Biology subjects

Ziemann, U.

Publications and source records attributed to Ziemann, U..

At least 19 recordsLinked to original sources

bioRxiv2026

Disentangling evoked and induced activity from single-trial neurophysiological time series

Evoked and induced responses are fundamental neurophysiological readouts in the development and assessment of treatments for disorders with impaired neural function. Distinguishing these responses is essential for interpreting single-trial variability, identifying the determinants of these responses, and enabling real-time probing of cortical responsiveness. However, because of dynamic overlap, separating evoked and induced activity remains unsolved at the single-trial level when instantaneous fluctuations of cortical responsiveness are of interest. We developed the Evoked-Ongoing/Induced Separator (EOIS), a framework for separating event-evoked, ongoing, and event-modulated induced activity from single-trial data. EOIS recovers evoked and ongoing/induced components by leveraging their distinct phase-consistency features in MEG and real-time EEG, enabling subject-agnostic single-trial time-series analysis and attribution of cortical response variability to specific components. By delivering component-resolved time series, EOIS enables biomarker refinement, pharmacodynamic readouts, and closed-loop neuromodulation, facilitating translational deployment across individuals and care settings.

neuroscience↗

bioRxiv2026

Aperiodic neural activity predicts cortical excitability in TMS-EEG

Background: Fluctuating cortical excitability states are inherent to and critical for brain function. They can be probed non-invasively using transcranial magnetic stimulation (TMS) combined with electroencephalography (EEG) by measuring early local TMS-evoked potentials (EL-TEPs). Pre-stimulus periodic neural activity can predict fluctuating excitability states. To what extent aperiodic neural activity represents a marker of cortical excitability has not been explored. Objectives: We investigated the relationship between pre-stimulus aperiodic and periodic neural activity and EL-TEP amplitude in both the primary motor cortex (M1) and dorsolateral prefrontal cortex (DLPFC). Methods: Single-pulse TMS was delivered to the left M1 or left DLPFC in 29 and 11 healthy adults, respectively, while EEG was recorded simultaneously. Pre-stimulus EEG was parameterized into aperiodic and periodic components to extract the aperiodic exponent (slope of 1/f-like activity), alpha power (8-13 Hz), and beta power (13-30 Hz). EL-TEP peak-to-peak amplitudes were extracted from approximately 20-65 ms after the TMS pulse. Results: Aperiodic exponent showed a strong negative relationship with EL-TEP amplitude in both M1 and DLPFC, with steeper 1/f-like slopes associated with smaller EL-TEP amplitudes. Inconsistent relationships were found between alpha and beta power with EL-TEP amplitude across the two regions. Conclusions: These findings indicate that pre-stimulus aperiodic neural activity influences cortical excitability in both motor and prefrontal areas. Notably, the aperiodic exponent exhibited stronger associations with cortical excitability compared to periodic alpha or beta power. Brain-state-dependent protocols may benefit from real-time extraction of aperiodic exponent as a target feature to improve the precision and efficacy of TMS.

neuroscience↗

bioRxiv2026

Suppression of corticospinal excitability by sleep spindles without increase in GABAergic inhibition

Thalamocortical sleep spindles are hypothesised to support memory consolidation during sleep by creating transient windows of enhanced hippocampal-neocortical communication and synaptic plasticity. A recent real-time electroencephalography (EEG)-triggered transcranial magnetic stimulation (TMS) study found a pulsed suppression of corticospinal excitability during spindles relative to spindle-free non-rapid eye movement (NREM) sleep, driven by the spindle falling phase. We hypothesised that this phasic suppression may reflect local inhibitory network dynamics, measurable as GABA-A receptor-mediated short-interval intracortical inhibition (SICI) using paired-pulse TMS. We applied real-time EEG-triggered single- and paired-pulse TMS over the primary motor cortex during pre-sleep wakefulness, spindle-free N2/N3 sleep, and at four sleep spindle phases (peak, falling, trough, and rising). Corticospinal excitability was strongly reduced from wakefulness to spindle-free N2/N3 sleep, and further suppressed during sleep spindles. Numerically, excitability was lowest during the falling phase and trough, although we found no significant modulation across spindle phases. Contrary to our hypothesis, neither spindle presence nor phase significantly modulated SICI. Secondary analyses provided preliminary evidence that slow oscillations present at stimulation increased excitability and reduced SICI, irrespective of spindle presence. Together, these findings indicate distinct contributions of sleep/wake vigilance states, sleep spindles, and slow oscillations to cortical network dynamics, and provide new insight into the transient modulation of corticospinal excitability and GABA-A-receptor mediated SICI during human NREM sleep.

