EEG Recordings Research Articles

Efficient Metastability Mitigation and Sub-Band Filtering in VCO-Based EEG Recording

Efficient Metastability Mitigation and Sub-Band Filtering in VCO-Based EEG Recording

Intraoperative Frontal EEG Alpha Power is Associated with Post-Operative Mortality and Other Adverse Outcomes.

With estimated global post-operative mortality rates at 1-4% leading to approximately 3-12 million deaths per year, an urgent need exists for reliable measures of perioperative risk. Existing approaches suffer from poor performance, place a high burden on clinicians to gather data, or do not incorporate intraoperative data. Prior work demonstrated that intraoperative anesthetics induce prefrontal EEG oscillations in the alpha band (8-12Hz) that correlate with post-operative cognitive outcomes. We analyzed a retrospective cohort of 1,081 patients undergoing surgery with general anesthesia at Massachusetts General Hospital with intraoperative EEG recordings. The association between EEG alpha power and adverse outcomes were characterized using statistical models that were fitted on propensity weighted data. Our primary outcome was post-operative mortality, measured from date of surgery to date of death or last follow-up. Secondary outcomes included mortality within pre-specified time windows (30-days, 90-days, 180-days, and 1-year), hospital and PACU lengths of stay, discharge to long-term care, and 30-day hospital readmission. Alpha power was associated with mortality risk (HR = 0.92, 95% CI:[ 0.85, 0.99], p=0.039). Within specified time windows, alpha power was associated with 30-day mortality (OR = 0.81, 95% CI: [0.66, 0.95], p=0.010), 90-day mortality (OR = 0.68, 95% CI: [0.55, 0.79], p<0.001), 180-day mortality (OR = 0.75, 95% CI: [0.66, 0.83], p<0.001), and 1-year mortality (OR = 0.85, 95% CI: [0.79, 0.91], p<0.001). Additionally, alpha power was associated with discharge to long-term care (OR = 0.91, 95% CI: [0.86, 0.96], p<0.001). We did not find significant associations among alpha power and 30-day readmission and hospital or PACU lengths of stay. Intraoperative EEG alpha power is independently associated with post-operative mortality and adverse outcomes, suggesting it could represent a broad measure of post-operative physical resilience and provide clinicians with a low-burden, personalized measure of post-operative risk.

A Closed-Loop Ear-Worn Wearable EEG System with Real-Time Passive Electrode Skin Impedance Measurement for Early Autism Detection

Autism spectrum disorder (ASD) is a chronic neurological disorder with the severity directly linked to the diagnosis age. The severity can be reduced if diagnosis and intervention are early (age &lt; 2 years). This work presents a novel ear-worn wearable EEG system designed to aid in the early detection of ASD. Conventional EEG systems often suffer from bulky, wired electrodes, high power consumption, and a lack of real-time electrode–skin interface (ESI) impedance monitoring. To address these limitations, our system incorporates continuous, long-term EEG recording, on-chip machine learning for real-time ASD prediction, and a passive ESI evaluation system. The passive ESI methodology evaluates impedance using the root mean square voltage of the output signal, considering factors like pressure, electrode surface area, material, gel thickness, and duration. The on-chip machine learning processor, implemented in 180 nm CMOS, occupies a minimal 2.52 mm² of active area while consuming only 0.87 µJ of energy per classification. The performance of this ML processor is validated using the Old Dominion University ASD dataset.

Vetiveria zizanioides modulates experimental epilepsy-induced seizures, oxidative stress, and apoptosis in the brain of rats.

