Electroencephalography Data Research Articles

Five-week music therapy improves overall symptoms in schizophrenia by modulating theta and gamma oscillations.

Some clinical studies have shown that music therapy as an adjunctive therapy can improve overall symptoms in patients with schizophrenia. However, the neural mechanisms of this improvement remain unclear due to insufficient neuroimaging evidence. In this work, 17 patients with schizophrenia accepted a five-week music therapy (music group) that integrated listening, singing, and composing, and required patients to cooperate in a group to complete music therapy tasks. Meanwhile, 15 patients with schizophrenia received a five-week visual art intervention as the control group including handicraft and painting activities. We collected the Manchester Scale (MS) and Positive and Negative Symptom Scale (PANSS) scores and electroencephalography (EEG) data before and after intervention in two groups. Behavioral results showed that both interventions mentioned above can effectively help patients with schizophrenia relieve their overall symptoms, while a trend-level effect was observed in favor of music therapy. The EEG results indicated that music therapy can improve abnormal neural oscillations in schizophrenia which is reflected by a decrease in theta oscillation in the parietal lobe and an increase in gamma oscillation in the prefrontal lobe. In addition, correlation analysis showed that in the music group, both reductions in theta oscillations in the parietal lobe and increases in gamma oscillations in the prefrontal lobe were positively correlated with the improvement of overall symptoms. These findings help us to better understand the neural mechanisms by which music therapy improves overall symptoms in schizophrenia and provide more evidence for the application of music therapy in other psychiatric disorders.

The effects of mental fatigue on fine motor performance in humans and its neural network connectivity mechanism: a dart throwing study.

While it is well known that mental fatigue impairs fine motor performance, the investigation into its neural basis remains scant. Here, we investigate the impact of mental fatigue on fine motor performance and explore its underlying neural network connectivity mechanisms. A total of 24 healthy male university students were recruited and randomly divided into two groups: a mental fatigue group (MF) and a control group (Control). Both groups completed 50 dart throws, while electroencephalography (EEG) data were collected. Following the Stroop intervention, participants in the MF group exhibited a decrease in Stroop task accuracy and throwing performance, and an increase in reaction time along with VAS and NASA scores. The EEG data during dart-throwing revealed that the network connectivity strength of theta oscillations in the frontal and left central regions was significantly higher in the MF group compared with the Control group, while the network connectivity strength of alpha oscillations in the left parietal region was significantly enhanced. The interregional connectivity within the theta and alpha rhythm bands, particularly in the frontal-central-parietal network connections, also showed a significant increase in the MF group. Mental fatigue impairs dart throwing performance and is accompanied by increased connectivity in alpha and theta.

A tutorial on fitting joint models of M/EEG and behavior to understand cognition

We present motivation and practical steps necessary to find parameter estimates of joint models of behavior and neural electrophysiological data. This tutorial is written for researchers wishing to build joint models of human behavior and scalp and intracranial electroencephalographic (EEG) or magnetoencephalographic (MEG) data, and more specifically those researchers who seek to understand human cognition. Although these techniques could easily be applied to animal models, the focus of this tutorial is on human participants. Joint modeling of M/EEG and behavior requires some knowledge of existing computational and cognitive theories, M/EEG artifact correction, M/EEG analysis techniques, cognitive modeling, and programming for statistical modeling implementation. This paper seeks to give an introduction to these techniques as they apply to estimating parameters from neurocognitive models of M/EEG and human behavior, and to evaluate model results and compare models. Due to our research and knowledge on the subject matter, our examples in this paper will focus on testing specific hypotheses in human decision-making theory. However, most of the motivation and discussion of this paper applies across many modeling procedures and applications. We provide Python (and linked R) code examples in the tutorial and appendix. Readers are encouraged to try the exercises at the end of the document.

