Scalp EEG Recordings Research Articles

Beta-gamma phase-amplitude coupling of scalp electroencephalography during walking preparation in Parkinson’s disease differs depending on the freezing of gait

IntroductionDespite using beta oscillations within the subthalamic nucleus as a biomarker of akinesia or rigidity in Parkinson’s disease, a specific biomarker for freezing of gait (FOG) remains unclear. Recently, scalp phase-amplitude coupling (PAC) measured through scalp electroencephalography (EEG) has emerged as a promising tool for analyzing brain function. In this study, we examined whether PAC could be a biomarker for FOG.MethodsWe enrolled 11 patients with Parkinson’s disease and recorded scalp EEG in preparation for and during gait while simultaneously assessing motor function, including FOG. We investigated changes in cortical PAC during walking with and without FOG and examined its correlation with the postural instability and gait difficulty (PIGD) score.ResultsPatient characteristics were as follows: mean age 59.1 ± 6.9 years, disease duration 13.9 ± 4.1 years, and seven men. Four trials were excluded from the analysis owing to artifacts. In the trials without FOG (n = 18), beta-gamma PAC in the sensorimotor area decreased during gait preparation (p = 0.011; linear mixed-effects model), which was not the case in trials with FOG (n = 6) (p = 0.64; linear mixed-effects model). Using a support vector machine, machine learning of PAC during preparation for walking predicted the presence of FOG with an accuracy of 71.2%. Conversely, PAC increased during walking in trials with FOG (p = 0.0042; linear mixed-effects model), and PAC 20 s after the start of walking was positively correlated with the PIGD score (correlation coefficient = 0.406, p = 0.032; Pearson’s rank correlation).ConclusionBeta-gamma PAC in the sensorimotor area during preparation for walking differs depending on the emergence of FOG. As gait symptoms worsened, beta-gamma PAC in the sensorimotor area during walking gradually increased. Cortical PAC may be a biomarker for FOG in Parkinson’s disease and may lead to the development of strategies to prevent falls in the future.

Patterns of ictal surface EEG in occipital seizures: A simultaneous scalp and intracerebral recording study

ObjectiveTo describe the ictal scalp EEG patterns of occipital seizures (OS) and their spatiotemporal correlations with intracerebral occipital ictal discharges derived from simultaneous SEEG-EEG recordings. MethodsPatients with SEEG confirmed OS (14 OS from 8 patients) were selected from an epilepsy surgery center and were monitored 3–10 days using simultaneous scalp EEG and SEEG recordings. ResultsOn scalp EEG, the most common onset patterns were background activity suppression (28.6 %) and high amplitude slow wave corresponding to intracerebral DC-shift (28.6 %) and occurred with a median delay of 0 s after intra-cerebral onset. The initial discharge involved occipital electrodes in only 50 % of the seizures (7/14) with additional basal temporal (8/14) or parietal electrodes (5/14). The onset was ipsilateral to the intra-cerebral onset zone in 71.4 % of seizures and bilateral in the remaining (28.6 %). The most common propagation pattern was either unilateral (50 %) or bilateral (50 %) and a rhythmic slow activity (66.7 %). Different OS subtypes display distinct scalp EEG patterns. ConclusionScalp EEG accurately determines intra-cerebral seizure onset time in OS and has good lateralizing value. However, initial scalp modification does not always involves occipital electrodes and the second modification is well lateralizing in only 50 % of seizures. SignificanceThis study describes will help clinicians to better identify OS during video EEG and better plan intra-cerebral explorations for epilepsy surgery.

Cochlear Implant Artifacts in Continuous Electroencephalogram Recording.

Cochlear implants to aid sensorineural hearing loss are becoming commonplace. In this study, we describe two cases that showed artifacts related to the cochlear implant device during scalp EEG recording. To our knowledge, cochlear implant artifacts have not been reported previously. Recognizing cochlear implant artifacts will avoid misinterpretation and resultant inappropriate treatment.

Conflict Dynamics of Post-Retrieval Extinction: A Comparative Analysis of Unconditional and Conditional Reminders Using Skin Conductance Responses and EEG.

