Beta Frequency Research Articles

Rhythmic beta-frequency TMS over human right parietal cortex strengthens visual size illusions.

Rhythmic brain activity has been proposed to structure visual processing. Here we investigated the causal contributions of parietal beta oscillations to context-dependent visual size perception, which is indicated by the classic Ebbinghaus and Ponzo illusions. On each trial, rhythmic transcranial magnetic stimulation (TMS) was applied over the left or right superior parietal lobule in a train of five pulses at beta frequency (20 Hz). Immediately after the last pulse of the stimulation train, participants were presented with the illusory configuration, and performed a size-matching task. The results revealed that right parietal stimulation significantly increased the magnitudes of both size illusions relative to control vertex stimulation, whereas the illusion effects were unaffected with left parietal stimulation. Moreover, the stimulation effect was not observed with right parietal TMS at theta frequency (5 Hz). The findings clearly demonstrate the functional relevance of beta oscillations for the implementation of cognitive performance, supporting the causal contribution of parietal cortex to the processing of visual size illusions.

Aberrant Functional Connectivity and Brain Network Organization in High-Schizotypy Individuals: An Electroencephalography Study.

Oscillatory synchrony plays a crucial role in establishing functional connectivity across distinct brain regions. Within the realm of schizophrenia, suggested to be a neuropsychiatric disconnection syndrome, discernible aberrations arise in the organization of brain networks. We aim to investigate whether the resting-state functional network is already altered in healthy individuals with high schizotypy traits, highlighting the pivotal influence of brain rhythms in driving brain network alterations. Two-minute resting-state electroencephalography recordings were conducted on healthy participants with low and high schizotypy scores. Subsequently, spectral Granger causality was used to compute functional connectivity in theta, alpha, beta, and gamma frequency bands, and graph theory metrics were employed to assess global and local brain network features. Results highlighted that high-schizotypy individuals exhibit a lower local efficiency in theta and alpha frequencies and a decreased global efficiency across theta, alpha, and beta frequencies. Moreover, high schizotypy is characterized by a lower nodes' centrality and a frequency-specific decrease of functional connectivity, with a reduced top-down connectivity mostly in slower frequencies and a diminished bottom-up connectivity in faster rhythms. These results show that healthy individuals with a higher risk of developing psychosis exhibit a less efficient functional brain organization, coupled with a systematic decrease in functional connectivity impacting both bottom-up and top-down processing. These frequency-specific network alterations provide robust support for the dimensional model of schizophrenia, highlighting distinctive neurophysiological signatures in high-schizotypy individuals.

Effect of 40 continuous connected breathing on electroencephalogram and heart rate variability of healthy volunteers