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bioRxiv2026

Sleep spindle state-dependent motor cortical plasticity induction by EEG-triggered ripple burst TMS

Background: Sleep spindles are fundamental for plasticity and memory consolidation. Here we sought to target different spindle states in sleeping healthy participants with real-time EEG-burst repetitive transcranial magnetic stimulation (rTMS) at hippocampal ripple frequency, and test the spindle state-dependent induction of corticospinal and sensorimotor cortical plasticity. We hypothesized that the spindle-trough is a particularly critical state for plasticity induction because hippocampal ripples are naturally nested in the spindle-trough, reflecting replay of memory traces and facilitating memory consolidation. Methods: Fourteen participants underwent four experimental nights, in which rTMS was applied either at the spindle-trough, spindle-peak, spindle random phase or during spindle-free epochs. Readouts of plasticity were changes in resting-state EEG (rsEEG) power, motor evoked potential (MEP) amplitude, local mean field amplitude (LMFA, for the N45 and P60 potential components), immediate response slope (IRS) and TMS-related time frequency response (TFR), tested 10 and 30 min after the end of the rTMS interventions upon awakening, and compared to pre-sleep baseline. Results: Spindle-trough rTMS resulted in pre- to post-sleep decreases of rsEEG beta-band power, MEP amplitude, P60-LMFA, IRS and TFR in the alpha-band, and an increase in the N45-LMFA. None of the other spindle state-dependent rTMS interventions resulted in consistent plastic changes. Conclusions: Targeting the spindle-trough with ripple-burst rTMS stands out in consistently leading to long-term depression-like changes across a broad array of corticospinal and sensorimotor cortical excitability readouts. This opens the intriguing opportunity of targeted manipulation of human sleep physiology for improving specific behavioral processes, such as memory consolidation.

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bioRxiv2026

Brain-State-Resolved Consistency of Corticospinal Responses with EEG-TMS

BackgroundTranscranial magnetic stimulation (TMS) over the primary motor cortex (M1) elicits motor-evoked potentials (MEPs), a neurophysiological marker of corticospinal excitability. Ongoing brain activity at the time of stimulation, such as the phase and power of the sensorimotor mu rhythm (8-13 Hz), has a significant impact on MEP amplitudes. However, it remains unclear whether these endogenous excitability states also influence the consistency of MEP amplitudes across repeated trials. ObjectivesWe investigated whether instantaneous mu dynamics modulate not only the magnitude but also the consistency of corticospinal responses to TMS. MethodsTwenty-nine healthy participants received 1200 single TMS pulses over the left M1 during simultaneous EEG recording. Trials were stratified based on pre-stimulus mu power, phase, and interhemispheric M1-M1 functional connectivity. Brain-state-resolved MEP variability was quantified using the coefficient of variation (CV) within subsets of trials defined by similar pre-stimulus mu dynamics. ResultsTrial subsets characterized by high mu power or high M1-M1 functional connectivity were associated with reduced MEP variability, indicating more consistent corticospinal output. In contrast, the mu phase did not significantly influence response consistency. Brain-state-resolved MEP variability showed greater stability across sessions compared to MEP variability estimated from random trial subsampling. ConclusionsPre-stimulus mu dynamics shape not only magnitude but also consistency of corticospinal responses to TMS. We show that corticospinal response consistency reflects a structured, brain-state-dependent property of the sensorimotor network. These findings contribute to our mechanistic understanding of brain-state-dependent neuromodulation and may be leveraged to reduce variability and improve efficacy to TMS. HighlightsO_LIOngoing sensorimotor mu dynamics shape both magnitude and consistency of MEPs. C_LIO_LITrial subsets characterized by high mu power were associated with reduced MEP variability. C_LIO_LIMu phase modulated MEP amplitude but did not influence MEP consistency. C_LIO_LIBrain-state-resolved estimates of MEP variability were more reliable across sessions. C_LIO_LIFuture TMS protocols may reduce effect variability by targeting stable excitability states. C_LI