The post-retrieval extinction paradigm, rooted in reconsolidation theory, holds promise for enhancing extinction learning and addressing anxiety and trauma-related disorders. This study investigates the impact of two reminder types, mild US-reminder (US-R) and CS-reminder (CS-R), along with a no-reminder extinction, on fear recovery prevention in a categorical fear conditioning paradigm. Scalp EEG recordings during reminder and extinction processes were conducted in a three-day design. Results show that the US-R group exhibits a distinctive extinction learning pattern, characterized by a slowed-down yet successful process and pronounced theta-alpha desynchronization (source-located in the prefrontal cortex) during CS processing, followed by enhanced synchronization (source-located in the anterior cingulate) after shock cancellation in extinction trials. These neural dynamics correlate with the subtle advantage of US-R in the Day 3 recovery test, presenting faster spontaneous recovery fading and generally lower fear reinstatement responses. Conversely, the CS reminder elicits CS-specific effects in later episodic tests. The unique neural features of the US-R group suggest a larger prediction error and subsequent effortful conflict learning processes, warranting further exploration.

Epileptiform Discharges Recorded as Monopoles and Dipoles in the EEG: A Practical Approach to Differentiate Tangential and Radial Generators Using Electrode Position Versus Voltage Graphs.

Differentiating dipoles (tangential generators) from monopoles (radial generators) in routine EEG reading can be difficult. The polarity of sharp waves seen on surface EEG will change depending on the generator being located at the wall of the sulcus versus the crown of a gyrus. In this article, the authors introduce visual rules that may be used to determine polarity and estimate the localization of potentials during analysis of the EEG. They also review a practical approach to differentiate monopoles (radial generators) from dipoles (tangential dipoles) in the surface EEG using "electrode position versus voltage graphs." Finally, the authors illustrate examples of dipoles and monopoles with focal spikes located in the following locations: (1) bipolar spikes located in the anterior bank of the central sulcus, (2) bipolar spikes located in the posterior bank of the central sulcus, (3) monopolar spikes located in the crown of the precentral gyrus, (4) bipolar spikes with a vertically oriented dipole originated within the temporal (inferior) bank of the Sylvian fissure, and (5) monopolar spikes located in the convexity of a temporal gyrus. In summary, this article discusses electrographic features of spikes localized in various fissures and gyri and provides practical rules that permit the identification and location of dipoles and monopoles in standard scalp EEG recordings.

Enhanced Working Memory Representations for Rare Events.

Rare events (oddballs) produce a variety of enhanced physiological responses relative to frequent events (standards), including the P3b component of the event-related potential (ERP) waveform. Previous research has suggested that the P3b is related to working memory, which implies that working memory representations will be enhanced for rare stimuli. To test this hypothesis, we devised a modified oddball paradigm in which the target was a disk presented at one of 16 different locations, which were divided into a rare set and a frequent set. Participants made a binary response on each trial to report whether the target appeared in the rare set or the frequent set. As expected, the P3b was much larger for stimuli appearing at a location within the rare set. We also included occasional probe trials in which the subject reported the exact location of the target. We found that these reports were more accurate for locations within the rare set than for locations within the frequent set. Moreover, the mean accuracy of these reports was correlated with the mean amplitude of the P3b. We also applied multivariate pattern analysis to the ERP data to "decode" the remembered location of the target. Decoding accuracy was greater for locations within the rare set than for locations within the frequent set. We then replicated and extended our behavioral findings in a follow-up experiment. These behavioral and electrophysiological results demonstrate that although both frequent and rare events are stored in working memory, the representations are enhanced for rare oddball events.