Objectives: Continuous connected breathing (CCB) is a high-frequency breathing technique without breath retention. This study examines changes in electroencephalogram (EEG) band power and heart rate variability (HRV) before, during and after breathwork intervention of 3 rounds of 40 CCB versus normal breathing in healthy volunteers. Materials and Methods: Thirty-three healthy volunteers, aged between 19 and 23 years, participated in the study (n = 15; female = 7; mean age = 21.5 years) group, while the control group consisted of 18 individuals (n = 18; male = 11; mean age = 19.6 years). After acquiring baseline measures, the study group engaged in 40 CCB practice online for 10 days, while the control group continued with their usual activities. EEG and HRV signals were recorded for both groups after the 10 days. Post-intervention, the study group practised 40 CCB breathwork, while the control group breathed normally during signal acquisition. EEG was recorded for 1-min durations at five different time events (1st, 4th, 5th, 7th and 10th min) according to the protocol. HRV was continuously recorded for the entire 10 min, and 1-min readings corresponding to the EEG recording time intervals were analysed. EEG signals were acquired using an Enobio-8 cap with dry electrodes in pre-frontal (FP1 and FP2), temporal (T7 and T8), central (C3 and C4) and occipital (O1 and O2) regions. HRV data were acquired from Polar H7/H10 chest strap sensors through Bluetooth using Elite HRV app on android phone and data were later processed with Kubios software. EEG signals were processed using MATLAB software to extract EEG power for delta (0– 3.5Hz), theta (4–7 Hz), alpha (8–13 Hz), beta (14–29 Hz) and gamma frequencies (30–50 Hz). The EEG power and HRV signals were later arranged as before-after and during time events and statistically analysed for within and between-group changes using t-tests for significance of means. Results: There was an overall quantitative change in EEG band power of −2 units within the study group and −0.03 within the control group. A significant decrease was found in overall delta (−2.27), theta (−1.7), alpha (−1.8) and beta (−2.27) brain waves in pre-frontal (FP1, FP8), temporal (T7, T8) and occipital (O1, O2) regions after breathwork compared to before within the study group. A significant increase was found in beta (+2.8) band power in the control group with a higher increase in the left compared to the right hemisphere. Bilateral average change ranged from −1.6 to −2.6 in the study and −0.1 to +4.9 in the control group. HRV parameters were reduced after intervention in both groups but without significance. The parameters improved after the intervention compared to during within the study group, though not significant. Conclusion: 10 days of practice of 40 CCB breathworks (3 rounds) for 5 min/day may reduce EEG power in delta, theta, alpha and beta waves. It may help to maintain autonomic balance with an increase of HRV in the recovery phase after an initial decrease during breathwork. Further studies are recommended to test the consistency of outcomes.

Cerebellar Oscillatory Patterns in Essential Tremor: Modulatory Effects of VIM-DBS

Essential tremor (ET) is a common movement disorder, and while ventral intermediate nucleus deep brain stimulation (VIM-DBS) is a well-established treatment, its precise mechanisms or modulatory effects, particularly in relation to cerebellar oscillations, remain unclear. In this study, we hypothesized that VIM-DBS would modulate cerebellar oscillatory activity across both resting and motor task conditions, reflecting its impact on cerebello-thalamic pathways. Ten patients diagnosed with ET participated in this study. We examined the effects of VIM-DBS on mid-cerebellar oscillations during resting-state and lower-limb pedaling motor tasks. Frequency analysis was conducted on the resting-state signal and time-frequency analysis was performed on motor task-related signals. We explored the modulatory effects of VIM-DBS on oscillatory activity across delta, theta, alpha, beta, and gamma frequency bands. We found that ON VIM-DBS increased mid-cerebellar relative theta power during resting-state conditions, with no significant changes in other frequency bands. During a pedaling motor task, VIM-DBS led to significant reductions in theta, alpha, and gamma power, highlighting the frequency-specific effects of stimulation. VIM-DBS also increased peak acceleration of leg movements during the pedaling task. Furthermore, VIM-DBS selectively increased mid-frontal relative theta and beta power as well as mid-occipital relative theta power during resting condition, suggesting localized mid-cerebellar modulation. Moreover, similarity analyses between mid-cerebellar and nearby mid-occipital signals revealed differences in coherence, phase coherence, and cross-spectrum phase coherence. Overall, these results support the role of VIM-DBS in modulating mid-cerebellar oscillations in ET and provide new insights into the neural mechanisms underlying DBS efficacy.

Beyond the neural underpinnings of action emulation in expert athletes: An EEG study.