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bioRxiv2026

Real-time brain-state-coupled cortico-cortical paired associative stimulation of cognitive networks

Brain networks coordinate distributed neuronal assemblies to support cognition. Spike-timing-dependent plasticity (STDP) and neuronal oscillations are key substrates for state-gated learning rules that shape network coupling and cognitive operations; nonetheless, how STDP mechanisms interact with neuronal oscillations is largely unexplored in humans. Cortico-cortical paired associative stimulation (ccPAS) provides a non-invasive system-level model of associative timing rules by pairing dual-site transcranial magnetic stimulation (TMS) across axonally connected regions with an inter-stimulus interval matched to pathway conduction. Here we: 1) synthesize ccPAS applications and barriers to brain-state-coupled implementation in cognitive networks; 2) provide an actionable roadmap for real-time state estimation, targeting, and dual-site parameter selection; and 3) demonstrate a novel implementation of theta-phase-locked fronto-parietal (FP) ccPAS with concurrent EEG in adult human participants. We tested whether ccPAS delivered at the positive phase of ongoing theta (POS) induces distinct changes in evoked EEG activity and FP connectivity compared to phase-uncoupled ccPAS (RAND) and phase-locked single-site prefrontal (PREF) controls. At the evoked level, POS produced a fronto-central polarity reversal of the canonical N45 component and a right parieto-temporal negativity relative to both controls. At the network level, POS induced frequency-specific reconfigurations in post-intervention connectivity beyond either control ingredient alone. Together, these changes in evoked activity and rapid network reconfiguration provide the first empirical evidence consistent with phase-gated STDP in humans--whereby oscillatory phase gates cortical excitability and modulates STDP efficacy--emerging as short-term network-level expression. Future work will assess long-term plasticity by tracking connectivity at later time points and testing for concomitant behavioral effects. SignificanceThe real-time brain state critically shapes how plasticity mechanisms are expressed in response to brain stimulation. This article provides a forward-looking synthesis of the scientific and technical challenges associated with ccPAS--an STDP induction model in the human cortex--and outlines the steps required to advance it toward real-time brain-state-coupled implementation. To our knowledge, this is the first application of brain-state-coupled ccPAS within a cognitive network. By personalizing stimulation to the individuals ongoing neural state, this approach may reduce variability, limit off-target effects, and enhance plasticity induction. Ultimately--by modulating network-level function in a brain-state-dependent manner--this technique could augment therapeutic outcomes in disorders marked by network dysfunction such as ADHD, Alzheimers disease, and major depressive disorder, potentially maximizing efficacy in patients unresponsive to existing treatments.