In this study, we investigated the protective actions of Vetiveria zizanioides oil (VET) against oxidative stress and apoptosis in a rat model of pentylenetetrazol (PTZ)-induced epilepsy model. Rats were divided into four groups: control (1ml/kg saline, by gavage, 7 days + 1ml/kg saline, i.p, single dose, 8th day), PTZ (1ml/kg saline, by gavage, 7 days + 60mg/kg, i.p, single dose, 8th day), PTZ + VET-200 (200mg/kg VET, by gavage, 7 days + 60mg/kg PTZ, i.p, single dose, 8th day), and PTZ + VET-400 (400mg/kg VET, by gavage, 7 days + 60mg/kg PTZ i.p, single dose, 8th day). Behavioral evaluation (Racine scale was used to classify the severity of seizures according to stages) and EEG recordings were taken. At the end of the experiment, the animals were sacrificed, and blood, hippocampus, and cerebral cortex tissues were removed for biochemical and histopathological examinations. PTZ injection increased the duration of the first epileptic spike and the total number of seizures and caused oxidative stress by increasing the total oxidant status (TOS). The treatment of PTZ induced neurodegenerative changes in the tissues such as increases of apoptosis, Bcl-2, Cyclin B1, and GABA expressions, but decreased Beta-tubulin. However, all the adverse changes of PTZ were modulated by the treatment of VET-200 and VET-400. In conclusion, these results showed that VET could ameliorate epileptic seiures, oxidative stress, and neuronal apoptosis in PTZ-induced seizures.

Spectral tuning and after-effects in neural entrainment

Neural entrainment has become a popular technique to non-invasively manipulate brain rhythms via external, periodic stimulation. However, there is still debate regarding its underlying mechanisms and effects on brain activity. Here, we used EEG recordings during a visual entrainment paradigm to assess characteristic changes in the spectral content of EEG signals due to entrainment. Our results demonstrate that entrainment not only increases synchrony between neural oscillations and the entraining stimulus but also elicits previously unreported spectral tuning effects and long-lasting after-effects. These findings offer compelling evidence for the presence of dedicated, flexible, and adaptive mechanisms for neural entrainment, which may have key roles in adjusting the sensitivity and dynamic range of brain oscillators in response to environmental temporal structures.

Sensitization and Habituation of Hyper-Excitation to Constant Presentation of Pattern-Glare Stimuli.

Pattern glare, associated with cortical hyperexcitability, induces visual distortions and discomfort, particularly in individuals susceptible to migraines or epilepsy. While previous research has primarily focused on transient EEG responses to patterned stimuli, this study aims to investigate how continuous presentation of pattern-glare stimuli affects neural adaptation over both fine (seconds) and coarse (entire experiment) temporal scales. EEG recordings were obtained from 40 healthy participants exposed to horizontal square-wave gratings at three spatial frequencies presented continuously for three seconds each across multiple trials. Participants' susceptibility to visual stress, headaches, and discomfort was assessed using questionnaires before and during the experiment. The experiment employed a two-by-two design to evaluate habituation (exponentially decreasing response) and sensitisation (exponentially increasing response) effects at two different time granularities. Mass univariate analysis with cluster-based permutation tests was conducted to identify significant brain response changes during the period of constant stimulation, which we call the DC-shift period. Significant effects were observed during the DC-shift period, indicating sustained hyper-excitation to the medium-pattern glare stimulus. In particular, the mean/intercept analysis revealed a consistent positive-going response to the medium stimulus throughout the DC-shift period, suggesting continued neural engagement. Participants reporting higher discomfort exhibited sensitisation at fine temporal granularity and habituation at coarser temporal granularity. These effects were predominantly localised to the right posterior scalp regions. The study demonstrates that individuals sensitive to pattern-glare stimuli exhibit dynamic neural adaptation characterised by short-term sensitisation and long-term habituation. These findings enhance the understanding of cortical hyperexcitability mechanisms and may inform future interventions for visual-stress-related conditions, such as migraines and epilepsy. Further research is needed to explore the underlying neural processes and validate these effects in clinical populations.