Athletes specializing in sports demanding rapid predictions and hand-eye coordination are highly trained in predicting the consequences of motor commands. This can be framed as highly efficient action emulation, but the neural underpinnings of this remain elusive. We examined the neural processes linked to the training effect of athletes (4000h of training) by employing a continuous pursuit tracking task and EEG data. We manipulated feedback availability by intermittently occluding the cursor. As a performance measure, we used the distance between cursor and target (position error), the angle between the cursor and target movement direction (direction error) and the magnitude of cursor acceleration (acceleration error) to quantify movement strategy. In EEG data, we investigated beta, alpha, and theta frequency band oscillations. Athletes' position error is lower than non-athletes' when there is no feedback about the cursor location, but direction error is not. We found no quantitative power differences in the investigated frequency bands, but evidence that athletes and non-athletes accomplish action emulation through different functional neuroanatomical structures, especially when alpha and beta band activity is concerned. We surmise that non-athletes seemed to rely on top-down inhibitory control to predict guesses on cursor trajectories in the absence of cursor position feedback. In contrast, athletes might benefit from enhanced inhibitory gating mechanisms in the ventral stream and the integration of sensory and motor processes in the insular cortex, which could provide them with processing advantages in computing forward models. We further reflect that this advantage might be supported by alpha band activity in athletes' motor cortex, suggesting less inhibitory gating and a higher likelihood of executing integrated sensorimotor programs. We posit that current framings of neuroanatomical structures and neurophysiological processes in the action emulation framework must be revised to better capture superior motor performance.

High beta power in the ventrolateral prefrontal cortex indexes human approach behavior: a case study.

Deep brain stimulation (DBS) of the ventral capsule and ventral striatum (VC/VS) is an effective therapy for treatment resistant obsessive-compulsive disorder (trOCD). DBS initiation often produces acute improvements in mood and energy. These acute behavioral changes, which we refer to as "approach behaviors", include increased social engagement and talkativeness. We investigated the relationship between stimulation amplitude, spectral power in the ventrolateral prefrontal cortex (vlPFC), and speech rate in one male patient with trOCD implanted with bilateral VC/VS DBS leads and subdural electrodes adjacent to orbitofrontal cortex (OFC) and vlPFC. Several times over the first 17 weeks of therapy, we conducted experiments where we recorded data during epochs of high amplitude or zero/low amplitude stimulation. We found that both speech rate and vlPFC power in a high beta frequency band (31±1.5Hz, 1/f activity removed) increased during high amplitude as compared to low amplitude periods. Speech rate correlated with vlPFC high beta power. These effects were more consistent across time points in the left hemisphere than the right. At week 17, we performed an experiment where stimulation was held constant while the patient was asked to speak or remain silent. We showed that the presence or absence of speech was not sufficient to increase the vlPFC high beta power, suggesting stimulation is a key driver of the observed neurobehavioral phenomenon. Our results suggest vlPFC high beta power is a biomarker for approach behaviors associated with VC/VS DBS.Significance Statement In one patient receiving DBS of the ventral capsule and ventral striatum (VC/VS) for OCD, we leveraged a unique clinical opportunity to study the neurophysiological basis of approach behaviors using chronic intracranial recordings from prefrontal cortical regions. VC/VS DBS initiation often produces acute improvements in mood and energy associated with increased social engagement and talkativeness (approach behaviors). Our results suggest that vlPFC high beta activity (particularly in the left hemisphere) may index approach behaviors (quantified here by speech features). This neural signal is consistent with our previous non-invasive studies identifying predictors of mania in patients with bipolar disorder, and we hope to gather further evidence that it indexes a continuum from adaptive approach behavior to maladaptive manic symptoms.

Explainable AI for Bipolar Disorder Diagnosis Using Hjorth Parameters

Background: Despite the prevalence and severity of bipolar disorder (BD), current diagnostic approaches remain largely subjective. This study presents an automatic diagnostic framework using electroencephalography (EEG)-derived Hjorth parameters (activity, mobility, and complexity), aiming to establish objective neurophysiological markers for BD detection and provide insights into its underlying neural mechanisms. Methods: Using resting-state eyes-closed EEG data collected from 20 BD patients and 20 healthy controls (HCs), we developed a novel diagnostic approach based on Hjorth parameters extracted across multiple frequency bands. We employed a rigorous leave-one-subject-out cross-validation strategy to ensure robust, subject-independent assessment, combined with explainable artificial intelligence (XAI) to identify the most discriminative neural features. Results: Our approach achieved remarkable classification accuracy (92.05%), with the activity Hjorth parameters from beta and gamma frequency bands emerging as the most discriminative features. XAI analysis revealed that anterior brain regions in these higher frequency bands contributed most significantly to BD detection, providing new insights into the neurophysiological markers of BD. Conclusions: This study demonstrates the exceptional diagnostic utility of Hjorth parameters, particularly in higher frequency ranges and anterior brain regions, for BD detection. Our findings not only establish a promising framework for automated BD diagnosis but also offer valuable insights into the neurophysiological basis of bipolar and related disorders. The robust performance and interpretability of our approach suggest its potential as a clinical tool for objective BD diagnosis.

