Are high-frequency (600 Hz) oscillations in human somatosensory evoked potentials due to phase-resetting phenomena?

Wearable electroencephalography and multi-modal mental state classification: A systematic literature review

BACKGROUND

Wearable multi-modal time-series classification applications outperform their best uni-modal counterparts and hold great promise. A modality that directly measures electrical correlates from the brain is electroencephalography. Due to varying noise sources, different key brain regions, key frequency bands, and signal characteristics like non-stationarity, techniques for data pre-processing and classification algorithms are task-dependent.

METHOD

Here, a systematic literature review on mental state classification for wearable electroencephalography is presented. Four search terms in different combinations were used for an in-title search. The search was executed on the 29th of June 2022, across Google Scholar, PubMed, IEEEXplore, and ScienceDirect. 76 most relevant publications were set into context as the current state-of-the-art in mental state time-series classification.

RESULTS

Pre-processing techniques, features, and time-series classification models were analyzed. Across publications, a window length of one second was mainly chosen for classification and spectral features were utilized the most. The achieved performance per time-series classification model is analyzed, finding linear discriminant analysis, decision trees, and k-nearest neighbors models outperform support-vector machines by a factor of up to 1.5. A historical analysis depicts future trends while under-reported aspects relevant to practical applications are discussed.

CONCLUSIONS

Five main conclusions are given, covering utilization of available area for electrode placement on the head, most often or scarcely utilized features and time-series classification model architectures, baseline reporting practices, as well as explainability and interpretability of Deep Learning. The importance of a 'test battery' assessing the influence of data pre-processing and multi-modality on time-series classification performance is emphasized.

Non-invasive recording of high-frequency signals from the human spinal cord

Throughout the somatosensory system, neuronal ensembles generate high-frequency signals in the range of several hundred Hertz in response to sensory input. High-frequency signals have been related to neuronal spiking, and could thus help clarify the functional architecture of sensory processing. Recording high-frequency signals from subcortical regions, however, has been limited to clinical pathology whose treatment allows for invasive recordings. Here, we demonstrate the feasibility to record 200-1200 Hz signals from the human spinal cord non-invasively, and in healthy individuals. Using standard electroencephalography equipment in a cervical electrode montage, we observed high-frequency signals between 200 and 1200 Hz in a time window between 8 and 16 ms after electric median nerve stimulation (n = 15). These signals overlapped in latency, and, partly, in frequency, with signals obtained via invasive, epidural recordings from the spinal cord in a patient with neuropathic pain. Importantly, the observed high-frequency signals were dissociable from classic spinal evoked responses. A spatial filter that optimized the signal-to-noise ratio of high-frequency signals led to submaximal amplitudes of the evoked response, and vice versa, ruling out the possibility that high-frequency signals are merely a spectral representation of the evoked response. Furthermore, we observed spontaneous fluctuations in the amplitude of high-frequency signals over time, in the absence of any concurrent, systematic change to the evoked response. High-frequency, "spike-like" signals from the human spinal cord thus carry information that is complementary to the evoked response. The possibility to assess these signals non-invasively provides a novel window onto the neurophysiology of the human spinal cord, both in a context of top-down control over perception, as well as in pathology.

Electrophysiological characterization of the hyperdirect pathway and its functional relevance for subthalamic deep brain stimulation

The subthalamic nucleus (STN) receives input from various cortical areas via hyperdirect pathway (HDP) which bypasses the basal-ganglia loop. Recently, the HDP has gained increasing interest, because of its relevance for STN deep brain stimulation (DBS). To understand the HDP's role cortical responses evoked by STN-DBS have been investigated. These responses have short (<2 ms), medium (2-15 ms), and long (20-70 ms) latencies. Medium-latency responses are supposed to represent antidromic cortical activations via HDP. Together with long-latency responses the medium responses can potentially be used as biomarker of DBS efficacy as well as side effects. We here propose that the activation sequence of the cortical evoked responses can be conceptualized as high frequency oscillations (HFO) for signal analysis. HFO might therefore serve as marker for antidromic activation. Using existing knowledge on HFO recordings, this approach allows data analyses and physiological modeling to advance the pathophysiological understanding of cortical DBS-evoked high-frequency activity.