Decoding EEG for optimizing naturalistic memory

Background:Spectral features of human electroencephalographic (EEG) recordings during learning predict subsequent recall variability. New method:Capitalizing on these fluctuating neural features, we develop a non-invasive closed-loop (NICL) system for real-time optimization of human learning. Participants play a virtual navigation-and-memory game; recording multi-session data across days allowed us to build participant-specific classification models of recall success. In subsequent closed-loop sessions, our platform manipulated the timing of memory encoding, selectively presenting items during periods of predicted good or poor memory function based on EEG features decoded in real time. Results:The induced memory effect (the difference between recall rates when presenting items during predicted good vs. poor learning periods) increased with the accuracy of neural decoding. Comparison with Existing Methods:This study demonstrates greater-than-chance memory decoding from EEG recordings in a naturalistic virtual navigation task with greater real-world validity than basic word-list recall paradigms. Here we modulate memory by timing stimulus presentation based on noninvasive scalp EEG recordings, whereas prior closed-loop studies for memory improvement involved intracranial recordings and direct electrical stimulation. Other noninvasive studies have investigated the use of neurofeedback or remedial study for memory improvement. Conclusions:These findings present a proof-of-concept for using non-invasive closed-loop technology to optimize human learning and memory through principled stimulus timing, but only in those participants for whom classifiers reliably predict out-of-sample memory function.

The Role of EEG microstates in predicting oxcarbazepine treatment outcomes in patients with newly-diagnosed focal epilepsy

PurposeMicrostates represent the global and topographical distribution of electrical brain activity from scalp-recorded EEG. This study aims to explore EEG microstates of patients with focal epilepsy prior to medication, and employ extracted microstate metrics for predicting treatment outcomes with Oxcarbazepine monotherapy. MethodsThis study involved 25 newly-diagnosed focal epilepsy patients (13 females), aged 12 to 68, with various etiologies. Patients were categorized into Non-Seizure-Free (NSF) and Seizure-Free (SF) groups according to their first follow-up outcomes. From pre-medication EEGs, four representative microstates were identified by using clustering. The temporal parameters and transition probabilities of microstates were extracted and analyzed to discern group differences. With generating sample method, Support Vector Machine (SVM), Logistic Regression (LR), and Naïve Bayes (NB) classifiers were employed for predicting treatment outcomes. ResultsIn the NSF group, Microstate 1 (MS1) exhibited a significantly higher duration (mean±std. = 0.092±0.008 vs. 0.085±0.008, p = 0.047), occurrence (mean±std. = 2.587±0.334 vs. 2.260±0.278, p = 0.014), and coverage (mean±std. = 0.240±0.046 vs. 0.194±0.040, p = 0.014) compared to the SF group. Additionally, the transition probabilities from Microstate 2 (MS2) and Microstate 3 (MS3) to MS1 were increased. In MS2, the NSF group displayed a stronger correlation (mean±std. = 0.618±0.025 vs. 0.571±0.034, p < 0.001) and a higher global explained variance (mean±std. = 0.083±0.035 vs. 0.055±0.023, p = 0.027) than the SF group. Conversely, Microstate 4 (MS4) in the SF group demonstrated significantly greater coverage (mean±std. = 0.388±0.074 vs. 0.334±0.052, p = 0.046) and more frequent transitions from MS2 to MS4, indicating a distinct pattern. Temporal parameters contribute major predictive role in predicting treatment outcomes of Oxcarbazepine, with area under curves (AUCs) of 0.95, 0.70, and 0.86, achieved by LR, NB and SVM, respectively. ConclusionThis study underscores the potential of EEG microstates as predictive biomarkers for Oxcarbazepine treatment responses in newly-diagnosed focal epilepsy patients.

Dynamic channelwise functional-connectivity states extracted from resting-state EEG signals of patients with Parkinson’s disease

BackgroundParkinson’s disease (PD) is a progressive neurodegenerative disease that usually happens to elderly people, with a wide range of motor and dementia symptoms. An objective and convenient biomarker for PD detection is extremely valuable, especially one that could be acquired non-invasively and low-costly. To this end, this study used resting-state scalp electroencephalography (EEG) signals to explore dynamic functional-connectivity (dFC) states between each pair of EEG recording channels, without source localization.MethodsdFC refers to synchronization patterns over time between each pair of EEG channels. First, five frequency bands were extracted from EEG signals with fourth-order Butterworth bandpass filter, including delta (0.5–4 Hz), theta (4–8 Hz), alpha (8–13 Hz), beta (8–30 Hz) and gamma (30–50 Hz). Then, after non-random joint fluctuation was measured with weighted symbolic mutual information (wSMI) algorithm, whole-brain dynamic channelwise dFC states were estimated, and classified with k-means clustering. At last, FC state occurrences were calculated, and ANOVA analyses were performed for each state. Two open-source resting-state EEG data sets (https://doi.org/10.18112/openneuro.ds002778.v1.0.4: 32 channels, 16 health controls and 15 PD subjects. https://doi.org/10.18112/openneuro.ds003490.v1.1.0: 64 channels, 25 health controls and 25 PD subjects) were used to test our methods.ResultsSignificant changes in proportions of various dFC states within beta frequency-band were consistently observed in these both data sets (p value < 0.05).ConclusionsOur findings suggest that channelwise dFC states within beta frequency-band directly extracted from resting-state scalp–EEG recordings could potentially serve as a biomarker of PD.