Transient cortical beta-frequency oscillations associated with contextual novelty in high density mouse EEG

Beta-frequency oscillations (20–30 Hz) are prominent in both human and rodent electroencephalogram (EEG) recordings. Discrete epochs of beta (or Beta2) oscillations are prevalent in the hippocampus and other brain areas during exploration of novel environments. However, little is known about the spatial distribution and temporal relationships of beta oscillations across the cortex in response to novel contexts. To investigate this, mice fitted with 30-channel EEG-style multi-electrode arrays underwent a single recording session in a novel environment. While changes to spectral properties of cortical oscillations were minimal, there was a profound increase in the rate of beta bursts during the initial part of the recording session, when the environment was most novel. This was true across the cortex but most notable in recording channels situated above the retrosplenial cortex. Additionally, novelty was associated with greater connectivity between retrosplenial areas and the rest of the cortex, specifically in the beta frequency range. However, it was also found that the cortex in general, is highly modulated by environmental novelty. This data further suggests the retrosplenial cortex is an important hub for distinguishing environmental context and highlights the diversity of functions for beta oscillations across the brain, which can be observed using high-density EEG.

Spatial-Frequency Based EEG Features for Classification of Human Emotions

Human emotion classification without bias and unfairness is challenging because most existing image-based methods are directly or indirectly affected by subjectivity. Therefore, we propose an EEG (Electroencephalogram) based model for an accurate emotion classification without the effect of subjectivity. The captured EEG signals are converted into Delta, Theta, Alpha, Beta, and Gama frequency bands. As emotions change, the frequency bands change and provide unique patterns for each emotion irrespective of different persons. With this observation, the statical features, namely, mean, standard deviation, variance, and kurtosis, and frequency-based features, namely, Power Spectral Density (PSD) and Petrosian Fractal Dimension (PFD) are extracted. To integrate the strength of spatial and frequency-based features, the features are supplied to quadratic discriminative analysis for the final classification. The experiments on the benchmark datasets, DEAP and SEED-IV, achieve 99.40% and 91.97% accuracy, respectively. A comparison with state-of-the-art methods shows that the method performs very well on some datasets.

The role of the somatosensory cortex in self-paced movement impairment in Parkinson's disease.

The aim of this work was to study the differences at the whole-brain level between self-paced and cued movement processing in Parkinson's disease (PD). High density electroencephalogram (HD-EEG) was recorded during the performance of self-paced movements (Bereitschaftspotential - BP) and visually cued movements (VMT) in PD patients (n=38) and in a group of healthy controls (HC, n=23). Oscillatory changes in the alpha, beta, and gamma frequencies were evaluated and correlated to the clinical scales- MDS-UPDRS and Freezing of Gait Questionnaire (FOGQ). The main difference in the alpha range was an activation in the basal ganglia area during VMT performance as compared to BP performance; this activation was present only in HC. The most important finding was observed in the high beta range: a higher activation of the right postcentral area during BP performance in PD subjects as compared to HC, correlating to the severity of FOG. Moreover, PD patients had lower gamma activation of the right frontal areas. A simplification of motor circuits and a hyperactivation of the right somatosensory cortex were observed in PD subjects. Future studies should be focused on this area to confirm or disprove its role in FOG.

Analysis of Connectivity in Electromyography Signals to Examine Neural Correlations in the Activation of Lower Leg Muscles for Postural Stability: A Pilot Study.

