Biology subjects

Csaky, R.

Publications and source records attributed to Csaky, R..

3 recordsLinked to original sources

bioRxiv2024

Generalizable gesture recognition usingmagnetomyography

The progression of human-computer interfaces into immersive and touchless realities requires new ways of interacting with machines that are correspondingly intuitive and seamless. Among these are gesture-based systems that use natural hand movements to interact with and control digital devices. Today, these systems are most commonly implemented through the use of cameras or inertial sensors, which have drawbacks in environments that are poorly lit, in conditions where the hands are obscured, or for applications that require fine motor control. More recent studies have advocated for the use of surface electromyography (sEMG) to capture gesture information by sensing electrical activity generated by muscle contraction. While promising demonstrations have been shown, studies have also outlined limitations in sEMG when it comes to generalization across a population, largely due to physiological differences between individuals. Magnetomyography (MMG) is an alternative modality for measuring the same motor signals at the muscle, but is impervious to distortions caused by tissue, hair, and moisture; this indicates potential for lower variability caused by physiological differences and changes in skin conductivity, making MMG a promising generalizable solution for gesture control. To test this theory, we developed wristbands with magnetic sensors and implemented a signal processing pipeline for gesture classification. Using this system, we measured MMG across 30 participants performing a gesture task consisting of nine discrete gestures. We demonstrate average single-participant classification accuracy of 95.4%, rivaling state-of-the-art accuracy with sEMG. In addition, we achieved higher cross-session and cross-participant accuracy compared to sEMG studies. Given that these results were obtained with a non-ideal recording system, we anticipate significantly better results with better sensors. Together, these findings suggest that MMG can provide higher performance for control systems based on gesture recognition by overcoming limitations of existing techniques.

neuroscience↗

bioRxiv2024

Magnetomyography: A novel modality for non-invasive muscle sensing

The measurement of magnetic fields generated by skeletal muscle activity, called magnetomyography (MMG), has seen renewed interest from the academic community in recent years. Although studies have demonstrated complex models of MMG and experiments classifying between different movements using MMG, there has yet to be time frequency analysis of MMG as well as concurrent recordings of MMG and its electrical counterpart, surface electromyography (sEMG). Here, we aim to better understand MMG in the context of sEMG by simultaneously recording both modalities during various muscle contraction tasks. We found that, similar to sEMG, MMG shows highly linearly correlated power to the degree of muscle contraction, has a unimodal distribution in spectral power, and can detect changes in muscle fatigue via changes in the spectral distribution. One main difference we found was that MMG typically has more high frequency content compared to sEMG, even when accounting for the filtering induced by the size of the sEMG electrodes. We additionally demonstrate empirically the decrease in MMG power due to distance from the arm and show MMG decreases slower than the inverse square law and can be measured up to 50 mm from the surface of the skin. Finally, we were able to capture MMG with non-OPM sensors showing that sensor technology has made great strides towards enabling MMG applications.

neuroscience↗

bioRxiv2023

Interpretable full-epoch multiclass decoding for M/EEG

Multivariate pattern analysis (MVPA) of Magnetoencephalography (MEG) and Electroencephalography (EEG) data is a valuable tool for understanding how the brain represents and discriminates between different stimuli. Identifying the spatial and temporal signatures of stimuli is typically a crucial output of these analyses. Such analyses are mainly performed using linear, pairwise, sliding window decoding models. These allow for relative ease of interpretation, e.g. by estimating a time-course of decoding accuracy, but are computationally intensive and can have limited decoding performance. On the other hand, full epoch decoding models, commonly used for brain-computer interface (BCI) applications, can provide better decoding performance. However, they lack methods for interpreting the contributions of spatial and temporal features. In this paper, we propose an approach that combines a multiclass, full epoch decoding model with supervised dimensionality reduction, while still being able to reveal the contributions of spatiotemporal and spectral features using permutation feature importance. We demonstrate the approach on 3 different task MEG datasets using image presentations. Our results demonstrate that this approach consistently achieves higher accuracy than the peak accuracy of a sliding window decoder while estimating the relevant spatiotemporal features in the MEG signal. Finally, we show that our multiclass model can also be used for pairwise decoding, eliminating the computational burden of training separate models for each pairwise combination of stimuli.

neuroscience↗