Biology subjects

Metsomaa, J.

Publications and source records attributed to Metsomaa, J..

4 recordsLinked to original sources

bioRxiv2026

Real-time noise cancellation for EEG monitoring and brain-computer interfaces

Raw electroencephalography (EEG) data suffer from a low signal-to-noise ratio (SNR). Effective real-time cleaning of EEG would improve the robustness of EEG monitoring, brain-computer interfaces, and brain-state-dependent stimulation applications in both laboratory and real-life environments. Here, we present a real-time source-estimate-utilizing noise-discarding algorithm (rtSOUND) for EEG noise suppression. We adapted the established offline SOUND algorithm for real-time use and implemented it into an open-source EEG-processing software. Instead of using conventional temporal filtering, SOUND operates the data with a spatial filter. This makes it particularly suitable for real-time applications, since each streamed data sample can be cleaned separately in near real-time. Unlike temporal filtering methods, SOUND does not make assumptions about the frequency content of noise and brain signals but assumes that the noise topographies differ from those generated by brain activity. With an EEG forward model, rtSOUND cross-validates the EEG time series and estimates the spatial distribution of noise, resulting in a data-cleaning linear spatial filter. Each streamed EEG sample is processed synchronously, and the filter is periodically updated in an asynchronous parallel process. The algorithm rapidly updates the spatial filter in response to changes in the data, ensuring that noise reduction is both immediate and accurately adapted to the current EEG state. rtSOUND was validated with transcranial magnetic stimulation (TMS)-EEG and resting-state EEG datasets measured from 25 healthy subjects. SNR of both TMS-evoked potentials and resting-state data consistently improved across various metrics. rtSOUND particularly benefitted channels with poor SNR, while retaining the waveforms in high-quality channels. The algorithm was fast enough to process data streamed at 5 kHz. rtSOUND improves the SNR of EEG in real time, providing a robust method to improve signal quality for brain-state-monitoring applications.

neuroscience↗

bioRxiv2023

Intrinsic motor network connectivity predicts corticospinal excitability

State-dependent non-invasive brain stimulation (NIBS) informed by electroencephalography (EEG) has contributed to the understanding of NIBS inter-subject and inter-session variability. While these approaches focused on local EEG characteristics, it is acknowledged that the brain exhibits an intrinsic long-range dynamic organization in networks. This proof-of-concept study explores whether EEG connectivity of the primary motor cortex (M1) in the pre-stimulation period aligns with the motor network (MN) and how MN state affects responses to transcranial magnetic stimulation (TMS) of M1. One thousand suprathreshold TMS pulses were delivered to left M1 in 8 subjects at rest, with simultaneous EEG. Motor evoked potentials (MEPs) were measured from the right hand. Source-space functional connectivity of left M1 to the whole-brain was assessed using the imaginary part of the Phase Locking Value at the frequency of the sensorimotor {micro}-rhythm in a 1-second window before the pulse. Group-level connectivity revealed functional links between left M1, left supplementary motor area, and right M1. Also, pulses delivered at high MN connectivity states result in a greater MEP amplitude compared to low connectivity states. At single-subject level, this relation is more expressed in subjects that feature an overall high cortico-spinal excitability. In conclusion, this study paves the way for MN connectivity based NIBS. HighlightsO_LIEEG pre-stimulus connectivity of left M1 largely corresponds to the motor network C_LIO_LIStronger motor network (MN) connectivity corresponds to greater MEP amplitudes C_LIO_LILinear regression models based on MN connectivity predicts MEP amplitude C_LI

neuroscience↗

bioRxiv2021

Closed-loop optimization of transcranial magnetic stimulation with electroencephalography feedback

BackgroundTranscranial magnetic stimulation (TMS) is widely used in brain research and treatment of various brain dysfunctions. However, the optimal way to target stimulation and administer TMS therapies, for example, where and in which electric field direction the stimuli should be given, is yet to be determined. ObjectiveTo develop an automated closed-loop system for adjusting TMS parameters (in this work, the stimulus orientation) online based on TMS-evoked brain activity measured with electroencephalography (EEG). MethodsWe developed an automated closed-loop TMS-EEG set-up. In this set-up, the stimulus parameters are electronically adjusted with multi-locus TMS. As a proof of concept, we developed an algorithm that automatically optimizes the stimulation orientation based on single-trial EEG responses. We applied the algorithm to determine the electric field orientation that maximizes the amplitude of the TMS- EEG responses. The validation of the algorithm was performed with six healthy volunteers, repeating the search twenty times for each subject. ResultsThe validation demonstrated that the closed-loop control worked as desired despite the large variation in the single-trial EEG responses. We were often able to get close to the orientation that maximizes the EEG amplitude with only a few tens of pulses. ConclusionOptimizing stimulation with EEG feedback in a closed-loop manner is feasible and enables effective coupling to brain activity.

bioengineering↗

bioRxiv2020

Automated search of stimulation targets with closed-loop transcranial magnetic stimulation

Transcranial magnetic stimulation (TMS) protocols often include a manual search of an optimal location and orientation of the coil or peak stimulating electric field to elicit motor responses in a target muscle. This target search is laborious, and the result is user-dependent. Here, we present a closed-loop search method that utilizes automatic electronic adjustment of the stimulation based on the previous responses. The electronic adjustment is achieved by multi-locus TMS, and the adaptive guiding of the stimulation is based on the principles of Bayesian optimization to minimize the number of stimuli (and time) needed in the search. We compared our target-search method with other methods, such as systematic sampling in a predefined cortical grid. Validation experiments on five healthy volunteers and further offline simulations showed that our adaptively guided search method needs only a relatively small number of stimuli to provide outcomes with good accuracy and precision. The automated method enables fast and user-independent optimization of stimulation parameters in research and clinical applications of TMS.Competing Interest StatementRJI is an advisor and a minority shareholder of Nexstim Plc. The other authors declare no competing interests.View Full Text

bioengineering↗