Single-channel seizure detection with clinical confirmation of seizure locations using CHB-MIT dataset.

Long-term electroencephalography (EEG) monitoring is advised to patients with refractory epilepsy who have a failure of anti-seizure medication and therapy. However, its real-life application is limited mainly due to the use of multiple EEG channels. We proposed a patient-specific deep learning-based single-channel seizure detection approach using the long-term scalp EEG recordings of the Children's Hospital Boston-Massachusetts Institute of Technology (CHB-MIT) dataset, in conjunction with neurologists' confirmation of spatial seizure characteristics of individual patients. We constructed 18-, 4-, and single-channel seizure detectors for 13 patients. Neurologists selected a specific channel among four channels, two close to the behind-the-ear and two at the forehead for each patient, after reviewing the patient's distinctive seizure locations with seizure re-annotation. Our multi- and single-channel detectors achieved an average sensitivity of 97.05-100%, false alarm rate of 0.22-0.40/h, and latency of 2.1-3.4 s for identification of seizures in continuous EEG recordings. The results demonstrated that seizure detection performance of our single-channel approach was comparable to that of our multi-channel ones. We suggest that our single-channel approach in conjunction with clinical designation of the most prominent seizure locations has a high potential for wearable seizure detection on long-term EEG recordings for patients with refractory epilepsy.

The impact of simultaneous intracranial recordings on scalp EEG: A finite element analysis

BackgroundIn this study, we examined the utility of simultaneous scalp and stereotactic intracranial electroencephalography (SSIEEG) in epilepsy patients. Although SSIEEG offers valuable insights into epilepsy and cognitive function, its routine use is uncommon. Challenges include interpreting post-craniotomy scalp EEG due to surgically implanted electrodes. New methodWe describe our methodology for conducting SSIEEG recordings. To simulate the potential impact on EEG interpretation, we computed the leadfield of scalp electrodes with and without burrholes using Finite Element Analysis to compare the resulting sensitivity volume and waveforms of simulated intracranial signals between skulls with and without burrholes. ResultsThe presence of burr holes in the skull layer of the leadfield models did not discernibly modify simulated waveforms or scalp EEG topology. Using realistic SEEG burr hole diameter, the difference in the average leadfield of scalp electrodes was 0.12% relative to the effect of switching two nearby electrodes, characterized by the cosine similarity difference. No patients experienced adverse events related to SSIEEG. Comparison with existing methodsAlthough there is increasing acceptance and interest in SSIEEG, few studies have characterized the technical feasibility. Here, we demonstrate through modelling that scalp recordings from SSIEEG are comparable to that through an intact skull. ConclusionThe placement and simultaneous acquisition of scalp EEG during invasive monitoring through stereotactically inserted EEG electrodes is routinely performed at the Hospital for Sick Children. Scalp EEG recordings may assist with clinical interpretation. Burr holes in the skull layer did not discernibly alter EEG waveforms or topology.

An 800MΩ-Input-Impedance 95.3dB-DR Δ-ΔΣ AFE for Dry-Electrode Wearable EEG Recording.