Noninvasive neuromagnetic single-trial analysis of human neocortical population spikes

Neuronal spiking is commonly recorded by invasive sharp microelectrodes, whereas standard noninvasive macroapproaches (e.g., electroencephalography [EEG] and magnetoencephalography [MEG]) predominantly represent mass postsynaptic potentials. A notable exception are low-amplitude high-frequency (∼600 Hz) somatosensory EEG/MEG responses that can represent population spikes when averaged over hundreds of trials to raise the signal-to-noise ratio. Here, a recent leap in MEG technology-featuring a factor 10 reduction in white noise level compared with standard systems-is leveraged to establish an effective single-trial portrayal of evoked cortical population spike bursts in healthy human subjects. This time-resolved approach proved instrumental in revealing a significant trial-to-trial variability of burst amplitudes as well as time-correlated (∼10 s) fluctuations of burst response latencies. Thus, ultralow-noise MEG enables noninvasive single-trial analyses of human cortical population spikes concurrent with low-frequency mass postsynaptic activity and thereby could comprehensively characterize cortical processing, potentially also in diseases not amenable to invasive microelectrode recordings.

Intraoperative subdural low-noise EEG recording of the high frequency oscillation in the somatosensory evoked potential

OBJECTIVE

The detectability of high frequency oscillations (HFO, >200Hz) in the intraoperative ECoG is restricted by their low signal-to-noise ratio (SNR). Using the somatosensory evoked HFO, we quantify how HFO detectability can benefit from a custom-made low-noise amplifier (LNA).

METHODS

In 9 patients undergoing tumor surgery in the central region, subdural strip electrodes were placed for intraoperative neurophysiological monitoring. We recorded the somatosensory evoked potential (SEP) simultaneously by custom-made LNA and by a commercial device (CD). We varied the stimulation rate between 1.3 and 12.7Hz to tune the SNR of the N20 component and the evoked HFO and quantified HFO detectability at the single trial level. In three patients we compared Propofol® and Sevoflurane® anesthesia.

RESULTS

In the average, amplitude decreased in both in N20 and evoked HFO amplitude with increasing stimulation rate (p<0.05). We detected a higher percentage of single trial evoked HFO with the LNA (p<0.001) for recordings with low impedance (<5kΩ). Average amplitudes were indistinguishable between anesthesia compounds.

CONCLUSION

Low-noise amplification improves the detection of the evoked HFO in recordings with subdural electrodes with low impedance.

SIGNIFICANCE

Low-noise EEG might critically improve the detectability of interictal spontaneous HFO in subdural and possibly in scalp recordings.

Cortical somatosensory evoked high-frequency (600Hz) oscillations predict absence of severe hypoxic encephalopathy after resuscitation

OBJECTIVE

Following cardiac arrest (CA), hypoxic encephalopathy (HE) frequently occurs and hence reliable neuroprognostication is crucial to decide on the extent of intensive care. Several investigations predict severe HE leading to persistent unresponsive wakefulness or death, with high specificity. Only few studies attempted to predict absence of severe HE. Cortical somatosensory evoked high-frequency (600Hz) oscillation (HFO) bursts indicate the presence of highly synchronized spiking activity in the primary somatosensory cortex. Since global neuronal damage characterizes severe HE preserved cortical HFOs may early exclude severe HE.

METHODS

We determined amplitudes of early and late HFO bursts in 302 comatose CA patients after median nerve somatosensory evoked potential (SSEPs) and clinical outcome upon intensive care unit discharge using the cerebral performance category (CPC) scale.

RESULTS

We detected significant early HFO bursts in 146 patients and late HFO bursts in 95 patients. Only one of 27 unresponsive wakefulness patients had a late HFO burst amplitude above 70nV and all seventeen patients who died despite higher amplitudes died from non-neurological causes.

CONCLUSIONS

High-frequency SSEP components can reliably be studied in comatose CA patients using standard equipment.

SIGNIFICANCE

Late HFO burst amplitudes above 70nV largely exclude severe HE incompatible with regaining consciousness.