Neurophysiological evidence that frontoparietal connectivity and GABA-A receptor changes underpin the antidepressant response to ketamine

Revealing the acute cortical pharmacodynamics of an antidepressant dose of ketamine in humans with depression is key to determining the specific mechanism(s) of action for alleviating symptoms. While the downstream effects are characterised by increases in plasticity and reductions in depressive symptoms—it is the acute response in the brain that triggers this cascade of events. Computational modelling of cortical interlaminar and cortico-cortical connectivity and receptor dynamics provide the opportunity to interrogate this question using human electroencephalography (EEG) data recorded during a ketamine infusion. Here, resting-state EEG was recorded in a group of 30 patients with major depressive disorder (MDD) at baseline and during a 0.44 mg/kg ketamine dose comprising a bolus and infusion. Fronto-parietal connectivity was assessed using dynamic causal modelling to fit a thalamocortical model to hierarchically connected nodes in the medial prefrontal cortex and superior parietal lobule. We found a significant increase in parietal-to-frontal AMPA-mediated connectivity and a significant decrease in the frontal GABA time constant. Both parameter changes were correlated across participants with the antidepressant response to ketamine. Changes to the NMDA receptor time constant and inhibitory intraneuronal input into superficial pyramidal cells did not survive correction for multiple comparisons and were not correlated with the antidepressant response. These results provide evidence that the antidepressant effects of ketamine may be mediated by acute fronto-parietal connectivity and GABA receptor dynamics. Furthermore, it supports the large body of literature suggesting the acute mechanism underlying ketamine’s antidepressant properties is related to GABA-A and AMPA receptors rather than NMDA receptor antagonism.

Long-term prognosis of patients with photosensitive idiopathic generalized epilepsy

ObjectiveThe long-term prognosis of photosensitive idiopathic generalized epilepsy (p-IGE) is generally considered favorable; however, its specific characteristics remain unclear. Our objective was to investigate the extended prognosis of p-IGE. MethodsWe analyzed the demographics, clinical, and electroencephalographic (EEG) data of consecutive patients who were diagnosed as having p-IGE, who were under follow-up for a minimum of 10 years and exhibited a photoparoxysmal response (PPR) in their EEGs. Prognostic data, epilepsy course types, and electroclinical variables were compared using appropriate statistical methods. ResultsThe mean follow-up duration for 108 consecutive patients with p-IGE (74.1 % female) was 16.8 ± 6.5 years. The main syndromes within this cohort included juvenile myoclonic epilepsy (37 %), juvenile absence epilepsy (15.7 %), and epilepsy with eyelid myoclonia (EEM) (14.8 %). In terms of epilepsy course types, 27.8 % were in the relapse-remission group, and 13.9 % had never experienced remission. A low early remission rate (5.6 %) was evident, with the remaining half of the cohort categorized as the late remission group. Several significant poor prognostic factors were identified including self-induction, clinical symptoms accompanying PPR, asynchrony and focal findings in EEG discharges, a wide frequency range of PPR, the coexistence of three seizure types, the presence of accompanying focal seizure features, and a history of convulsive status epilepticus. ConclusionsOur long-term follow-up study, conducted within a substantial p-IGE group, unveiled newly proposed course types within this epilepsy category and highlighted significant poor prognostic factors related to photosensitivity. These findings furnish valuable insights for precise prognosis counselling and effective management strategies for patients with p-IGE.

Quantitative Electroencephalography in Children With Autism Spectrum Disorders

The goal of this study is to compare the brain connectivity patterns of autistic patients with those of children who are developing normally and analyzing quantitative electroencephalography in children with autism. The study included 50 children who were developing normally and 60 preschoolers who met the DSM-V criteria for autism spectrum disorder. Routine and quantitative electroencephalography were carried out on each subject, as well as a Childhood Autism Rating Scale. The electroencephalograms of 76.67% of autistic children were normal; 6.67% showed focal changes, and 16.67% showed generalized changes. While the alpha power in the central and temporal areas is significantly lower in autistic children, it is unchanged in the frontal and parieto-occipital regions. Children with autism have significantly higher absolute delta and theta band activity both globally and locally. It was shown that the total and regional absolute delta and theta power activity had a significant positive correlation with the disease severity score. Quantitative electroencephalography is a more effective tool for assessing and diagnosing children with autism spectrum disorder because it shows abnormalities in all autistic children. A correlation exists between the quantitative electroencephalography data and the disease severity score.