Chisco: An EEG-based BCI dataset for decoding of imagined speech

The rapid advancement of deep learning has enabled Brain-Computer Interfaces (BCIs) technology, particularly neural decoding techniques, to achieve higher accuracy and deeper levels of interpretation. Interest in decoding imagined speech has significantly increased because its concept akin to “mind reading”. However, previous studies on decoding neural language have predominantly focused on brain activity patterns during human reading. The absence of imagined speech electroencephalography (EEG) datasets has constrained further research in this field. We present the Chinese Imagined Speech Corpus (Chisco), including over 20,000 sentences of high-density EEG recordings of imagined speech from healthy adults. Each subject’s EEG data exceeds 900 minutes, representing the largest dataset per individual currently available for decoding neural language to date. Furthermore, the experimental stimuli include over 6,000 everyday phrases across 39 semantic categories, covering nearly all aspects of daily language. We believe that Chisco represents a valuable resource for the fields of BCIs, facilitating the development of more user-friendly BCIs.

Automated EEG-Based Brainwave Analysis for the Detection of Postoperative Delirium Does Not Result in a Shorter Length of Stay in Geriatric Hip Fracture Patients: A Multicentre Randomized Controlled Trial.

Introduction: Delirium in postoperative geriatric hip fracture patients is a serious and often preventable condition. If detected in time, it can be treated, but a delay in the diagnosis and initiation of treatment impairs outcomes. A novel approach to detect delirium is to use point-of-care electro-encephalogram (EEG) recording with automated analysis. In this study, the authors investigated whether screening for delirium with EEG recording and automated analysis resulted in reduced length of stay after surgery and superior screening performance in comparison to the Delirium Observation Screening Scale (DOS). Methods: This randomized control trial was conducted at two geriatric trauma centres in the Netherlands. Patients were eligible for inclusion if they were aged 70 years or above, were admitted to the geriatric trauma unit and undergoing surgery for a hip fracture. Patients were randomized to either the intervention (EEG-based brainwave analysis) or control group (DOSS screening tool). Participants were screened for delirium twice a day during three consecutive days starting at day 0 of the surgery, with the first measurement before the surgery. The primary outcome was length of stay. In addition, the screening performance for both automated EEG-based brainwave analysis and DOS was determined. Results: A total of 388 patients were included (189 per arm). There were no differences between groups in terms of median hospital length of stay (DOS 7 days (IQR 5.75-9) vs. EEG-based brainwave analysis 7 days (IQR 5-9); p = 0.867). The performance of EEG-based brainwave analysis was considerably lower than that of the DOSS in terms of discrimination between patients with and without postoperative delirium. Conclusions: Screening for postoperative delirium in geriatric hip fracture patients using automated EEG-based brainwave analysis did not result in a shorter length of stay. Additionally, the results of this study show no clear advantage in terms of the screening performance of EEG-based brainwave analysis over the current standard of care for geriatric patients with a hip fracture.

Wavelet-Detected Changes in Nocturnal Brain Electrical Activity in Patients with Non-Motor Disorders Indicative of Parkinson's Disease.

Background/Objectives-Parkinson's disease (PD) is the second most common neurodegenerative disorder caused by the destruction of neurons in the substantia nigra of the brain. Clinical diagnosis of this disease, based on monitoring motor symptoms, often leads to a delayed start of PD therapy and control, where over 60% of dopaminergic nerve cells are damaged in the brain substantia nigra. The search for simple and stable characteristics of EEG recordings is a promising direction in the development of methods for diagnosing PD and methods for diagnosing the preclinical stage of PD development. Methods-42 subjects participated in work, of which 4 female/10 male patients were included in the group of patients with non-motor disorders, belonging to the risk group for developing PD (median age: 62 years, height: 164 cm, weight: 70 kg, pulse: 70, BPsys and BPdia: 143 and 80)/(median age: 68 years, height: 170 cm, weight: 73.9 kg, pulse: 75, BPsys and BPdia: 143 and 82). The first control group of healthy participants included 6 women (median age: 33 years, height: 161 cm, weight: 66 kg, pulse: 80, BPsys and BPdia: 110 and 80)/8 men (median age: 36.3 years, height: 175 cm, weight: 69 kg, pulse: 78, BPsys and BPdia: 120 and 85). The second control group of healthy participants included 8 women (median age: 74 years, height: 164 cm, weight: 70 kg, pulse: 70, BPsys and BPdia: 145 and 82)/6 men (median age: 51 years, height: 172 cm, weight: 72.5 kg, pulse: 74, BPsys and BPdia: 142 and 80). Wavelet oscillatory pattern estimation is performed on patients' nocturnal sleep recordings without separating them into sleep stages. Results-Amplitude characteristics of oscillatory activity in patients without motor disorders and the prodromal PD stage are significantly reduced both in terms of changes in the number of patterns and in terms of their duration. This pattern is especially pronounced for high-frequency activity, in frequency ranges close to 40 Hz. Conclusions-The success of the analysis of the electrical activity of the brain, performed over the entire duration of the night recording, makes it promising to further use during daytime monitoring the concept of oscillatory wavelet patterns in patients with non-motor disorders, belonging to the risk group for developing PD. The daytime monitoring system can become the basis for developing screening tests to detect neurodegenerative diseases as part of routine medical examinations.