Detectability of the somatosensory evoked high frequency oscillation (HFO) co-recorded by scalp EEG and ECoG under propofol

Objective

The somatosensory evoked potential (SEP) elicited by median nerve stimulation consists of the N20 peak together with the concurrent high frequency oscillation (HFO, > 500 Hz). We describe the conditions for HFO detection in ECoG and scalp EEG in intraoperative recordings.

Methods

During neurosurgical interventions in six patients under propofol anesthesia, the SEP was recorded from subdural electrode strips (15 recordings) and from scalp electrodes (10/15 recordings). We quantified the spatial attenuation of the Signal-to-Noise Ratio (SNR) of N20 and HFO along the contacts of the electrode strip. We then compared the SNR of ECoG and simultaneous scalp EEG in a biophysical framework.

Results

HFO detection under propofol anesthesia was demonstrated. Visual inspection of strip cortical recordings revealed phase reversal for N20 in 14/15 recordings and for HFO in 10/15 recordings. N20 had higher maximal SNR (median 33.5 dB) than HFO (median 23 dB). The SNR of N20 attenuated with a larger spatial extent (median 7.2 dB/cm) than the SNR of HFO (median 12.3 dB/cm). We found significant correlations between the maximum SNR (rho = 0.58, p = 0.025) and the spatial attenuation (rho = 0.86, p < 0.001) of N20 and HFO. In 3/10 recordings we found HFO in scalp EEG. Based on the spatial attenuation and SNR in the ECoG, we estimated the scalp EEG amplitude ratio N20/HFO and found significant correlation with recorded values (rho = 0.65, p = 0.049).

Conclusions

We proved possible the intraoperative SEP HFO detection under propofol anesthesia. The spatial attenuation along ECoG contacts represents a good estimator of the area contributing to scalp EEG. The SNR and the spatial attenuation in ECoG recordings provide further insights for the prediction of HFO detectability in scalp EEG. The results obtained in this context may not be limited to SEP HFO, but could be generalized to biological signatures lying in the same SNR and frequency range.

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Somatosensory evoked HFOs can be recorded in ECoG and scalp EEG intraoperatively under propofol anesthesia.

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The HFO amplitude in ECoG attenuated to noise level over a smaller spatial scale than the N20 amplitude.

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The spatial attenuation of the cortical SNR directly relates to the noise level and to the scalp EEG amplitude.

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Parameters extracted from ECoG allowed estimating the ratio of N20/HFO amplitude in scalp EEG.

Correlates of a single cortical action potential in the epidural EEG

To identify the correlates of a single cortical action potential in surface EEG, we recorded simultaneously epidural EEG and single-unit activity in the primary somatosensory cortex of awake macaque monkeys. By averaging over EEG segments coincident with more than hundred thousand single spikes, we found short-lived (≈ 0.5 ms) triphasic EEG deflections dominated by high-frequency components > 800 Hz. The peak-to-peak amplitude of the grand-averaged spike correlate was 80 nV, which matched theoretical predictions, while single-neuron amplitudes ranged from 12 to 966 nV. Combining these estimates with post-stimulus-time histograms of single-unit responses to median-nerve stimulation allowed us to predict the shape of the evoked epidural EEG response and to estimate the number of contributing neurons. These findings establish spiking activity of cortical neurons as a primary building block of high-frequency epidural EEG, which thus can serve as a quantitative macroscopic marker of neuronal spikes.

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Cortical spikes are coincident with short-lived (~ 0.5 ms) EEG deflections.

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Cortical spikes produce ~  80 nV epidural EEG deflections at a distance of ~ 5 mm.

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EEG potentials due to spikes are dominated by high-frequency (> 800 Hz) components.

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High-frequency (> 800 Hz) EEG is a genuine macroscopic marker of spiking activity.