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bioRxiv2026

Not Just Noise: Aperiodic Brain Activity Reflects Corticospinal Excitability

BackgroundElectroencephalography (EEG) can be combined with transcranial magnetic stimulation (TMS) to perform brain-state-dependent stimulation. EEG-TMS studies have shown that corticospinal excitability, as measured via motor evoked potentials (MEPs), is modulated by pre-stimulus periodic EEG features, such as sensorimotor mu-rhythm phase and power. However, the influence of aperiodic brain activity on corticospinal excitability is largely unexplored. ObjectivesWe evaluated the relationship between aperiodic and periodic mu-power, aperiodic exponent, and mu-phase on MEP amplitudes using EEG-TMS. MethodsWe applied 800 single TMS pulses to the left primary motor cortex in 78 healthy adults. We calculated aperiodic/periodic mu-power, aperiodic exponent, and mu-phase for each trial from the pre-stimulus C3-Hjorth transformed EEG. MEP amplitudes were extracted from the right first dorsal interosseous muscle. A linear mixed-effects model assessed relationships between MEP amplitudes and EEG features, with interactions between mu-phase and all other EEG features. ResultsAperiodic and periodic mu-power, aperiodic exponent, and mu-phase significantly modulated MEP amplitudes. Higher aperiodic/periodic mu-power was associated with larger MEP amplitudes, while higher aperiodic exponent was associated with smaller MEP amplitudes. We found a significant interaction effect of aperiodic exponent and mu-phase on MEP amplitude. Aperiodic exponent was negatively associated with MEPs for trough, rising, falling phases, but positively associated with MEPs for peak phase. ConclusionsAperiodic and periodic features of brain activity are reflective of dissociable corticospinal excitability states. Future brain-state-dependent TMS interventions may include aperiodic EEG features, such as aperiodic mu-power and exponent, in addition to the well-established periodic features.

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bioRxiv2025

Diazepam alters the shape of alpha oscillations recorded from human cortex using EEG

While neural oscillations are conventionally assessed via their frequency, power and phase, developing literature suggests that their shape also provides neurophysiological and functional information. However, the extent to which the shape of oscillations recorded non-invasively in humans index specific brain processes remains unclear. This study implemented a pharmaco-EEG approach to begin addressing this limitation. In 21 healthy adults, resting-state EEG data was collected before and after placebo or diazepam, a positive allosteric modulator of type A {gamma}-aminobutyric acid (GABAA) receptors. The shape of individual cycles in the alpha band was then derived using empirical mode decomposition, followed by extraction of principal components (PCs) describing specific facets of alpha shape. Results of this approach show that all shape features were unchanged following placebo. In contrast, diazepam was associated with complex changes in several shape features, including peak-trough shape and edge speed. While changes in shape were apparent in all cortical lobes, the strongest alterations were specific to sensorimotor and parietal cortices. Taken together, our results support the neurophysiological utility of waveform shape, particularly with respect to non-invasive human recordings. Furthermore, the regional specificity of effects highlights the need for more granular exploration of waveform diversity.

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bioRxiv2025

Personalized real-time inference of momentary excitability from human EEG

The efficacy of transcranial magnetic stimulation (TMS) is often limited by non-adaptive protocols that disregard instantaneous brain states, potentially constraining therapeutic outcomes. Current EEG-guided approaches are hindered by their reliance on motor-evoked potentials (MEPs), which confound cortical and spinal excitability and restrict applications to the motor cortex, and a dependence on static biomarkers that cannot adapt to changing neurophysiological patterns. We introduce PRIME (Personalized Real-time Inference of Momentary Excitability), a deep learning framework that predicts cortical excitability, quantified by TMS-evoked potential (TEP) amplitude, from raw EEG signals. By targeting cortical excitability directly, PRIME enables brain state-dependent stimulation across any cortical region. PRIME incorporates transfer learning and continual adaptation to automatically identify personalized biomarkers, allowing stimulation timing to be adapted across individuals and sessions. PRIME successfully predicts cortical excitability with minimal latency, providing a computational foundation for next-generation, personalized closed-loop TMS interventions.

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bioRxiv2025

Predictive modeling of TMS-evoked responses: Unraveling instantaneous excitability states

Transcranial magnetic stimulation (TMS) combined with electroencephalography (EEG) and electromyography (EMG) provides a unique window into instantaneous cortical and corticospinal excitability states. We investigated 50 healthy participants to determine how fluctuations in pre-stimulus brain activity influence single-trial TMS-evoked potentials (TEPs) and motor-evoked potentials (MEPs). We developed a novel automated source-level TEP extraction method using individualized spatiotemporal priors that is robust against poor single-trial signal-to-noise ratios (SNRs) and ongoing oscillations. TEP and MEP amplitudes were predicted with linear mixed-effects models based on pre-stimulation EEG band-powers (theta to gamma), while accounting for temporal drifts (within-session trends), coil control, and inter-subject differences. We found that higher pre-stimulus sensorimotor alpha, beta, and gamma power were each associated with larger TEPs, indicating a more excitable cortical state. Increases in alpha and gamma power immediately before stimulation specifically predicted larger MEPs, reflecting increased corticospinal excitability. These results reveal relationships between ongoing oscillatory brain states and TMS response amplitudes, identifying EEG biomarkers of high- and low-excitability states. In conclusion, our study demonstrates the feasibility of single-trial source-level TMS-EEG analysis and shows that spontaneous alpha-, beta-, and gamma-band oscillations modulate motor cortical and corticospinal responsiveness. These findings pave the way for EEG-informed, brain-state-dependent TMS protocols to optimize neuromodulatory interventions in clinical and research applications.

