Biology subjects

Bezsudnova, Y.

Publications and source records attributed to Bezsudnova, Y..

3 recordsLinked to original sources

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↗