In quiet standing, the central nervous system implements a pre-programmed ankle strategy of postural control to maintain upright balance and stability. This strategy comprises a synchronized common neural drive delivered to synergistically grouped muscles. This study evaluated connectivity between EMG signals of the unilateral and bilateral homologous muscle pairs of the lower legs during various standing balance conditions using magnitude-squared coherence (MSC). The leg muscles examined included the right and left tibialis anterior (TA), medial gastrocnemius (MG), and soleus (S). MSC is a frequency domain measure that quantifies the linear phase relation between two signals and was analyzed in the alpha (8-13 Hz), beta (13-30 Hz), and gamma (30-100 Hz) neural frequency bands for feet together and feet tandem, with eyes open and eyes closed conditions. Results showed that connectivity in the beta and lower and upper gamma bands (30-100 Hz) was influenced by standing balance conditions and was indicative of a neural drive originating from the motor cortex. Instability was evaluated by comparing less stable standing conditions with a baseline-eyes open feet together stance. Changes in connectivity in the beta and gamma bands were found to be most significant in the muscle pairs of the back leg during a tandem stance regardless of dominant foot placement. MSC identified the MG:S muscle pair as significant for the right and left leg. The results of this study provided insight into the neural mechanism of postural control.

Alterations in resting-state EEG functional connectivity in patients with major depressive disorder receiving electroconvulsive therapy: A systematic review.

Electroconvulsive therapy (ECT) is highly efficacious for the treatment of major depressive disorder (MDD), but its mechanisms still require clarification. Even though depression is associated with alterations in functional connectivity (FC), EEG studies investigating effects of ECT on FC have not been systematically reviewed. Understanding these effects may help to identify the role of functional brain circuits in depression and its remission. This systematic review aimed to synthesize EEG studies investigating FC changes in ECT-treated patients with depression. A systematic literature search was conducted following PRISMA guidelines. Peer-reviewed studies on pre-to post-ECT resting-state EEG FC changes in adult patients with MDD were included. Three of 143 studies were included, of which two reported reduced FC in the alpha and beta frequency bands and increased theta band FC in patients with ECT-treated MDD. Changes in alpha band FC were associated with treatment outcomes. Patients with MDD exhibit increased electrophysiological resting-state alpha band FC, particularly frontally, compared with healthy subjects. Thus, ECT-induced decrease might indicate a trend toward normalization of oscillatory brain rhythms. As brain oscillations have been proposed to be involved in neuronal synchronization, which is important for communication between networks, the potential restoration in patients with depression and the association of FC changes with clinical improvement may indicate a potential mechanism of action of ECT. Understanding ECT's underlying mechanisms might ultimately enable treatment optimization, thus enhancing patient care. However, the number of studies is limited, with low-to-moderate EEG study quality, small sample sizes, and different electrophysiological FC measures.

Spontaneous slow cortical potentials and brain oscillations independently influence conscious visual perception.

Perceptual awareness results from an intricate interaction between external sensory input and the brain's spontaneous activity. Pre-stimulus ongoing activity influencing conscious perception includes both brain oscillations in the alpha (7 to 14 Hz) and beta (14 to 30 Hz) frequency ranges and aperiodic activity in the slow cortical potential (SCP, <5 Hz) range. However, whether brain oscillations and SCPs independently influence conscious perception or do so through shared mechanisms remains unknown. Here, we addressed this question in 2 independent magnetoencephalography (MEG) data sets involving near-threshold visual perception tasks in humans using low-level (Gabor patches) and high-level (objects, faces, houses, animals) stimuli, respectively. We found that oscillatory power and large-scale SCP activity influence conscious perception through independent mechanisms that do not have shared variance. In addition, through mediation analysis, we show that pre-stimulus oscillatory power and SCP activity have different relations to pupil size-an index of arousal-in their influences on conscious perception. Together, these findings suggest that oscillatory power and SCPs independently contribute to perceptual awareness, with distinct relations to pupil-linked arousal.