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bioRxiv2025

Plasticity of interhemispheric motor cortex connectivity induced by brain state-dependent cortico-cortical paired-associative stimulation

Transcallosal connectivity between the hand areas of the two primary motor cortices (M1) is important for coordination of unimanual and bimanual hand motor function. Effective connectivity of this M1-M1 pathway can be tested in the form of short-interval interhemispheric inhibition (SIHI) using dual-coil transcranial magnetic stimulation (TMS). Recently, we and others have demonstrated that the phase of the ongoing sensorimotor {micro}-rhythm has significant impact on corticospinal excitability as measured by motor evoked potential (MEP) amplitude, and repetitive TMS of the high-excitability state (trough of the {micro}-rhythm) but not other states resulted in long-term potentiation-like MEP increase. Here, we tested to what extent the phase of the ongoing {micro}-rhythm in the two M1 cortices affects long-term change in SIHI. In healthy subjects we applied cortico-cortical paired associative stimulation (ccPAS) in four different {micro}-phase conditions in the left conditioning M1 and right test M1 (trough-trough, trough-positive peak, positive peak-trough, random phase). We found long-term strengthening of SIHI but no differential effect of phase conditions. Findings point to a distinct regulation of plasticity of corticospinal vs. M1-M1 connectivity. The observed ccPAS-induced strengthening of effective M1-M1 connectivity (SIHI) may be utilized for therapeutic applications that potentially benefit from modification of interhemispheric excitation/inhibition balance.

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bioRxiv2024

Pulsed inhibition of corticospinal excitability by the thalamocortical sleep spindle

Thalamocortical sleep spindles, i.e., oscillatory bursts at [~]12-15 Hz of waxing and waning amplitude, are a hallmark feature of non-rapid eye movement (NREM) sleep and believed to play a key role in sleep-dependent memory reactivation and consolidation. Generated in the thalamus and projecting to neocortex and hippocampus, they are phasically modulated by neocortical slow oscillations (<1 Hz) and in turn phasically modulate hippocampal sharp-wave ripples (>80 Hz). This hierarchical cross-frequency nesting may enable phase-dependent plasticity in the neocortex, and spindles have thus been considered windows of plasticity in the sleeping brain. However, the assumed phasic excitability modulation had not yet been demonstrated for spindles. Utilizing a recently developed real-time spindle detection algorithm, we applied spindle phase-triggered transcranial magnetic stimulation (TMS) to the primary motor cortex (M1) hand area and measured motor evoked potentials (MEP) to characterize corticospinal excitability during sleep spindles. We found a net suppression of MEP amplitudes during spindles, driven by selective inhibition during the falling flank of the spindle oscillation, but no inhibition during its peak, rising flank, and trough. Importantly, this phasic inhibition occurred on top of the general sleep-related inhibition observed during spindle-free NREM sleep and did not extend into the immediate refractory post-spindle periods. We conclude that spindles exert asymmetric "pulsed inhibition" of corticospinal excitability, which is assumedly relevant for processes of phase-dependent plasticity. These findings and the developed real-time spindle targeting methods will enable future studies to uncover the causal role of spindles in synaptic plasticity and systems memory consolidation.

