Comparative analysis of machine learning algorithms for Alzheimer's disease classification using EEG signals and genetic information.

Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by cognitive decline, memory impairments, and behavioral changes. The presence of abnormal beta-amyloid plaques and tau protein tangles in the brain is known to be associated with AD. However, current limitations of imaging technology hinder the direct detection of these substances. Consequently, researchers are exploring alternative approaches, such as indirect assessments involving monitoring brain signals, cognitive decline levels, and blood biomarkers. Recent studies have highlighted the potential of integrating genetic information into these approaches to enhance early detection and diagnosis, offering a more comprehensive understanding of AD pathology beyond the constraints of existing imaging methods. Our study utilized electroencephalography (EEG) signals, genotypes, and polygenic risk scores (PRSs) as features for machine learning models. We compared the performance of gradient boosting (XGB), random forest (RF), and support vector machine (SVM) to determine the optimal model. Statistical analysis revealed significant correlations between EEG signals and clinical manifestations, demonstrating the ability to distinguish the complexity of AD from other diseases by using genetic information. By integrating EEG with genetic data in an SVM model, we achieved exceptional classification performance, with an accuracy of 0.920 and an area under the curve of 0.916. This study presents a novel approach of utilizing real-time EEG data and genetic background information for multimodal machine learning. The experimental results validate the effectiveness of this concept, providing deeper insights into the actual condition of patients with AD and overcoming the limitations associated with single-oriented data.

Authentication with a one-dimensional CNN model using EEG-based brain-computer interface.

Brain-computer interface (BCI) technology uses electroencephalogram (EEG) signals to create a direct interaction between the human body and its surroundings. Motor imagery (MI) classification using EEG signals is an important application that can help a rehabilitated or motor-impaired stroke patient perform certain tasks. Robust classification of these signals is an important step toward making the use of EEG more practical in many applications and less dependent on trained professionals. Deep learning methods have produced impressive results in BCI in recent years, especially with the availability of large electroencephalography (EEG) data sets. Dealing with EEG-MI signals is difficult because noise and other signal sources can interfere with the electrical amplitude of the brain, and its generalization ability is limited, so it is difficult to improve EEG classifiers. To address these issues, this paper presents a methodology based on one-dimensional convolutional neural networks (1-D CNN) for motor imagery (MI) recognition for the right hand, left hand, feet, and sedentary task. The proposed model is a lightweight model with fewer parameters and has an accuracy of 91.75%. Then, in an innovative exploitation of the four output classes, there is an idea that allows people with disabilities who are deprived of security measures, such as entering a secret code, to use the output classification, such as password codes. It is also an idea for a unique authentication system that is more secure and less vulnerable to theft or the like for a healthy person at the same time.

Attention-guided graph structure learning network for EEG-enabled auditory attention detection.

Humans possess the remarkable ability to selectively focus on one sound source in a cocktail party scenario. Decoding auditory attention from brain signals is essential for the development of neuro-steered hearing aids. However, it remains challenging to extract discriminative feature representation from electroencephalography (EEG) signals for auditory attention detection (AAD) tasks, and most methods ignore the intrinsic relationship between different EEG channels. To address these challenges, we propose a novel attention-guided graph structure learning network, AGSLnet, which leverages potential relationships between EEG channels to improve AAD performance. Specifically, AGSLnet is designed to dynamically capture latent relationships between channels and construct a graph structure of EEG signals. We evaluated AGSLnet on two publicly available AAD datasets and demonstrated its superiority and robustness over state-of-the-art models. Furthermore, visualization of the graph structure trained by AGSLnet supports previous neuroscience findings, enhancing our understanding of the underlying neural mechanisms.

Learning optimal biomarker-guided treatment policy for chronic disorders.

Electroencephalogram (EEG) provides noninvasive measures of brain activity and is found to be valuable for the diagnosis of some chronic disorders. Specifically, pre-treatment EEG signals in the alpha and theta frequency bands have demonstrated some association with antidepressant response, which is well-known to have a low response rate. We aim to design an integrated pipeline that improves the response rate of patients with major depressive disorder by developing a treatment policy guided by the resting state pre-treatment EEG recordings and other treatment effects modifiers. First, we design an innovative automatic site-specific EEG preprocessing pipeline to extract features with stronger signals than raw data. We then estimate the conditional average treatment effect (CATE) using causal forests and use a doubly robust technique to improve efficiency in the estimation of the average treatment effect. We present evidence of heterogeneity in the treatment effect and the modifying power of the EEG features, as well as a significant average treatment effect, a result that cannot be obtained with conventional methods. Finally, we employ an efficient policy learning algorithm to learn an optimal depth-2 treatment assignment decision tree and compare its performance with Q-Learning and outcome-weighted learning via simulation studies and an application to a large multi-site, double-blind, randomized controlled clinical trial, EMBARC.

Classification of motor imagery using chaotic entropy based on sub-band EEG source localization.