Cognitive, psychological, and physiological effects of a web-based mindfulness intervention in older adults during the COVID-19 pandemic: an open study

BackgroundThe development of effective strategies to maintain good mental health of older adults is a public health priority. Mindfulness-based interventions have the potential to improve psychological well-being and cognitive functions of older adults, but little is known about the effect of such interventions when delivered through internet. During the COVID-19 pandemic we evaluated short- and long-term cognitive, psychological, and physiological effects of a mindfulness-based intervention (MBI) delivered via web-based videoconference in healthy older adults.MethodsFifty older adults participated in an 8-week MBI, which comprised structured 2-h weekly group sessions. A comprehensive evaluation encompassing cognitive (verbal memory, attention and processing speed, executive functions) and psychological assessments (depression and anxiety symptoms, mindfulness, worries, emotion regulation strategies, well-being, interoceptive awareness and sleep) was conducted. Additionally, electroencephalography (EEG) data were recorded before and after the MBI and at the 6-month follow-up (T6). Data were analyzed using an intention-to-treat approach, using linear mixed models adjusted for age. The effect size for time was computed as omega squared.ResultsWe observed significant improvements from pre-MBI to post-MBI and at the T6 across several measures. These improvements were notable in the areas of verbal memory (California Verbal Learning Test, p ≤ .007), attention and executive functions (Trail Making Test A and BA, p < .050), interoceptive awareness (Multidimensional Assessment of Interoceptive Awareness, p = .0002 for self-regulation and p < .05 for noticing, body listening, and trusting dimensions), and rumination (Heidelberg Form for Emotion Regulation Strategies, p = .018). These changes were associated with low to medium effect size. Moreover, we observed significant changes in EEG patterns, with a decrease in alpha1 (p = .004) and an increase in alpha2 (p < .0001) from pre-MBI to T6. Notably, improvements in TMTBA and rumination were correlated with the decrease in alpha1 (p < .050), while improvements in TMTA were linked to the increase in alpha2 (p = .025).ConclusionsThe results of our study show that a web-based MBI in older adults leads to improvements in cognitive and psychological measures, with associated modulations in specific brain rhythms. While these findings are promising, further controlled studies are required to validate these preliminary results.Trial registrationThe trial has been registered with the United States National Library of Medicine at the National Institutes of Health Registry of Clinical Trials under the code NCT05941143 on July 12, 2023.

Sleepy without stimulation: subjective and objective sleepiness in actigraphy-verified natural short sleepers.

Natural short sleepers (NSS)-individuals who report minimal sleepiness or daytime dysfunction despite habitually sleeping less than the recommended amount (i.e., <7 h)-are a focus of growing interest in sleep research. Yet, the predominance of research on NSS has relied on subjective reports of functionality. The present study examined subjective and objective sleepiness among actigraphy-verified NSS in comparison with recommended (7-9 h/day) length sleepers (RLS) who reported similarly minimal daytime dysfunction. The study tested the hypothesis that under conditions of low environmental stimulation, NSS have increased risk of drowsiness and sleep onset, regardless of perceived alertness. The NSS and RLS groups were identified via screening and verified with a 14 day assessment with actigraphy, sleep diaries, and morning ratings of sleep restoration. In-laboratory resting electroencephalography (EEG) data were analysed using a computerised EEG-based algorithm (Vigilance Algorithm Leipzig; VIGALL) to classify second-by-second changes in objective sleepiness ranging from cognitively active alertness to sleep onset. Results demonstrated that NSS exhibited significantly higher drowsiness and sleep onset ('microsleeps') across 15 min of resting EEG despite perceptions of lower subjective sleepiness compared to RLS. Findings suggest that irrespective of perceived sleep restoration and alertness, NSS appear to be at high risk of objective sleepiness that is rapidly unmasked under conditions of low environmental stimulation. Such apparent discrepancy between subjective and objective sleepiness has potentially important public health implications. Future research directions, including tests of mechanisms and tailored sleep extension intervention, are discussed.

