovery research.

neuroscience↗

bioRxiv2025

Decoding with multivariate pattern analysis is superior for optically pumped magnetometer-based magnetoencephalography compared to superconducting quantum interference device-based systems

BackgroundMultivariate pattern analysis (MVPA) has become an increasingly important method for decoding distributed brain activity from neural electrophysiological recordings by leveraging both temporal and spatial features. These multivariate approaches have proven important for both cognitive neuroscience and brain-computer interfaces. MVPA might benefit from magnetoencephalography (MEG) systems based on optically pumped magnetometers (OPMs), as these sensors can be placed closer to the scalp, providing higher spatial resolution compared to conventional MEG systems that rely on superconducting quantum interference devices (SQUIDs). As OPM-based MEG systems become available at more institutions, it is essential to experimentally compare their performance with traditional SQUID-based systems using MVPA. MethodsWe adapted a visual object-word paradigm from a previous study, originally implemented on a TRIUX MEGIN SQUID system, to the FieldLine HEDscan OPM system. Participants were recruited and we recorded their ingoing brain activity while did the same task in both systems. Visual stimuli of different objects were presented alternately in two modalities: pictures and the corresponding written words. For each modality, MVPA was used to classify the objects from OPM and SQUID magnetometers data respectively. To further investigate the advantages of OPM, we evaluated the effect classification accuracy of two spatial factors by controlling the number of sensors included and the spatial frequency content of the sensor data. ResultsWe found higher time-resolved decoding accuracy for the OPM compared to the SQUID data. Moreover, OPMs show higher classification performance compared to SQUIDs when controlling for the same number of sensors; consistently, the OPM system required fewer sensors to reach the performance limit of the SQUID system. Our analysis considering the spatial frequency content of the signal revealed that decoding accuracy plateaued for the SQUID system at lower spatial frequencies while the performance of the OPM system continued to improve when higher-order spatial components were included. ConclusionOur OPM-MEG system outperformed the SQUID-MEG system on MVPA on decoding of visual processing. This advantage of OPM is driven by its higher spatial resolution, resulting from the sensors being positioned closer to the head and thus able to capture higher spatial frequency components of the brain signal. OPM may facilitate cognitive neuroscience research as well as brain-computer interfaces by providing higher sensitivity when employing paradigms using multi-variate data analysis.

neuroscience↗

bioRxiv2025

Neural Sharpening of Object Categories Through Parafoveal Priming

During natural vision, humans make saccades approximately every 250 - 300 ms to fixate on important objects in a scene. Given this rapid succession of eye movements, it has been proposed that visual processing begins in the parafovea, prior to fixation. Such parafoveal previewing may serve as a form of priming, facilitating subsequent processing at fixation. In this study, we investigated whether parafoveal priming leads to both a reduced neural response - akin to repetition suppression - and a more selective neural representation, consistent with neuronal sharpening. Participants performed a visual exploration task involving natural object images that were either parafoveally previewed or not, while magnetoencephalographic (MEG) data were recorded. Event-related fields revealed a reduced neural response when an image had been parafoveally presented 250 ms earlier. Moreover, multivariate pattern analysis showed enhanced decoding of object category following parafoveal priming, suggesting increased neural specificity. These findings support the idea that parafoveal previewing contributes to neural sharpening, potentially aiding evidence accumulation by forming sparse and precise representations of objects during visual scene exploration.

neuroscience↗

bioRxiv2023

Spatiotemporal properties of common semantic categories for words and pictures

The timing of semantic processing during object recognition in the brain is a topic of ongoing discussion. One way of addressing this question is by applying multivariate pattern analysis (MVPA) to human electrophysiological responses to object images of different semantic categories. However, while MVPA can reveal whether neuronal activity patterns are distinct for different stimulus categories, concerns remain on whether low-level visual features also contribute to the classification results. To circumvent this issue, we applied a cross-decoding approach to magnetoencephalography (MEG) data from stimuli from two different modalities: images and their corresponding written words. We employed items for three categories and presented them in a randomized order. We show that If the classifier is trained on words, pictures are classified between 150 - 430 ms from after stimulus onset, and when training on pictures, words are classified between 225 - 430 ms. The topographical map identified using a searchlight approach for cross-modal activation in both directions showed left lateralization confirming the involvement of linguistics representations. These results point to semantic activation of pictorial stimuli occurring at {approx}150 ms whereas for words the semantic activation occurs at {approx}230 ms.