Objective.Electroencephalography (EEG) has been widely used in motor imagery (MI) research by virtue of its high temporal resolution and low cost, but its low spatial resolution is still a major criticism. The EEG source localization (ESL) algorithm effectively improves the spatial resolution of the signal by inverting the scalp EEG to extrapolate the cortical source signal, thus enhancing the classification accuracy.Approach.To address the problem of poor spatial resolution of EEG signals, this paper proposed a sub-band source chaotic entropy feature extraction method based on sub-band ESL. Firstly, the preprocessed EEG signals were filtered into 8 sub-bands. Each sub-band signal was source localized respectively to reveal the activation patterns of specific frequency bands of the EEG signals and the activities of specific brain regions in the MI task. Then, approximate entropy, fuzzy entropy and permutation entropy were extracted from the source signal as features to quantify the complexity and randomness of the signal. Finally, the classification of different MI tasks was achieved using support vector machine.Main result.The proposed method was validated on two MI public datasets (brain-computer interface (BCI) competition III IVa, BCI competition IV 2a) and the results showed that the classification accuracies were higher than the existing methods.Significance.The spatial resolution of the signal was improved by sub-band EEG localization in the paper, which provided a new idea for EEG MI research.

Bridging Genetic Insights with Neuroimaging in Autism Spectrum Disorder-A Systematic Review.

Autism Spectrum Disorder (ASD) is an early onset neurodevelopmental disorder characterized by impaired social interaction and communication, and repetitive patterns of behavior. Family studies show that ASD is highly heritable, and hundreds of genes have previously been implicated in the disorder; however, the etiology is still not fully clear. Brain imaging and electroencephalography (EEG) are key techniques that study alterations in brain structure and function. Combined with genetic analysis, these techniques have the potential to help in the clarification of the neurobiological mechanisms contributing to ASD and help in defining novel therapeutic targets. To further understand what is known today regarding the impact of genetic variants in the brain alterations observed in individuals with ASD, a systematic review was carried out using Pubmed and EBSCO databases and following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. This review shows that specific genetic variants and altered patterns of gene expression in individuals with ASD may have an effect on brain circuits associated with face processing and social cognition, and contribute to excitation-inhibition imbalances and to anomalies in brain volumes.

An Analytical Approach for Naturalistic Cooperative and Competitive EEG-Hyperscanning Data: A Proof-of-Concept Study.

Investigating the neural mechanisms underlying both cooperative and competitive joint actions may have a wide impact in many social contexts of human daily life. An effective pipeline of analysis for hyperscanning data recorded in a naturalistic context with a cooperative and competitive motor task has been missing. We propose an analytical pipeline for this type of joint action data, which was validated on electroencephalographic (EEG) signals recorded in a proof-of-concept study on two dyads playing cooperative and competitive table tennis. Functional connectivity maps were reconstructed using the corrected imaginary part of the phase locking value (ciPLV), an algorithm suitable in case of EEG signals recorded during turn-based competitive joint actions. Hyperbrain, within-, and between-brain functional connectivity maps were calculated in three frequency bands (i.e., theta, alpha, and beta) relevant during complex motor task execution and were characterized with graph theoretical measures and a clustering approach. The results of the proof-of-concept study are in line with recent findings on the main features of the functional networks sustaining cooperation and competition, hence demonstrating that the proposed pipeline is promising tool for the analysis of joint action EEG data recorded during cooperation and competition using a turn-based motor task.

Enhanced EEG power density during painful stretching in individuals with cerebral palsy.

BACKGROUND: Pain perception mechanisms in cerebral palsy remain largely unclear. AIMS: This study investigates brain activity in adults with cerebral palsy during painful and non-painful stretching to elucidate their pain processing characteristics. METHODS AND PROCEDURES: Twenty adults with cerebral palsy and 20 controls underwent EEG in three conditions: rest, non-painful stretching, and painful stretching. Time-frequency power density of theta, alpha, and beta waves in somatosensory and frontal cortices was analyzed, alongside baseline pressure pain thresholds. OUTCOMES AND RESULTS: Cerebral palsy individuals exhibited higher theta, alpha, and beta power density in both cortices during painful stretching compared to rest, and lower during non-painful stretching. Controls showed higher power density during non-painful stretching but lower during painful stretching. Cerebral palsy individuals had higher pain sensitivity, with those more sensitive experiencing greater alpha power density. CONCLUSIONS AND IMPLICATIONS: These findings confirm alterations in the cerebral processing of pain in individuals with cerebral palsy. This knowledge could enhance future approaches to the diagnosis and treatment of pain in this vulnerable population.

Spatio-temporal modulation of cortical activity during motor deadaptation depends on the feedback of task-related error.