Predictors for estimating subcortical EEG responses to continuous speech.

Perception of sounds and speech involves structures in the auditory brainstem that rapidly process ongoing auditory stimuli. The role of these structures in speech processing can be investigated by measuring their electrical activity using scalp-mounted electrodes. However, typical analysis methods involve averaging neural responses to many short repetitive stimuli that bear little relevance to daily listening environments. Recently, subcortical responses to more ecologically relevant continuous speech were detected using linear encoding models. These methods estimate the temporal response function (TRF), which is a regression model that minimises the error between the measured neural signal and a predictor derived from the stimulus. Using predictors that model the highly non-linear peripheral auditory system may improve linear TRF estimation accuracy and peak detection. Here, we compare predictors from both simple and complex peripheral auditory models for estimating brainstem TRFs on electroencephalography (EEG) data from 24 participants listening to continuous speech. We also investigate the data length required for estimating subcortical TRFs, and find that around 12 minutes of data is sufficient for clear wave V peaks (>3 dB SNR) to be seen in nearly all participants. Interestingly, predictors derived from simple filterbank-based models of the peripheral auditory system yield TRF wave V peak SNRs that are not significantly different from those estimated using a complex model of the auditory nerve, provided that the nonlinear effects of adaptation in the auditory system are appropriately modelled. Crucially, computing predictors from these simpler models is more than 50 times faster compared to the complex model. This work paves the way for efficient modelling and detection of subcortical processing of continuous speech, which may lead to improved diagnosis metrics for hearing impairment and assistive hearing technology.

Blink-Related Oscillations Provide Naturalistic Assessments of Brain Function and Cognitive Workload within Complex Real-World Multitasking Environments.

There is a significant need to monitor human cognitive performance in complex environments, with one example being pilot performance. However, existing assessments largely focus on subjective experiences (e.g., questionnaires) and the evaluation of behavior (e.g., aircraft handling) as surrogates for cognition or utilize brainwave measures which require artificial setups (e.g., simultaneous auditory stimuli) that intrude on the primary tasks. Blink-related oscillations (BROs) are a recently discovered neural phenomenon associated with spontaneous blinking that can be captured without artificial setups and are also modulated by cognitive loading and the external sensory environment-making them ideal for brain function assessment within complex operational settings. Electroencephalography (EEG) data were recorded from eight adult participants (five F, M = 21.1 years) while they completed the Multi-Attribute Task Battery under three different cognitive loading conditions. BRO responses in time and frequency domains were derived from the EEG data, and comparisons of BRO responses across cognitive loading conditions were undertaken. Simultaneously, assessments of blink behavior were also undertaken. Blink behavior assessments revealed decreasing blink rate with increasing cognitive load (p < 0.001). Prototypical BRO responses were successfully captured in all participants (p < 0.001). BRO responses reflected differences in task-induced cognitive loading in both time and frequency domains (p < 0.05). Additionally, reduced pre-blink theta band desynchronization with increasing cognitive load was also observed (p < 0.05). This study confirms the ability of BRO responses to capture cognitive loading effects as well as preparatory pre-blink cognitive processes in anticipation of the upcoming blink during a complex multitasking situation. These successful results suggest that blink-related neural processing could be a potential avenue for cognitive state evaluation in operational settings-both specialized environments such as cockpits, space exploration, military units, etc. and everyday situations such as driving, athletics, human-machine interactions, etc.-where human cognition needs to be seamlessly monitored and optimized.