Expert level of detection of interictal discharges with a deep neural network.

Deep learning methods have shown potential in automating the detection of interictal epileptiform discharges (IEDs) in electroencephalography (EEG). We compared IED detection using our previously trained deep neural network with a group of experts to assess its potential applicability. First, we performed clinical validation on an internal data set. Seven experts reviewed all EEG studies. Performance agreement between experts and the network was compared at both the EEG and IED levels. All EEG recordings were also processed with Persyst. Subsequently, we performed external validation, with data from four centers, using a hybrid approach, where detections by the deep neural network were reviewed by an expert. In case of disagreement with the original report, the EEG recording was annotated independently by five experts. For internal validation we included 22 EEG studies with IEDs and 28 EEG studies from controls. At the EEG level, our network showed performance similar to that of the experts. For individual IED detection, the sensitivities between experts ranged from 20.7%-86.4%, whereas the sensitivity of our network was 82.5% (confidence interval [CI]: 77.7%-87.4%) at 99% specificity and a false detection rate (FDR) of <.2/min, outperforming Persyst, with 64.6% sensitivity (CI: 61.4%-67.9%) at 98% specificity. External validation in 174 EEG studies demonstrated that all 85 EEG recordings classified as normal in the original report were classified correctly, with an FDR of .10/min. Of the 89 EEG studies with IEDs according to the report, 56 were correctly classified (Cohen's κ = .62). Visual analysis of the remaining 33 EEG recordings showed high interobserver variability among the five experts (Fleiss' κ = .13). Our deep neural network detects IEDs on par with clinical experts. The external validation in a hybrid approach showed substantial agreement with the original report. Disagreement was due mainly to high interobserver variability. Our deep neural network may support visual EEG analysis and assist in diagnostics, particularly when human resources are limited.

Multicentre analysis of seizure outcome predicted by removal of high frequency oscillations.

In drug-resistant focal epilepsy, planning surgical resection may involve presurgical intracranial EEG recordings (iEEG) to detect seizures and other iEEG patterns to improve postsurgical seizure outcome. We hypothesized that resection of tissue generating interictal high frequency oscillations (HFOs, 80-500 Hz) in the iEEG predicts surgical outcome. Eight international epilepsy centres recorded iEEG during the patients' pre-surgical evaluation. The patients were of all ages, had epilepsy of all types, and underwent surgical resection of a single focus aiming at seizure freedom. In a prospective analysis we applied a fully automated definition of HFO which was independent of the dataset. Using an observational cohort design that was blinded to postsurgical seizure outcome, we analysed HFO rates during non-rapid-eye-movement sleep. If channels had consistently high rates over multiple epochs, they were labelled the "HFO area". After HFO analysis, centres provided the electrode contacts located in the resected volume and the seizure outcome at follow-up ≥24 months after surgery. The study was registered at www.clinicaltrials.gov (NCT05332990). We received 160 iEEG datasets. In 146 datasets (91%), the HFO area could be defined. The patients with completely resected HFO area were more likely to achieve seizure freedom compared to those without (OR 2.61 CI [1.15-5.91], P = 0.02). Among seizure free patients, the HFO area was completely resected in 31 and was not completely resected in 43. Among patients with recurrent seizures, the HFO area was completely resected in 14 and was not completely resected in 58. When predicting seizure freedom, the negative predictive value of the HFO area (68% CI [52-81]) was higher than that for the resected volume as predictor by itself (51% CI [42-59], P = 4e-5). The sensitivity and specificity for complete HFO area resection were 0.88 CI [0.72-0.98] and 0.39 CI [0.25-0.54] and the area under the curve was 0.83 CI [0.58-0.97], indicating good predictive performance. In a blinded cohort study from independent epilepsy centres, applying a previously validated algorithm for HFO marking without the need of adjusting to new datasets allowed us to validate the clinical relevance of HFOs to plan the surgical resection.