Electroencephalographic markers in Major Depressive Disorder: insights from absolute, relative power, and asymmetry analyses.

Major Depressive Disorder (MDD) leads to dysfunction and impairment in neurological structures and cognitive functions. Despite extensive research, the pathophysiological mechanisms and effects of MDD on the brain remain unclear. This study aims to assess the impact of MDD on brain activity using EEG power spectral analysis and asymmetry metrics. EEG recordings were obtained from 48 patients with MDD and 78 healthy controls. The data were segmented into 2-second windows (1024 data points) and analyzed using the Welch method, an advanced variant of the Fast Fourier Transform (FFT). A Hanning time window with 50% overlap was applied to compute the modified periodogram. Absolute and relative power, along with asymmetry values in the theta, alpha, and beta frequency bands, were calculated. Patients with MDD exhibited significantly higher absolute and relative power in the theta and beta bands and decreased power in the alpha band compared to healthy controls. Asymmetry analysis revealed significant differences between symmetric channels in the theta band (F7-F8, C3-C4, T3-T4, T5-T6), alpha band (F7-F8, C3-C4, T3-T4, T5-T6, O1-O2), and beta band (C3-C4, T3-T4, T5-T6, P3-P4). The findings suggest that MDD affects brain mechanisms and cognitive functions, as evidenced by altered power values in the theta and alpha bands. Additionally, asymmetry values in theta, alpha, and beta bands may serve as potential biomarkers for MDD. This study highlights that beyond the commonly used alpha asymmetry, theta and beta asymmetry can also provide valuable insights into the neurophysiological effects of MDD, aligning with previous neuroimaging studies that indicate impairments in memory, attention, and neuroanatomical connectivity in MDD.

Diurnal fluctuations of subthalamic nucleus local field potentials follow naturalistic sleep-wake behavior in Parkinson's disease.

Deep brain stimulation (DBS) of the subthalamic nucleus (STN) may improve sleep dysfunction, a common non-motor symptom of Parkinson disease (PD). Improvement in motor symptoms correlates with DBS-suppressed local field potential (LFP) activity, particularly in the beta frequency (13 - 30 Hz). Although well-characterized in the short term, little is known about the innate progression of these oscillations across the sleep-wake cycle. We sought to characterize LFP fluctuations over several days and nights in the home setting in patients chronically treated with DBS. LFPs in the beta and alpha frequency range were recorded from the STN in 13 PD subjects (18 hemispheres) over 14.6 (interquartile range 4) days. Sleep and wake were determined by validated actigraphy. We calculated the mean difference between sleep and wakefulness in LFP power (µVp), probability density functions of normalized LFP, and the fraction of overlap between probability density histograms. STN LFPs showed a consistent fluctuation based on behavioral state. LFP power was higher during wakefulness than during sleep, with little overlap in the magnitude of LFP power between these two states. Delineation of subject activity patterns revealed that LFP variance by time of day was more strongly correlated at night. STN LFP fluctuations represent a useful measure to distinguish between sleep and wakefulness in PD. These fluctuations can be detected in the home setting using commercially available devices, including in patients who have been treated with DBS for years. This technology may lead to opportunities for closed-loop DBS therapy.

Subthalamic nucleus deep brain stimulation in the beta frequency range boosts cortical beta oscillations and slows down movement.