Non-invasive, closed-loop brain modulation offers an accessible and cost-effective means of evaluating and modulating one's mental and physical well-being, such as Parkinson's disease, epilepsy, and sleep disorders. However, wearable EEG systems pose significant challenges for the analog front-end (AFE) circuits in view of µV-level EEG signals of interest, multiple sources of interference, and ill-defined skin contact. This paper presents a direct-digitization AFE tailored for dry-electrode scalp EEG recording, characterized by wide input dynamic range (DR) and high input impedance. The AFE utilizes a second-order 5-bit delta-delta sigma (Δ-ΔΣ) ADC to shape DC electrode offset (DEO) and low-frequency disturbances while retaining high accuracy. A non-inverting pseudo-differential instrumentation amplifier (IA) embedded in the ADC ensures high input impedance (Zin) and common-mode rejection ratio (CMRR). Fabricated in a standard 0.18-μm CMOS process, the AFE delivers 700-mVpp input signal range, 95.3-dB DR, 87-dB SNDR, and 800-MΩ input impedance at 50 Hz while consuming 88.4µW from a 1.2 V supply. The benefits of high DR and high input impedance have been validated by dry-electrode EEG measurement.

EEG Source Imaging-Clinical Considerations for EEG Acquisition and Signal Processing for Improved Temporo-Spatial Resolution.

EEG source imaging (ESI) has gained traction in recent years as a useful clinical tool for the noninvasive surgical work-up of patients with drug-resistant focal epilepsy. Despite its proven benefits for the temporo-spatial modeling of spike and seizure sources, ESI remains widely underused in clinical practice. This partly relates to a lack of clarity around an optimal approach to the acquisition and processing of scalp EEG data for the purpose of ESI. Here, we describe some of the practical considerations for the clinical application of ESI. We focus on patient preparation, the impact of electrode number and distribution across the scalp, the benefit of averaging raw data for signal analysis, and the relevance of modeling different phases of the interictal discharge as it evolves from take-off to peak. We emphasize the importance of recording high signal-to-noise ratio data for reliable source analysis. We argue that the accuracy of modeling cortical sources can be improved using higher electrode counts that include an inferior temporal array, by averaging interictal waveforms rather than limiting ESI to single spike analysis, and by careful interrogation of earlier phase components of these waveforms. No amount of postacquisition signal processing or source modeling sophistication, however, can make up for suboptimally recorded scalp EEG data in a poorly prepared patient.

One out of ten independent components shows flipped polarity with poorer data quality: EEG database study.

Independent component analysis (ICA) is widely used today for scalp-recorded EEG analysis. One of the limitations of ICA-based analysis is polarity indeterminacy. It is not easy to find detailed documentations that explains engineering solutions of how the polarity indeterminacy is addressed in a given implementation. We investigated how it is implemented in the case of EEGLAB and also the relation between the outcome of the polarity determination and classification of independent components (ICs) in terms of the estimated nature of the sources (brain, muscle, eye, etc.) using an open database of n = 212 EEG dataset of resting state recordings. We found that (1) about 91% of ICs showed positive-dominant IC scalp topographies; (2) positive-dominant ICs were more associated with brain-originated signals; (3) positive-dominant ICs showed more radial (peaked at 10-30 degrees deviations from the radial axis) dipolar projection pattern with less residual variance from fitting the equivalent current dipole. In conclusion, using the EEGLAB's default ICA algorithm, one out of 10 ICs results in flipping its polarity to negative, which is associated with non-radial dipole orientation with higher residual variance. Thus, we determined EEGLAB biases toward positive polarity in decomposing high-quality brain ICs.

EEG network functional connectivity during sleep and in presence of network hyperexcitability in Alzheimer’s disease

AbstractBackgroundFunctional connectivity (FC) studies in Alzheimer’s disease (AD) have focused primarily on the awake resting state. Yet, sleep disturbances in AD are common and occur early in the disease. Network hyperexcitability in AD also arises during sleep, resulting in epileptiform activity and seizures, and impacting memory consolidation. Hence, understanding the FC changes that occur during sleep in AD could provide important biological insights. To date, no studies have examined FC changes occurring during sleep in AD and their relationship with network hyperexcitability.MethodUsing ambulatory scalp EEG recordings, we assessed sleep FC in cognitively normal healthy controls (HC, n = 35), individuals with early clinical stages of AD with no history or risk factors for epilepsy (ADNoEp, n = 44), and individuals with early clinical stages of AD and epilepsy related to AD (ADEp, n = 14). We measured FC using corrected amplitude envelope correlation and assessed FC on different scales, including channel‐pair associations, regional associations, seed‐based connectivity, and graph theory metrics. Machine learning classifiers were developed to determine whether sleep FC measures provide accurate discrimination between HC, ADNoEp, and ADEp.ResultThere were no significant differences in age, sex, or education between HC, ADNoEp, and ADEp. Compared to HC, the ADNoEp group had 1) reduced FC during REM sleep in the alpha band in posterior regions; and 2) increased FC during NREM2 sleep in the gamma‐band in frontal regions. Compared to ADNoEp, the ADEp group had reduced FC in NREM2 sleep in the delta‐band in temporal and posterior regions. Graph theory metrics corroborated these sleep stage‐ and frequency‐specific findings between groups. Machine learning classifiers using graph theory metrics as input features distinguished ADNoEp from HC, and ADNoEp from ADEp, with high performance (AUC&gt;0.85).ConclusionAD is associated with sleep stage‐, frequency band‐, and region‐specific changes in FC. Network hyperexcitability in ADEp is also associated with changes in sleep FC, which are distinct from those seen in ADNoEp. Our findings expand understanding of brain network changes in AD to the sleep state and demonstrate sleep‐stage specific alterations in FC that may have important implications for understanding AD neurophysiology and for disease monitoring and therapeutic development.