Influence of sleep on seizures and interictal epileptiform discharges in epilepsy.

Sleep significantly influences seizure occurrence and interictal epileptiform discharges (IEDs) in patients with epilepsy. Sleep-related epilepsy, where seizures occur exclusively or predominantly during sleep, has been observed in various epilepsy syndromes. Understanding the influence of sleep on seizures and IEDs is crucial in the diagnosis, classification, and management of epilepsy. Although there is a bidirectional relationship between sleep and epilepsy, this review focuses on the influence of sleep on seizures and IEDs in epilepsy. Seizures are more common during non-rapid eye movement (NREM) sleep, particularly during stage N2, and are suppressed during rapid eye movement (REM) sleep. Sleep also activates IEDs, increasing the diagnostic yield of EEG recordings. The rate of IEDs increases during NREM sleep, reaches its maximum during stage N3, and decreases during REM sleep. Sleep affects the electrical field of IEDs, with an increase of spiking fields during NREM sleep and a decrease during REM sleep. In the localization of epileptogenic foci, REM sleep is less sensitive but more specific than NREM sleep. Thalamocortical EEG synchronization during NREM sleep and desynchronization during REM sleep underlie their opposing effects on seizures and IEDs. Accumulating evidence has suggested an antiseizure effect of orexinergic antagonism in animal studies. Interventions that promote REM sleep, including orexinergic antagonists, should be studied in the future as novel treatment strategies for epilepsy.

Quantitative EEG and its relationship with attentional control in patients with anxiety disorders.

Attentional control is crucial in the development and maintenance of anxiety disorders. Understanding the underlying mechanisms of attentional control can help to shed light on the neuropathological processes in anxiety disorders (ANX). Quantitative electroencephalography (QEEG) offers a cost-effective, noninvasive method for examining the neuropathological mechanisms of mental disorders. In this study, 67 patients with ANX and 45 healthy controls (HC) were recruited. EEG recordings were obtained for 5 minutes in an eyes-closed condition. QEEG was employed to evaluate the mechanisms of attentional control in ANX. Neurophysiological measures indicated that anxiety patients exhibited a more frontal topographic pattern of theta/beta ratio (TBR) compared to HC. Additionally, a significant decrease in temporal beta power was observed in the ANX group. Correlation analysis revealed that decreased beta power and increased TBR were significant association between attentional control deficits in ANX. These findings provide electrophysiological evidence of impaired attentional control processing in anxiety patients, characterized by decreased temporal beta power and increased frontal TBR. Temporal beta power and frontal TBR may serve as promising biomarkers for attentional control in ANX.

Deep-learning models reveal how context and listener attention shape electrophysiological correlates of speech-to-language transformation.

To transform continuous speech into words, the human brain must resolve variability across utterances in intonation, speech rate, volume, accents and so on. A promising approach to explaining this process has been to model electroencephalogram (EEG) recordings of brain responses to speech. Contemporary models typically invoke context invariant speech categories (e.g. phonemes) as an intermediary representational stage between sounds and words. However, such models may not capture the complete picture because they do not model the brain mechanism that categorizes sounds and consequently may overlook associated neural representations. By providing end-to-end accounts of speech-to-text transformation, new deep-learning systems could enable more complete brain models. We model EEG recordings of audiobook comprehension with the deep-learning speech recognition system Whisper. We find that (1) Whisper provides a self-contained EEG model of an intermediary representational stage that reflects elements of prelexical and lexical representation and prediction; (2) EEG modeling is more accurate when informed by 5-10s of speech context, which traditional context invariant categorical models do not encode; (3) Deep Whisper layers encoding linguistic structure were more accurate EEG models of selectively attended speech in two-speaker "cocktail party" listening conditions than early layers encoding acoustics. No such layer depth advantage was observed for unattended speech, consistent with a more superficial level of linguistic processing in the brain.