Recordings from Parkinson's disease (PD) patients typically show strong beta-band oscillations (13-35Hz), which can be modulated by deep brain stimulation (DBS). While high-frequency DBS (>100Hz) ameliorates motor symptoms and reduces beta activity in basal ganglia and motor cortex, the effects of low-frequency DBS (<30Hz) are less clear. Clarifying these effects is relevant for the debate about the role of beta oscillations in motor slowing, which might be causal or epiphenomenal. Here, we investigated how subthalamic nucleus (STN) beta-band DBS affects cortical beta oscillations and motor performance. We recorded the magnetoencephalogram of 14 PD patients (9 males) with DBS implants while on their usual medication. Following a baseline recording (DBS off), we applied bipolar DBS at beta frequencies (10, 16, 20, 26, and 30Hz) via the left electrode in a cyclic fashion, turning stimulation on (5s) and off (3s) repeatedly. For each frequency, cyclic stimulation was applied at rest and during right hand finger tapping. In the baseline recording, we observed a negative correlation between the strength of hemispheric beta power lateralization and the tap rate. Importantly, beta-band DBS accentuated the lateralization and reduced the tap rate proportionally. The change in lateralization was specific to the alpha/beta range (8-26Hz), outlasted stimulation and did not depend on the stimulation frequency, suggesting a remote induced response rather than entrainment. Our study demonstrates that cortical beta oscillations can be manipulated by STN beta-band DBS. Importantly, this manipulation appears to have consequences for motor performance, supporting a causal role of beta oscillations in motor control.Significance Statement The slowing of movement in Parkinson's disease is known to be related to pathologically enhanced neural oscillations in the beta range (13-35Hz). Whether these oscillations are the cause of motor slowing, however, remains debated. Here, we stimulated the brains of Parkinson patients in the beta range, using their implanted deep brain stimulator. Consistent with a causal effect, stimulation slowed down finger tapping. Additionally, the subcortical stimulation affected the hemispheric balance of beta oscillations in the remote motor cortices. The stronger the shift, the stronger was the slowing. These results suggest that subcortical beta-band stimulation causes motor slowing by boosting motor cortical beta oscillations, indicating that beta oscillations might indeed be "a bad boy in Parkinson's disease".

Characterizing autogenous and reactive obsessions using theta and beta oscillations under inhibitory demands.

Obsessive-Compulsive Disorder (OCD) is a heterogenous mental health condition that causes significant impairment and is often associated with poor treatment outcomes. The aim of the current study was to examine the association between electroencephalographic (EEG) oscillatory power during inhibitory task performance and obsessive-compulsive symptoms (OCS). OCS was assessed using the well-established the Autogenous-Reactive Obsession (AO-RO) model as the main framework to address its heterogeneous clinical manifestations. The severity of AO and RO, as primary outcome measures, was indexed using the Revised Obsessive Intrusion Inventory (ROII). Cognitive- and behavioral inhibition (CI; BI) tasks were administered while EEG data were recorded from an analogue sample of 63 undergraduate students with OCS assessed along a dimensional spectrum. Oscillatory power was computed from frontal-central electrodes Fz and Cz for theta and beta frequency bands, using event-related spectral perturbations (ERSPs), which were entered into two hierarchical linear regression models to predict the severity of AO and RO, respectively, while controlling for covariates (i.e., sex, age, ethnicity, anxiety, depression, worry, and behavioral task performance). Theta power during CI (Theta-CI) was the only significant EEG predictor of AO severity, whereas beta power during BI (Beta-BI) was the only significant EEG predictor of RO severity. These results suggest that AO severity is primarily associated with an overactive neural correlate of cognitive control, whereas RO severity is primarily associated with an overactive neural correlate of behavioral cancellation. These results agree with previous literature suggesting overactive band power representing the dysfunction within OCD. Theta-CI and Beta-BI may serve as potential biomarkers differentially associated with AO and RO among undiagnosed individuals displaying varying levels of OCS, which warrants further investigation.

Forearm Bio-Medical Signal Processing

This research utilizes low-dimensional surface EMG and EEG data, obtained from the human arm using ECG electrodes, to analyze forearm muscle signals through a novel approach. Both EMG and EEG signals are employed side by side: EEG captures brain activity, particularly in the beta (13-30 Hz) and alpha (8-12 Hz) frequency ranges, while EMG focuses on muscle activity in the 20 Hz to 200 Hz range. Beta waves are associated with motor planning and voluntary movements, while alpha waves decrease during movement execution, indicating disengagement from a resting state. Event-related desynchronization (ERD) of alpha and beta waves is vital in understanding motor tasks, including forearm movements. Although EEG alone showed a poor response in tracking movement execution, EMG provided better performance, with higher frequencies reflecting more intense muscle contractions and motor unit activation. The combination of EEG and EMG improves the analysis of event-related potentials (ERPs) in timing tasks, enabling precise monitoring of both brain and muscle activity. However, coordinating both signals can be complex and often relies on user experience. Extensive experimentation confirms that using this dual approach is feasible and effective in replicating natural arm movements. The findings highlight important advancements in applying signal processing techniques to muscle activity analysis, providing new insights into the control of muscles and enhancing accessibility to modern assistive technologies.