Monitoring Disease Severity of Mild Cognitive Impairment from Single-Channel EEG Data Using Regression Analysis.

A deviation in the soundness of cognitive health is known as mild cognitive impairment (MCI), and it is important to monitor it early to prevent complicated diseases such as dementia, Alzheimer's disease (AD), and Parkinson's disease (PD). Traditionally, MCI severity is monitored with manual scoring using the Montreal Cognitive Assessment (MoCA). In this study, we propose a new MCI severity monitoring algorithm with regression analysis of extracted features of single-channel electro-encephalography (EEG) data by automatically generating severity scores equivalent to MoCA scores. We evaluated both multi-trial and single-trail analysis for the algorithm development. For multi-trial analysis, 590 features were extracted from the prominent event-related potential (ERP) points and corresponding time domain characteristics, and we utilized the lasso regression technique to select the best feature set. The 13 best features were used in the classical regression techniques: multivariate regression (MR), ensemble regression (ER), support vector regression (SVR), and ridge regression (RR). The best results were observed for ER with an RMSE of 1.6 and residual analysis. In single-trial analysis, we extracted a time-frequency plot image from each trial and fed it as an input to the constructed convolutional deep neural network (CNN). This deep CNN model resulted an RMSE of 2.76. To our knowledge, this is the first attempt to generate automated scores for MCI severity equivalent to MoCA from single-channel EEG data with multi-trial and single data.

Interictal EEG source connectivity to localize the epileptogenic zone in patients with drug-resistant epilepsy: A machine learning approach.

To deconstruct the epileptogenic networks of patients with drug-resistant epilepsy (DRE) using source functional connectivity (FC) analysis; unveil the FC biomarkers of the epileptogenic zone (EZ); and develop machine learning (ML) models to estimate the EZ using brief interictal electroencephalography (EEG) data. We analyzed scalp EEG from 50 patients with DRE who had surgery. We reconstructed the activity (electrical source imaging [ESI]) of virtual sensors (VSs) across the whole cortex and computed FC separately for epileptiform and non-epileptiform EEG epochs (with or without spikes). In patients with good outcome (Engel 1a), four cortical regions were defined: EZ (resection) and three non-epileptogenic zones (NEZs) in the same and opposite hemispheres. Region-specific FC features in six frequency bands and three spatial ranges (long, short, inner) were compared between regions (Wilcoxon sign-rank). We developed ML classifiers to identify the VSs in the EZ using VS-specific FC features. Cross-validation was performed using good outcome data. Performance was compared with poor outcomes and interictal spike localization. FC differed between EZ and NEZs (p < .05) during non-epileptiform and epileptiform epochs, showing higher FC in the EZ than its homotopic contralateral NEZ. During epileptiform epochs, the NEZ in the epileptogenic hemisphere showed higher FC than its contralateral NEZ. In good outcome patients, the ML classifiers reached 75% accuracy to the resection (91% sensitivity; 74% specificity; distance from EZ: 38 mm) using epileptiform epochs (gamma and beta frequency bands) and 62% accuracy using broadband non-epileptiform epochs, both outperforming spike localization (accuracy = 47%; p < .05; distance from EZ: 57 mm). Lower performance was seen in poor outcomes. We present an FC approach to extract EZ biomarkers from brief EEG data. Increased FC in various frequencies characterized the EZ during epileptiform and non-epileptiform epochs. FC-based ML models identified the resection better in good than poor outcome patients, demonstrating their potential for presurgical use in pediatric DRE.

Improving the detection of sleep slow oscillations in electroencephalographic data.