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bioRxiv2024

Decoding Motor Excitability in TMS using EEG-Features:An Exploratory Machine Learning Approach

BackgroundWith the burgeoning interest in personalized treatments for brain network disorders, closed-loop transcranial magnetic stimulation (TMS) represents a promising frontier. Relying on the real-time adjustment of stimulation parameters based on brain signal decoding, the success of this approach depends on the identification of precise biomarkers for timing the stimulation optimally. ObjectiveWe aimed to develop and validate a supervised machine learning framework for the individualized prediction of motor excitability states, leveraging a broad spectrum of sensor and source space EEG features. MethodsOur approach integrates multi-scale EEG feature extraction and selection within a nested cross-validation scheme, tested on a cohort of 20 healthy participants. We assessed the frameworks performance across different classifiers, feature sets, and experimental protocols to ensure robustness and generalizability. ResultsPersonalized classifiers demonstrated a statistically significant mean predictive accuracy of 72 {+/-} 11%. Consistent performance across various testing conditions highlighted the sufficiency of sensor-derived features for accurate excitability state predictions. Subtype analysis revealed distinct clusters linked to specific brain regions and oscillatory features as well as the need for a more extensive feature set for effective biomarker identification than conventionally considered. ConclusionsOur machine learning framework effectively identifies predictive biomarkers for motor excitability, holding potential to enhance the efficacy of personalized closed-loop TMS interventions. While the clinical applicability of our findings remains to be validated, the consistent performance across diverse testing conditions and the efficacy of sensor-only features suggest promising avenues for clinical research and wider applications in brain signal classification.

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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

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bioRxiv2023

Modulation of dorsal premotor cortex disrupts neuroplasticity of primary motor cortex in young and older adults

Although transcranial magnetic stimulation (TMS) research demonstrates that dorsal premotor cortex (PMd) influences neuroplasticity within primary motor cortex (M1), it is unclear how ageing modifies this communication. The present study investigated the influence of PMd on different indirect (I) wave inputs within M1 that mediate cortical plasticity in young and older adults. 15 young and 15 older participants completed two experimental sessions that examined the effects of intermittent theta burst stimulation (iTBS) to M1 when preceded by iTBS (PMd iTBS-M1 iTBS) or sham stimulation (PMd sham-M1 iTBS) to PMd. Changes in corticospinal excitability post-intervention were assessed with motor evoked potentials (MEP) recorded from right first dorsal interosseous using posterior-anterior (PA) and anterior-posterior (AP) current single-pulse TMS (PA1mV; AP1mV; PA0.5mV, early I-wave; AP0.5mV, late I-wave). Although PA1mV did not change post-intervention (P = 0.628), PMd iTBS-M1 iTBS disrupted the expected facilitation of AP1mV (to M1 iTBS) in young and older adults (P = 0.002). Similarly, PMd iTBS-M1 iTBS disrupted PA0.5mV facilitation in young and older adults (P = 0.030), whereas AP0.5mV facilitation was not affected in either group (P = 0.218). This suggests that while PMd specifically influences the plasticity of early I-wave circuits, this communication is preserved in older adults.

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bioRxiv2021

Artifacts in EEG-based BCI therapies: friend or foe?

EEG-based brain-computer interfaces (BCI) have promising therapeutic potential beyond traditional neurofeedback training, such as enabling personalized and optimized virtual reality (VR) neurorehabilitation paradigms where the timing and parameters of the visual experience is synchronized with specific brain-states. While BCI algorithms are often designed to focus on whichever portion of a signal is most informative, in these brain-state-synchronized applications, it is of critical importance that the resulting decoder is sensitive to physiological brain activity representative of various mental states, and not to artifacts, such as those arising from naturalistic movements. In this study, we compare the relative classification accuracy with which different motor tasks can be decoded from both extracted brain activity and artifacts contained in the EEG signal. EEG data was collected from 17 chronic stroke patients while performing six different head, hand, and arm movements in a realistic VR-based neurorehabilitation paradigm. Results show that the artifactual component of the EEG signal is significantly more informative than brain activity with respect to classification accuracy. This finding is consistent across different feature extraction methods and classification pipelines. While informative brain signals can be recovered with suitable cleaning procedures, we recommend that features should not be designed solely to maximize classification accuracy, as this could select for remaining artifactual components. We also propose the use of machine learning approaches that are interpretable to verify that classification is driven by physiological brain-states. In summary, whereas informative artifacts are a helpful friend in BCI-based communication applications, they can be a problematic foe in the estimation of physiological brain states.