EEG correlates of self-recognition in morphed faces: association with social anxiety

Recognizing one’s own face is important for self-identification and is considered an indicator of self-consciousness. Social anxiety is related to special attention to self. The aim was to investigate the oscillatory dynamics associated with self-recognition/non-self-recognition in morphed faces and the correlation with social anxiety in these processes. During EEG recordings with 128 electrodes, 48 volunteers (31 females) recognized themselves in morphed faces. During self-recognition, a greater increase in theta rhythm was revealed in the time interval from 800 to 1500 ms than in the non-self-recognition condition. Based on the data on the relationship of the theta rhythm with attention and memory, it could be assumed that the increase in theta rhythm may be related to memory and attention processes when perceiving details, mismatches, and misrepresentation of one’s own face. Social anxiety was positively related to the magnitude of theta rhythm during self-recognition, it could be related to the increased attention that socially anxious people focus on themselves and distortions of their own face.

The development of aperiodic neural activity in the human brain.

The neurophysiological mechanisms supporting brain maturation are fundamental to attention and memory capacity across the lifespan. Human brain regions develop at different rates, with many regions developing into the third and fourth decades of life. Here, in this preregistered study ( https://osf.io/gsru7 ), we analyzed intracranial EEG (iEEG) recordings from widespread brain regions in a large developmental cohort. Using task-based (i.e., attention to-be-remembered visual stimuli) and task-free (resting-state) data from 101 children and adults (5.93 - 54.00 years, 63 males; n electrodes = 5691), we mapped aperiodic (1/ƒ-like) activity, a proxy of excitation:inhibition (E:I) balance with steeper slopes indexing inhibition and flatter slopes indexing excitation. We reveal that aperiodic slopes flatten with age into young adulthood in both association and sensorimotor cortices, challenging models of early sensorimotor development based on brain structure. In prefrontal cortex (PFC), attentional state modulated age effects, revealing steeper task-based than task-free slopes in adults and the opposite in children, consistent with the development of cognitive control. Age-related differences in task-based slopes also explained age-related gains in memory performance, linking the development of PFC cognitive control to the development of memory. Last, with additional structural imaging measures, we reveal that age-related differences in gray matter volume are differentially associated with aperiodic slopes in association and sensorimotor cortices. Our findings establish developmental trajectories of aperiodic activity in localized brain regions and illuminate the development of PFC inhibitory control during adolescence in the development of attention and memory.

Self-Supervised Data-Driven Approach Defines Pathological High-Frequency Oscillations in Human.

Interictal high-frequency oscillations (HFOs) are a promising neurophysiological biomarker of the epileptogenic zone (EZ). However, objective criteria for distinguishing pathological from physiological HFOs remain elusive, hindering clinical application. We investigated whether the distinct mechanisms underlying pathological and physiological HFOs are encapsulated in their signal morphology in intracranial EEG (iEEG) recordings and whether this mechanism-driven distinction could be simulated by a deep generative model. In a retrospective cohort of 185 epilepsy patients who underwent iEEG monitoring, we analyzed 686,410 HFOs across 18,265 brain contacts. To learn morphological characteristics, each event was transformed into a time-frequency plot and input into a variational autoencoder. We characterized latent space clusters containing morphologically defined putative pathological HFOs (mpHFOs) using interpretability analysis, including latent space disentanglement and time-domain perturbation. mpHFOs showed strong associations with expert-defined spikes and were predominantly located within the seizure onset zone (SOZ). Discovered novel pathological features included high power in the gamma (30-80 Hz) and ripple (>80 Hz) bands centered on the event. These characteristics were consistent across multiple variables, including institution, electrode type, and patient demographics. Predicting 12-month postoperative seizure outcomes using the resection ratio of mpHFOs outperformed unclassified HFOs (F1=0.72 vs. 0.68) and matched current clinical standards using SOZ resection (F1=0.74). Combining mpHFO data with demographic and SOZ resection status further improved prediction accuracy (F1=0.83). Our data-driven approach yielded a novel, explainable definition of pathological HFOs, which has the potential to further enhance the clinical use of HFOs for EZ delineation.