We aimed to build a tool which facilitates manual labeling of sleep slow oscillations (SOs) and evaluate the performance of traditional sleep SO detection algorithms on such a manually labeled data set. We sought to develop improved methods for SO detection. SOs in polysomnographic recordings acquired during nap time from ten older adults were manually labeled using a custom built graphical user interface tool. Three automatic SO detection algorithms previously used in the literature were evaluated on this data set. Additional machine learning and deep learning algorithms were trained on the manually labeled data set. Our custom built tool significantly decreased the time needed for manual labeling, allowing us to manually inspect 96,277 potential SO events. The three automatic SO detection algorithms showed relatively low accuracy (max. 61.08%), but results were qualitatively similar, with SO density and amplitude increasing with sleep depth. The machine learning and deep learning algorithms showed higher accuracy (best: 99.20%) while maintaining a low prediction time. Accurate detection of SO events is important for investigating their role in memory consolidation. In this context, our tool and proposed methods can provide significant help in identifying these events.

Morphometric magnetic resonance imaging (MRI) postprocessing in MRI-negative patients with first unprovoked seizure.

The aim of the study was to evaluate the benefits of morphometric magnetic resonance imaging (MRI) postprocessing in patients presenting with a first seizure and negative MRI results and to investigate these findings in the context of the clinical and electroencephalographic data, seizure recurrence rates, and epilepsy diagnosis in these patients. We retrospectively reviewed 97 MRI scans of patients with first unprovoked epileptic seizure and no evidence of epileptogenic lesion on clinical routine MRI. Morphometric Analysis Program (MAP; v2018), automated postprocessing software, was used to identify subtle, potentially epileptogenic lesions in the three-dimensional T1-weighted MRI data. The resulting probability maps were examined together with the conventional MRI images by a reviewer who remained blinded to the patients' clinical and electroencephalographical data. Clinical data were prospectively collected between February 2018 and May 2023. Among the apparently MRI-negative patients, a total of 18 of 97 (18.6%) showed cortical changes suggestive of focal cortical dysplasia. Within the population with positive MAP findings (MAP+), seizure recurrence rates were 61.1% and 66.7% at 1 and 2 years after the first unprovoked seizure, respectively. Conversely, patients with negative MAP findings (MAP-) had lower seizure recurrence rates of 27.8% and 34.2% at 1 and 2 years after the first unprovoked seizure, respectively. Patients with MAP+ findings were significantly more likely to be diagnosed with epilepsy than those patients with MAP- findings (χ2 [1, n = 97] = 14.820, p < .001, odds ratio = 21.371, 95% CI = 2.710-168.531) during a mean follow-up time of 22.51 months (SD = 16.7 months, range = 1-61 months). MRI postprocessing can be a valuable tool for detecting subtle epileptogenic lesions in patients with a first seizure and negative MRI results. Patients with first seizure and MAP+ findings had high seizure recurrence rates, meeting the criteria for beginning epilepsy.

Data augmentation for invasive brain–computer interfaces based on stereo-electroencephalography (SEEG)

Objective. Deep learning is increasingly used for brain–computer interfaces (BCIs). However, the quantity of available data is sparse, especially for invasive BCIs. Data augmentation (DA) methods, such as generative models, can help to address this sparseness. However, all the existing studies on brain signals were based on convolutional neural networks and ignored the temporal dependence. This paper attempted to enhance generative models by capturing the temporal relationship from a time-series perspective. Approach. A conditional generative network (conditional transformer-based generative adversarial network (cTGAN)) based on the transformer model was proposed. The proposed method was tested using a stereo-electroencephalography (SEEG) dataset which was recorded from eight epileptic patients performing five different movements. Three other commonly used DA methods were also implemented: noise injection (NI), variational autoencoder (VAE), and conditional Wasserstein generative adversarial network with gradient penalty (cWGANGP). Using the proposed method, the artificial SEEG data was generated, and several metrics were used to compare the data quality, including visual inspection, cosine similarity (CS), Jensen–Shannon distance (JSD), and the effect on the performance of a deep learning-based classifier. Main results. Both the proposed cTGAN and the cWGANGP methods were able to generate realistic data, while NI and VAE outputted inferior samples when visualized as raw sequences and in a lower dimensional space. The cTGAN generated the best samples in terms of CS and JSD and outperformed cWGANGP significantly in enhancing the performance of a deep learning-based classifier (each of them yielding a significant improvement of 6% and 3.4%, respectively). Significance. This is the first time that DA methods have been applied to invasive BCIs based on SEEG. In addition, this study demonstrated the advantages of the model that preserves the temporal dependence from a time-series perspective.