Magnetoencephalography detection of high-frequency oscillations in the developing brain

Increasing evidence from invasive intracranial recordings suggests that the matured brain generates both physiological and pathological high-frequency signals. The present study was designed to detect high-frequency brain signals in the developing brain using newly developed magnetoencephalography (MEG) methods. Twenty healthy children were studied with a high-sampling rate MEG system. Functional high-frequency brain signals were evoked by electrical stimulation applied to the index fingers. To determine if the high-frequency neuromagnetic signals are true brain responses in high-frequency range, we analyzed the MEG data using the conventional averaging as well as newly developed time-frequency analysis along with beamforming. The data of healthy children showed that very high-frequency brain signals (>1000 Hz) in the somatosensory cortex in the developing brain could be detected and localized using MEG. The amplitude of very high-frequency brain signals was significantly weaker than that of the low-frequency brain signals. Very high-frequency brain signals showed a much earlier latency than those of a low-frequency. Magnetic source imaging (MSI) revealed that a portion of the high-frequency signals was from the somatosensory cortex, another portion of the high-frequency signals was probably from the thalamus. Our results provide evidence that the developing brain generates high-frequency signals that can be detected with the non-invasive technique of MEG. MEG detection of high-frequency brain signals may open a new window for the study of developing brain function.

Non-invasive single-trial EEG detection of evoked human neocortical population spikes

QUESTION

Human high-frequency (>400 Hz) components of somatosensory evoked potentials (hf-SEPs), which can be recorded non-invasively at the scalp, are generated by cortical population spikes, as inferred from microelectrode recordings in non-human primates. It is a critical limitation to broader neurophysiological study of hf-SEPs in that hundreds of responses have to be averaged to detect hf-SEPs reliably. Here, we establish a framework for detecting human hf-SEPs non-invasively in single trials.

METHODS

Spatio-temporal features were extracted from band-pass filtered (400-900 Hz) hf-SEPs by bilinear Common Spatio-Temporal Patterns (bCSTP) and then classified by a weighted Extreme Learning Machine (w-ELM). The effect of varying signal-to-noise ratio (SNR), number of trials, and degree of w-ELM re-weighting was characterized using surrogate data. For practical demonstration of the algorithm, median nerve hf-SEPs were recorded inside a shielded room in four subjects, spanning the hf-SEP signal-to-noise ratio characteristic for a larger population, utilizing a custom-built 29-channel low-noise EEG amplifier.

RESULTS

Using surrogate data, the SNR proved to be pivotal to detect hf-SEPs in single trials efficiently, with the trade-off between sensitivity and specificity of the algorithm being obtained by the w-ELM re-weighting parameter. In practice, human hf-SEPs were detected non-invasively in single trials with a sensitivity of up to 99% and a specificity of up to 97% in two subjects, even without any recourse to knowledge of stimulus timing. Matching with the results of the surrogate data analysis, these rates dropped to 62-79% sensitivity and 18-31% specificity in two subjects with lower SNR.

CONCLUSIONS

Otherwise buried in background noise, human high-frequency EEG components can be extracted from low-noise recordings. Specifically, refined supervised filter optimization and classification enables the reliable detection of single-trial hf-SEPs, representing non-invasive correlates of cortical population spikes.

SIGNIFICANCE

While low-frequency EEG reflects summed postsynaptic potentials, and thereby neuronal input, we suggest that high-frequency EEG (>400 Hz) can provide non-invasive access to the unaveraged output of neuronal computation, i.e., single-trial population spike activity evoked in the responsive neuronal ensemble.

Recording human cortical population spikes non-invasively--An EEG tutorial

BACKGROUND

Non-invasively recorded somatosensory high-frequency oscillations (sHFOs) evoked by electric nerve stimulation are markers of human cortical population spikes. Previously, their analysis was based on massive averaging of EEG responses. Advanced neurotechnology and optimized off-line analysis can enhance the signal-to-noise ratio of sHFOs, eventually enabling single-trial analysis.

METHODS

The rationale for developing dedicated low-noise EEG technology for sHFOs is unfolded. Detailed recording procedures and tailored analysis principles are explained step-by-step. Source codes in Matlab and Python are provided as supplementary material online.

RESULTS

Combining synergistic hardware and analysis improvements, evoked sHFOs at around 600 Hz ('σ-bursts') can be studied in single-trials. Additionally, optimized spatial filters increase the signal-to-noise ratio of components at about 1 kHz ('κ-bursts') enabling their detection in non-invasive surface EEG.

CONCLUSIONS

sHFOs offer a unique possibility to record evoked human cortical population spikes non-invasively. The experimental approaches and algorithms presented here enable also non-specialized EEG laboratories to combine measurements of conventional low-frequency EEG with the analysis of concomitant cortical population spike responses.