Prediction and analysis of chronic epilepsy using electroencephalographic signals on medical internet of things platform

Epilepsy patients who are presently refractory may be monitored using a seizure prediction Brain-Computer Interface (BCI), which uses electrodes strategically implanted in the brain to anticipate and regulate the onset and duration of a seizure. Real-time approaches to these technologies have challenges, as seen by seizures’ instantaneous electrographic activity. Electroencephalographic (EEG) signals are inherently non-stationary, which means that the regular and seizure signals differ significantly among people with epilepsy. Due to the restricted number of contacts on electrodes, dynamically processed and collected characteristics cannot be employed in a prediction function without causing significant processing delays. Big data can guarantee secure storage in these situations, and it has the maximum processing capability to identify, record, and analyze time in real-time to conduct the seizure event on the timetable. Seizure prediction and location for huge Scalp EEG recordings have been the focus of this study, which used wearable sensor data and deep learning to use cloud storage to develop the systems. A novel technique is suggested to avoid an epileptic seizure and discover the seizure origin from the utilized wearable sensors. Secondly, deep learning architectures called Clustered Autoencoder with Convolutional Neural Network (CAE-CNN), an expanded optimization methodology is presented based on the Principal Component Analysis (PCA), the Hierarchical Searching Algorithm (HSA), and the Medical Internet of Things (MIoT) has been established to define the suggested frameworks based on the collection of big data storage of the wearable sensors in real-time, automatic computation and storage. According to clinical trials, CAE-CNN outperforms the current wearable sensor-based treatment for unresolved chronic epilepsy patients.

A Two-Stage Automatic System for Detection of Interictal Epileptiform Discharges from Scalp Electroencephalograms.

The objective of this work was to develop a deep learning-based automatic system with reliable performance in detecting interictal epileptiform discharges (IEDs) from scalp electroencephalograms (EEGs). For the present study, 484 raw scalp EEG recordings were included, standardized, and split into 406 for training and 78 for testing. Two neurophysiologists individually annotated the recordings for training in channel-wise manner. Annotations were divided into segments, on which nine deep neural networks (DNNs) were trained for the multiclassification of IED, artifact, and background. The fitted IED detectors were then evaluated on 78 EEG recordings with IED events fully annotated by three experts independently (majority agreement). A two montage-based decision mechanism (TMDM) was designed to determine whether an IED event occurred at a single time instant. Area under the precision-recall curve (AUPRC), as well as false-positive rates, F1 scores, and kappa agreement scores for sensitivity = 0.8 were estimated. In multitype classification, five DNNs provided one-versus-rest AUPRC mean value >0.993 using fivefold cross-validation. In IED detection, the system that had integrated the temporal convolutional network (TCN)-based IED detector and the TMDM rule achieved an AUPRC of 0.811. The false positive was 0.194/min (11.64/h), and the F1 score was 0.745. The agreement score between the system and the experts was 0.905. The proposed framework provides a TCN-based IED detector and a novel two montage-based determining mechanism that combined to make an automatic IED detection system. The system would be useful in aiding clinic EEG interpretation.