neuroscience↗

bioRxiv2023

Optimizing magnetometers arrays and pre-processing pipelines for multivariate pattern analysis

BackgroundMultivariate pattern analysis (MVPA) has proven an excellent tool in cognitive neuroscience used M/EEG, and MRI. It also holds a strong promise when applied to optically-pumped magnetometer-based magnetoencephalography. New methodTo optimize OPM-MEG systems for MVPA experiments this study examines data from a conventional MEG magnetometer array, focusing on appropriate noise reduction techniques for magnetometers. We also determined the least required number of sensors needed for robust MVPA for image categorization experiments. ResultsWe found that the use of signal space separation (SSS) significantly lowered the classification accuracy considering a sub-array of 102 magnetometers or a sub-array of 204 gradiometers. We also found that classification accuracy did not improve when going beyond 30 sensors irrespective of whether SSS has been applied. Comparison with existing methodsThe power spectra of data filtered with SSS has a substantially higher noise floor that data cleaned with SSP or HFC. Consequently, the MVPA decoding results obtained from the SSS-filtered data are significantly lower compared to all other methods employed. ConclusionsWhen designing an MEG system based on SQUID magnetometers optimized for multivariate analysis for image categorization experiments, about 30 magnetometers are sufficient. We advise against applying SSS filters to data from MEG and OPM systems prior to performing MVPA as this method, albeit reducing low-frequency external noise contributions, also introduces an increase in broadband noise. We recommend employing noise reduction techniques that either decrease or maintain the noise floor of the data like signal-space projection, homogeneous field correction and gradient noise reduction. HighlightsO_LIA sensor array of about 30 sensors is sufficient for multivariate pattern analysis using conventional MEG magnetometers for image classification. C_LIO_LIUsing signal space separation filter on magnetometer data prior to multivariate pattern analysis might reduce classification accuracy due to an increase in white noise in the data contributed by the algorithm. C_LIO_LIWhen performing multivariate data analysis, other noise reduction approaches that diminish the contribution of external noise sources and reduce the variance of the data are advisable such as synthetic gradiometers, signal space projection or homogeneous field correction. C_LI

neuroscience↗

bioRxiv2022

Application of rapid invisible frequency tagging for brain computer interfaces

BackgroundBrain-computer interfaces (BCI) based on steady-state visual evoked potentials (SSVEPs/SSVEFs) are among the most commonly used BCI systems. They require participants to covertly attend to visual objects flickering at specified frequencies. The attended location is decoded in real-time by analysing the power of neuronal responses at the flicker frequency. New methodWe implemented a novel rapid invisible frequency-tagging technique, utilizing a state-of-the-art projector with refresh rates of up to 1440 Hz. We flickered the luminance of visual objects at 56 and 60 Hz, which was invisible to participants but produced strong neuronal responses measurable with magnetoencephalography (MEG). The direction of covert attention, decoded from frequency-tagging responses, was used to control a real-time BCI PONG game. ResultsOur results show that seven out of eight participants were able to play the pong game controlled by the frequency-tagging signal, with average accuracies exceeding 60%. Importantly, participants were able to modulate the power of the frequency-tagging response within a 1-second interval, while only seven occipital sensors were required to reliably decode the neuronal response. Comparison with existing methodsIn contrast to existing SSVEP-based BCI systems, rapid frequency-tagging does not produce a visible flicker. This extends the time-period participants can use it without fatigue, by avoiding distracting visual input. Furthermore, higher frequencies increase the temporal resolution of decoding, resulting in higher communication rates. ConclusionUsing rapid invisible frequency-tagging opens new avenues for fundamental research and practical applications. In combination with novel optically pumped magnetometers (OPMs), it could facilitate the development of high-speed and mobile next-generation BCI systems.

neuroscience↗