Methodological aspects of EEG and body dynamics measurements during motion

EEG involves the recording, analysis, and interpretation of voltages recorded on the human scalp which originate from brain gray matter. EEG is one of the most popular methods of studying and understanding the processes that underlie behavior. This is so, because EEG is relatively cheap, easy to wear, light weight and has high temporal resolution. In terms of behavior, this encompasses actions, such as movements that are performed in response to the environment. However, there are methodological difficulties which can occur when recording EEG during movement such as movement artifacts. Thus, most studies about the human brain have examined activations during static conditions. This article attempts to compile and describe relevant methodological solutions that emerged in order to measure body and brain dynamics during motion. These descriptions cover suggestions on how to avoid and reduce motion artifacts, hardware, software and techniques for synchronously recording EEG, EMG, kinematics, kinetics, and eye movements during motion. Additionally, we present various recording systems, EEG electrodes, caps and methods for determinating real/custom electrode positions. In the end we will conclude that it is possible to record and analyze synchronized brain and body dynamics related to movement or exercise tasks.

Size of cortical generators of epileptic interictal events and visibility on scalp EEG

Growing evidence indicates that fast oscillations (>80 Hz) can be recorded interictally in the scalp EEG of patients with epilepsy, and that they may point to the seizure-onset zone. However, mechanisms underpinning the emergence of scalp fast oscillations, and whether they differ from those of interictal epileptic discharges (IEDs), are yet to be understood. The visibility of cortical electric activity on scalp EEG recordings is dependent on two factors: the characteristics of the cortical generator and the background level. We studied this issue using scalp EEG recordings and detailed simulations, with a finite element model including 8 million elements and 8 different tissues. We observed an almost linear relationship between the amplitude of scalp electric potential and the extent of the generator on the cortex. However, this relationship is subject to substantial variability, with variations in factors greater than 3 occurring simply by changing the location on the cortex of generators of fixed extent. In addition, we showed that the background power in scalp EEG recordings decreases at higher frequency bands, being inversely proportional to a power of 2.5 of the frequency. In the specific case of fast oscillations, they can be detected within the lower noise level of the ripple band (80-200 Hz) even though their median amplitude on scalp EEG recordings is more than 10 times smaller than IEDs and consistent with cortical generators of approximately 1 cm(2). In conclusion, the physics governing the propagation of electrical activity from the brain to the scalp are consistent with the hypothesis that scalp fast oscillations and intracranial high-frequency oscillations (HFOs, 80-500 Hz) are expressions of common generators. Given the potential role of HFOs as biomarkers in epilepsy, the possibility to obtain some of the associated information from scalp EEG is of high clinical significance.

Influence of spiking activity on cortical local field potentials

The intra-cortical local field potential (LFP) reflects a variety of electrophysiological processes including synaptic inputs to neurons and their spiking activity. It is still a common assumption that removing high frequencies, often above 300 Hz, is sufficient to exclude spiking activity from LFP activity prior to analysis. Conclusions based on such supposedly spike-free LFPs can result in false interpretations of neurophysiological processes and erroneous correlations between LFPs and behaviour or spiking activity. Such findings might simply arise from spike contamination rather than from genuine changes in synaptic input activity. Although the subject of recent studies, the extent of LFP contamination by spikes is unclear, and the fundamental problem remains. Using spikes recorded in the motor cortex of the awake monkey, we investigated how different factors, including spike amplitude, duration and firing rate, together with the noise statistic, can determine the extent to which spikes contaminate intra-cortical LFPs. We demonstrate that such contamination is realistic for LFPs with a frequency down to ∼10 Hz. For LFP activity below ∼10 Hz, such as movement-related potential, contamination is theoretically possible but unlikely in real situations. Importantly, LFP frequencies up to the (high-) gamma band can remain unaffected. This study shows that spike–LFP crosstalk in intra-cortical recordings should be assessed for each individual dataset to ensure that conclusions based on LFP analysis are valid. To this end, we introduce a method to detect and to visualise spike contamination, and provide a systematic guide to assess spike contamination of intra-cortical LFPs.