Theta oscillations within right dorsolateral prefrontal cortex contribute differently to speech versus limb inhibition.

Evidence suggests that speech and limb movement inhibition are subserved by common neural mechanisms, particularly within the right prefrontal cortex. In a recent study, we found that cathodal stimulation of right dorsolateral prefrontal cortex (rDLPFC) differentially modulated P3 event-related potentials for speech versus limb inhibition. In the present study, we further analyzed these data to examine the effects of cathodal high-definition transcranial direct current stimulation (HD-tDCS) over rDLPFC on frontal theta - an oscillatory marker of cognitive control - in response to speech and limb inhibition, during a Go/No-Go task in 21 neurotypical adults. Electroencephalography data demonstrated that both speech and limb No-Go elicited prominent theta activity over right prefrontal electrodes, with stronger activity for speech compared to limb. Moreover, we found that cathodal stimulation significantly increased theta power over right prefrontal electrodes for speech versus limb No-Go. Source analysis revealed that cathodal, but not sham, stimulation increased theta activity within rDLPFC and bilateral premotor cortex for speech No-Go compared to limb movement inhibition. These findings complement our previous report and suggest (1) right prefrontal theta activity is an amodal oscillatory mechanism supporting speech and limb inhibition, (2) larger theta activity in prefrontal electrodes for speech versus limb following cathodal stimulation may reflect allocation of additional neural resources for a more complex motor task, such as speech compared to limb movement. These findings have translational implications for conditions such as Parkinson's disease, wherein both speech and limb movement are impaired.

Effects of Background Noise and Linguistic Violations on Frontal Theta Oscillations During Effortful Listening.

Background noise and linguistic violations have been shown to increase the listening effort. The present study aims to examine the effects of the interaction between background noise and linguistic violations on subjective listening effort and frontal theta oscillations during effortful listening. Thirty-two normal-hearing listeners participated in this study. The linguistic violation was operationalized as sentences versus random words (strings). Behavioral and electroencephalography data were collected while participants listened to sentences and strings in background noise at different signal to noise ratios (SNRs) (-9, -6, -3, 0 dB), maintained them in memory for about 3 sec in the presence of background noise, and then chose the correct sequence of words from a base matrix of words. Results showed the interaction effects of SNR and speech type on effort ratings. Although strings were inherently more effortful than sentences, decreasing SNR from 0 to -9 dB (in 3 dB steps), increased effort rating more for sentences than strings in each step, suggesting the more pronounced effect of noise on sentence processing that strings in low SNRs. Results also showed a significant interaction between SNR and speech type on frontal theta event-related synchronization during the retention interval. This interaction indicated that strings exhibited higher frontal theta event-related synchronization than sentences at SNR of 0 dB, suggesting increased verbal working memory demand for strings under challenging listening conditions. The study demonstrated that the interplay between linguistic violation and background noise shapes perceived effort and cognitive load during speech comprehension under challenging listening conditions. The differential impact of noise on processing sentences versus strings highlights the influential role of context and cognitive resource allocation in the processing of speech.