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bioRxiv2021

Real-time decoding of 5 finger movements from 2 EMG channels for mixed reality human-computer interaction

The search for optimized forms of human-computer interaction (HCI) has intensified alongside the growing potential for the combination of biosignals with virtual reality (VR) and augmented reality (AR) to enable the next generation of personal computing. At the core, this requires decoding the users biosignals into digital commands. Electromyography (EMG) is a biosensor of particular interest due to the ease of data collection, the relatively high signal-to-noise-ratio, its non-invasiveness, and the ability to interpret the signal as being generated by (intentional) muscle activity. Here, we investigate the potential of using data taken from a simple 2-channel EMG setup to differentiate 5 distinct movements. In particular, EMG was recorded from two bipolar sensors over small hand muscles (extensor digitorum, flexor digitorum profundus) while a subject performed 50 trials of dorsal extension and return for each of the five digits. The maximum and the mean values across the trial were determined for each channel and used as features. A k-nearest neighbors (kNN) classification was performed and overall 5-class classification accuracy reached 94% when using the full trials time window, while simulated real-time classification reached 90.4% accuracy when using the constructed kNN model (k=3) with a 280ms sliding window. Additionally, unsupervised learning was performed and a homogeneity of 85% was achieved. This study demonstrates that reliable decoding of different natural movements is possible with fewer than one channel per class, even without taking into account temporal features of the signal. The technical feasibility of this approach in a real-time setting was validated by sending real-time EMG data to a custom Unity3D VR application through a Lab Streaming Layer to control a user interface. Further use-cases of gamification and rehabilitation were also examined alongside the integration of eye-tracking and gesture recognition for a sensor fusion approach to HCI and user intent.

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bioRxiv2021

Altered cortical phase- and amplitude-coupling in Multiple Sclerosis

Distinguishing groups of subjects or experimental conditions in a high-dimensional feature space is a common goal in modern neuroimaging studies. Successful classification depends on the selection of relevant features as not every neuronal signal component or parameter is informative about the research question at hand. Here, we developed a novel unsupervised multistage analysis approach that combines dimensionality reduction, bootstrap aggregating and multivariate classification to select relevant neuronal features. We tested the approach by identifying changes of brain-wide electrophysiological coupling in Multiple Sclerosis. Multiple Sclerosis is a demyelinating disease of the central nervous system that can result in cognitive decline and physical disability. However, related changes in large-scale brain interactions remain poorly understood and corresponding non-invasive biomarkers are sparse. We thus compared brain-wide phase- and amplitude-coupling of frequency specific neuronal activity in relapsing-remitting Multiple Sclerosis patients (n = 17) and healthy controls (n = 17) using magnetoencephalography. Our analysis approach allowed us to identify systematic and non-redundant changes of both phase- and amplitude-coupling in the diseased brain. Changes included both, increased and decreased neuronal coupling in wide-spread, bilateral neuronal networks across a broad range of frequencies. These changes allowed to successfully classify patients and controls with an accuracy of 84%. Furthermore, classification confidence predicted behavioral scores of disease severity. In sum, our results unravel systematic changes of large-scale phase- and amplitude coupling in Multiple Sclerosis. Furthermore, our results establish a new analysis approach to efficiently contrast high-dimensional neuroimaging data between experimental groups or conditions. HighlightsO_LIA novel multistage approach to analyze high-dimensional neuronal coupling data. C_LIO_LIApplication to MEG recordings in relapsing-remitting Multiple Sclerosis patients. C_LIO_LIIdentification of altered phase- and amplitude-coupling in Multiple Sclerosis. C_LIO_LIClassification of patients and controls with 84% accuracy. C_LIO_LIClassification confidence predicts behavioral scores of disease severity. C_LI

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