Comparison of Automated Spike Detection Software in Detecting Epileptiform Abnormalities on Scalp-EEG of Genetic Generalized Epilepsy Patients.

Despite availability of commercial EEG software for automated epileptiform detection, validation on real-world EEG datasets is lacking. Performance evaluation of two software packages on a large EEG dataset of patients with genetic generalized epilepsy was performed. Three epileptologists labelled IEDs manually of EEGs from three centres. All Interictal epileptiform discharge (IED) markings predicted by two commercial software (Encevis 1.11 and Persyst 14) were reviewed individually to assess for suspicious missed markings and were integrated into the reference standard if overlooked during manual annotation during a second phase. Sensitivity, precision, specificity, and F1-score were used to assess the performance of the software packages against the adjusted reference standard. One hundred and twenty-five routine scalp EEG recordings from different subjects were included (total recording time, 310.7 hours). The total epileptiform discharge reference count was 5,907 (including spikes and fragments). Encevis demonstrated a mean sensitivity for detection of IEDs of 0.46 (SD 0.32), mean precision of 0.37 (SD 0.31), and mean F1-score of 0.43 (SD 0.23). Using the default medium setting, the sensitivity of Persyst was 0.67 (SD 0.31), with a precision of 0.49 (SD 0.33) and F1-score of 0.51 (SD 0.25). Mean specificity representing non-IED window identification and classification was 0.973 (SD 0.08) for Encevis and 0.968 (SD 0.07) for Persyst. Automated software shows a high degree of specificity for detection of nonepileptiform background. Sensitivity and precision for IED detection is lower, but may be acceptable for initial screening in the clinical and research setting. Clinical caution and continuous expert human oversight are recommended with all EEG recordings before a diagnostic interpretation is provided based on the output of the software.

Frontal-midline theta and posterior alpha oscillations index early processing of spatial representations during active navigation

Previous research has demonstrated that humans combine multiple sources of spatial information such as self-motion and landmark cues while navigating through an environment. However, it is unclear whether this involves comparing multiple representations obtained from different sources during navigation (parallel hypothesis) or building a representation first based on self-motion cues and then combining with landmarks later (serial hypothesis). We tested these two hypotheses (parallel vs serial) in an active navigation task using wireless mobile scalp EEG recordings. Participants walked through an immersive virtual hallway with or without conflicts between self-motion and landmarks (i.e., intersections) and pointed toward the starting position of the hallway. We employed the oscillatory signals recorded during mobile wireless scalp EEG as a means of identifying when participant representations based on self-motion versus landmark cues might have first emerged. We found that path segments, including intersections present early during navigation, were more strongly associated with later pointing error, regardless of when they appeared during encoding. We also found that there was sufficient information contained within the frontal-midline theta and posterior alpha oscillatory signals in the earliest segments of navigation involving intersections to decode condition (i.e., conflicting vs not conflicting). Together, these findings suggest that intersections play a pivotal role in the early development of spatial representations, suggesting that memory representations for the geometry of walked paths likely develop early during navigation, in support of the parallel hypothesis.

Brain-Computer Interface: The HOL-SSA Decomposition and Two-Phase Classification on the HGD EEG Data.

An efficient processing approach is essential for increasing identification accuracy since the electroencephalogram (EEG) signals produced by the Brain-Computer Interface (BCI) apparatus are nonlinear, nonstationary, and time-varying. The interpretation of scalp EEG recordings can be hampered by nonbrain contributions to electroencephalographic (EEG) signals, referred to as artifacts. Common disturbances in the capture of EEG signals include electrooculogram (EOG), electrocardiogram (ECG), electromyogram (EMG) and other artifacts, which have a significant impact on the extraction of meaningful information. This study suggests integrating the Singular Spectrum Analysis (SSA) and Independent Component Analysis (ICA) methods to preprocess the EEG data. The key objective of our research was to employ Higher-Order Linear-Moment-based SSA (HOL-SSA) to decompose EEG signals into multivariate components, followed by extracting source signals using Online Recursive ICA (ORICA). This approach effectively improves artifact rejection. Experimental results using the motor imagery High-Gamma Dataset validate our method's ability to identify and remove artifacts such as EOG, ECG, and EMG from EEG data, while preserving essential brain activity.