A Multi Representation Deep Learning Approach for Epileptic Seizure Detection

Epileptic seizures, unpredictable in nature and potentially dangerous during activities like driving, pose significant risks to individual and public safety. Traditional diagnostic methods, which involve labour-intensive manual feature extraction from Electroencephalography (EEG) data, are being supplanted by automated deep learning frameworks. This paper introduces an automated epileptic seizure detection framework utilizing deep learning to bypass manual feature extraction. Our framework incorporates detailed pre-processing techniques: normalization via L2 normalization, filtering with an 80 Hz and 0,5 Hz Butterworth low-pass and high-pass filter, and a 50 Hz IIR Notch filter, channel selection based on standard deviation calculations and Mutual Information algorithm, and frequency domain transformation using FFT or STFT with Hann windows and 50% hop. We evaluated on two datasets: the first comprising 4 canines and 8 patients with 2.299 ictal, 23.445 interictal, and 32.915 test data, 400-5000Hz sampling rate across 16-72 channels; the second dataset, intended for testing, 733 icatal, 4.314 interictal, and 1908 test data, each 10 minutes long, recorded at 400Hz across 16 channels. Three deep learning architectures were assessed: CNN, LSTM, and a hybrid CNN-LSTM model-stems from their demonstrated efficacy in handling the complex nature of EEG data. Each model offers unique strengths, with the CNN excelling in spatial feature extraction, LSTM in temporal dynamics, and the hybrid model combining these advantages. The CNN model, comprising 31 layers, yielded highest accuracy, achieving 91% on the first dataset (precision 92%, recall 91%, F1-score 91%) and 82% on the second dataset using a 30-second threshold. This threshold was chosen for its clinical relevance. The research advances epileptic seizure detection using deep learning, indicating a promising direction for future medical technology. Future work will focus on expanding dataset diversity and refining methodologies to build upon these foundational results.

Evaluating Effects of Resting-State Electroencephalography Data Pre-Processing on a Machine Learning Task for Parkinson's Disease.

Resting-state electroencephalography pre-processing methods in machine learning studies into Parkinson's disease classification vary widely. Here three separate data sets were pre-processed to four different stages to investigate the effects on evaluation metrics, using power features from six regions-of-interest, Random Forest Classifiers for feature selection, and Support Vector Machines for classification. This showed muscle artefact inflated evaluation metrics, and alpha and theta band features produced the best results when fully pre-processing data.

The fusion of multi-omics profile and multimodal EEG data contributes to the personalized diagnostic strategy for neurocognitive disorders

BackgroundThe increasing prevalence of neurocognitive disorders (NCDs) in the aging population worldwide has become a significant concern due to subjectivity of evaluations and the lack of precise diagnostic methods and specific indicators. Developing personalized diagnostic strategies for NCDs has therefore become a priority.ResultsMultimodal electroencephalography (EEG) data of a matched cohort of normal aging (NA) and NCDs seniors were recorded, and their faecal samples and urine exosomes were collected to identify multi-omics signatures and metabolic pathways in NCDs by integrating metagenomics, proteomics, and metabolomics analysis. Additionally, experimental verification of multi-omics signatures was carried out in aged mice using faecal microbiota transplantation (FMT). We found that NCDs seniors had low EEG power spectral density and identified specific microbiota, including Ruminococcus gnavus, Enterocloster bolteae, Lachnoclostridium sp. YL 32, and metabolites, including L-tryptophan, L-glutamic acid, gamma-aminobutyric acid (GABA), and fatty acid esters of hydroxy fatty acids (FAHFAs), as well as disturbed biosynthesis of aromatic amino acids and TCA cycle dysfunction, validated in aged mice. Finally, we employed a support vector machine (SVM) algorithm to construct a machine learning model to classify NA and NCDs groups based on the fusion of EEG data and multi-omics profiles and the model demonstrated 92.69% accuracy in classifying NA and NCDs groups.ConclusionsOur study highlights the potential of multi-omics profiling and EEG data fusion in personalized diagnosis of NCDs, with the potential to improve diagnostic precision and provide insights into the underlying mechanisms of NCDs.69k9eiJKfEC56J752KW8KDVideo