Neuro-Cognitive Insights Into Engineering Design: Exploring Electroencephalography Predictive Associations With Task Performance

Abstract This paper investigates the relationship between brain activity, measured by electroencephalography (EEG) data, and the performance assessment result of engineering design activities involving different cognitive processes. Employing a novel signal processing pipeline, we analyzed EEG variations of 37 subjects during two design tasks that mostly leverage, respectively, convergent and divergent thinking: the design with morphological table (task and the problem-solving task. The EEG recordings underwent meticulous artifact removal, allowing for a comprehensive investigation into the statistical relationships between frequency bands, channels, and design outcome performance metrics. The developed models linking better design outcomes with brain (de)synchronization demonstrated remarkable accuracy, precision, and recall across performance metrics for both tasks. Notably, the EEG data in theta band measured from the frontal area at both hemispheres and a left parietal/occipital channel were essential for estimating better design performance with brain desynchronization. On the contrary, the model based on brain synchronization produces precise estimations of design performance with alpha band and channels in temporal and parietal areas. These findings highlight EEG variation as a viable proxy for design performance, paving the way for more effective performance prediction models with fewer sensors. Overall, this research contributes to the emerging field of neurocognitive design assessment and underscores the potential for EEG-based predictions in engineering design tasks.

A Machine Learning Framework for Classroom EEG Recording Classification: Unveiling Learning-Style Patterns

Classroom EEG recordings classification has the capacity to significantly enhance comprehension and learning by revealing complex neural patterns linked to various cognitive processes. Electroencephalography (EEG) in academic settings allows researchers to study brain activity while students are in class, revealing learning preferences. The purpose of this study was to develop a machine learning framework to automatically classify different learning-style EEG patterns in real classroom environments. Method: In this study, a set of EEG features was investigated, including statistical features, fractal dimension, higher-order spectra, entropy, and a combination of all sets. Three different machine learning classifiers, random forest (RF), K-nearest neighbor (KNN), and multilayer perceptron (MLP), were used to evaluate the performance. The proposed framework was evaluated on the real classroom EEG dataset, involving EEG recordings featuring different teaching blocks: reading, discussion, lecture, and video. Results: The findings revealed that statistical features are the most sensitive feature metric in distinguishing learning patterns from EEG. The statistical features and RF classifier method tested in this study achieved an overall best average accuracy of 78.45% when estimated by fivefold cross-validation. Conclusions: Our results suggest that EEG time domain statistics have a substantial role and are more reliable for internal state classification. This study might be used to highlight the importance of using EEG signals in the education context, opening the path for educational automation research and development.

Artifacts Deceive: The Electroretinogram in the Electroencephalogram of a teenager with cerebral anoxia.

Artifacts on an electroencephalogram (EEG) - whether physiologic or non-physiologic - can be common and are important to recognize to prevent errors in interpretation. One such EEG artifact is an electroretinogram (ERG) artifact which occurs during photic stimulation. Typically, of a low amplitude, its presence is usually obscured by normal EEG activity over the frontopolar channels but it can appear prominent in very suppressed or EEG recordings showing electrocerebral inactivity. Overall, rarely reported in the literature, the purpose of this case report is to highlight the presence of an ERG artifact in a teenage boy where EEG was obtained after a cerebral anoxic event. It is important that EEG readers identify this to be a non-cerebral waveform in order to provide an accurate assessment of neurologic prognosis.