Surfing beta burst waveforms to improve motor imagery-based BCI

Abstract Our understanding of motor-related, macroscale brain processes has been significantly shaped by the description of the event-related desynchronization (ERD) and synchronization (ERS) phenomena in the mu and beta frequency bands prior to, during, and following movement. The demonstration of reproducible, spatially- and band-limited signal power changes has, consequently, attracted the interest of non-invasive brain-computer interface (BCI) research for a long time. BCIs often rely on motor imagery (MI) experimental paradigms that are expected to generate brain signal modulations analogous to movement-related ERD and ERS. However, a number of recent neuroscience studies has questioned the nature of these phenomena. Beta band activity has been shown to occur, on a single-trial level, in short, transient, and heterogeneous events termed bursts rather than sustained oscillations. In a previous study, we established that an analysis of hand MI binary classification tasks based on beta bursts can be superior to beta power in terms of classification score. In this article, we elaborate on this idea, proposing a signal processing algorithm that is comparable to- and compatible with state-of-the-art techniques. Our pipeline filters brain recordings by convolving them with kernels extracted from beta bursts and then applies spatial filtering before classification. This data-driven filtering allowed for a simple and efficient analysis of signals from multiple sensors, thus being suitable for online applications. By adopting a time-resolved decoding approach, we explored MI dynamics and showed the specificity of the new classification features. In accordance with previous results, beta bursts improved classification performance compared to beta band power, while often increasing information transfer rate compared to state-of-the-art approaches.

Discovering EEG biomarkers of Lennox-Gastaut syndrome through unsupervised time-frequency analysis.

The discovery and validation of electroencephalography (EEG) biomarkers often rely on visual identification of waveforms. However, bias toward visually striking events restricts the search space for new biomarkers, and low interrater reliability can limit rigorous validation. We present a data-driven approach to biomarker discovery called scalp EEG Pattern Identification and Categorization (s-EPIC), which enables automated, unsupervised identification of EEG waveforms. S-EPIC is validated on Lennox-Gastaut syndrome (LGS), an epilepsy that is difficult to diagnose and assess due to its variable presentation and insidious evolution of symptoms. We retrospectively collected 10-min scalp EEG clips during non-rapid eye movement (NREM) sleep from 20 subjects with LGS and 20 approximately age-matched healthy controls. For s-EPIC, EEG events of interest (EOIs) were detected in all subjects using time-frequency analysis. The 11 705 EOIs were characterized based on 11 features and were collectively grouped using both k-means clustering and feature categorization. To provide clinical context, 1350 EOIs were visually reviewed and classified by three epileptologists. s-EPIC identified four clusters as candidate biomarkers of LGS, each having significantly more LGS EOIs than control EOIs. Two clusters contained EOIs resembling known LGS biomarkers such as interictal epileptiform discharges and generalized paroxysmal fast activity. The other two LGS-associated EEG clusters contained short bursts of power in beta and gamma frequency bands that were primarily unrecognized by epileptologists. This approach also uncovered significant differences in sleep spindles between LGS and control cohorts. s-EPIC provides a quantitative approach to waveform identification that could be broadly applied to EEG from both healthy subjects and those with suspected pathology. s-EPIC can objectively identify and characterize relevant EEG waveforms without visual review or assumptions about the waveform's morphology and could therefore be a powerful tool for the discovery and refinement of EEG biomarkers.