Motor adaptations are responsible for recalibrating actions and facilitating the achievement of goals in a constantly changing environment. Once consolidated, the decay of motor adaptation is a process affected by available sensory information during deadaptation. However, the cortical response to task error feedback during the deadaptation phase has received little attention. Here, we explored changes in brain cortical responses due to feedback of task-related error during deadaptation. Twelve healthy volunteers were recruited for the study. Right hand movement and EEG were recorded during repetitive trials of a hand reaching movement. A visuomotor rotation of 30° was introduced to induce motor adaptation. Volunteers participated in two experimental sessions organized in baseline, adaptation, and deadaptation blocks. In the deadaptation block, the visuomotor rotation was removed, and visual feedback was only provided in one session. Performance was quantified using angle end-point error, averaged speed, and movement onset time. A non-parametric spatiotemporal cluster-level permutation test was used to analyze the EEG recordings. During deadaptation, participants experienced a greater error reduction when feedback of the cursor was provided. The EEG responses showed larger activity in the left centro-frontal parietal areas during the deadaptation block when participants received feedback, as opposed to when they did not receive feedback. Centrally distributed clusters were found for the adaptation and deadaptation blocks in the absence of visual feedback. The results suggest that visual feedback of the task-related error activates cortical areas related to performance monitoring, depending on the accessible sensory information.

"Counting sheep PSG": EEGLAB-compatible open-source matlab software for signal processing, visualization, event marking and staging of polysomnographic data.

BACKGROUND: Progress in advancing sleep research employing polysomnography (PSG) has been negatively impacted by the limited availability of widely available, open-source sleep-specific analysis tools. NEW METHOD: Here, we introduce Counting Sheep PSG, an EEGLAB-compatible software for signal processing, visualization, event marking and manual sleep stage scoring of PSG data for MATLAB. RESULTS: Key features include: (1) signal processing tools including bad channel interpolation, down-sampling, re-referencing, filtering, independent component analysis, artifact subspace reconstruction, and power spectral analysis, (2) customizable display of polysomnographic data and hypnogram, (3) event marking mode including manual sleep stage scoring, (4) automatic event detections including movement artifact, sleep spindles, slow waves and eye movements, and (5) export of main descriptive sleep architecture statistics, event statistics and publication-ready hypnogram. COMPARISON WITH EXISTING METHODS: Counting Sheep PSG was built on the foundation created by sleepSMG (https://sleepsmg.sourceforge.net/). The scope and functionalities of the current software have made significant advancements in terms of EEGLAB integration/compatibility, preprocessing, artifact correction, event detection, functionality and ease of use. By comparison, commercial software can be costly and utilize proprietary data formats and algorithms, thereby restricting the ability to distribute and share data and analysis results. CONCLUSIONS: The field of sleep research remains shackled by an industry that resists standardization, prevents interoperability, builds-in planned obsolescence, maintains proprietary black-box data formats and analysis approaches. This presents a major challenge for the field of sleep research. The need for free, open-source software that can read open-format data is essential for scientific advancement to be made in the field.

Electrophysiological impact of mental fatigue on brain activity during a bike task: A wavelet analysis approach.

This study explored how mental fatigue affects brain activity during a low-intensity bike task utilising a continuous wavelet transformation in electroencephalography (EEG) analysis. The aim was to examine changes in brain activity potentially linked to central motor commands and to investigate their relationship with ratings of perceived exertion (RPE). In this study, sixteen participants (age: 21 ± 6 y, 7 females, 9 males) underwent one familiarization and two experimental trials in a randomised, blinded, cross-over study design. Participants executed a low-intensity bike task (9 min; 45 rpm; intensity (W): 10 % below aerobic threshold) after performing a mentally fatiguing (individualized 60-min Stroop task) or a control (documentary) task. Physiological (heart rate, EEG) and subjective measures (self-reported feeling of mental fatigue, RPE, cognitive load, motivation) were assessed prior, during and after the bike task. Post-Stroop, self-reported feeling of mental fatigue was higher in the intervention group (EXP) (74 ± 16) than in the control group (CON) (37 ± 17; p < 0.001). No significant differences in RPE during the bike task were observed between conditions. EEG analysis revealed significant differences (p < 0.05) in beta frequency (13-30 Hz) during the bike task, with EXP exhibiting more desynchronization during the pedal push phase and synchronization during the pedal release phase. These results suggest that mental fatigue, confirmed by both subjective and neurophysiological markers, did not significantly impact RPE during the bike task, possibly due to the use of the CR100 scale or absence of a performance outcome. However, EEG data did reveal significant beta band alterations during the task, indicating increased neural effort under mental fatigue. These findings reveal, for the first time, how motor-related brain activity at the motor cortex is impacted during a low-intensity bike task when mentally fatigued.

Identifying the Effect of Cognitive Motivation with the Method Based on Temporal Association Rule Mining Concept.

Being motivated has positive influences on task performance. However, motivation could result from various motives that affect different parts of the brain. Analyzing the motivation effect from all affected areas requires a high number of EEG electrodes, resulting in high cost, inflexibility, and burden to users. In various real-world applications, only the motivation effect is required for performance evaluation regardless of the motive. Analyzing the relationships between the motivation-affected brain areas associated with the task's performance could limit the required electrodes. This study introduced a method to identify the cognitive motivation effect with a reduced number of EEG electrodes. The temporal association rule mining (TARM) concept was used to analyze the relationships between attention and memorization brain areas under the effect of motivation from the cognitive motivation task. For accuracy improvement, the artificial bee colony (ABC) algorithm was applied with the central limit theorem (CLT) concept to optimize the TARM parameters. From the results, our method can identify the motivation effect with only FCz and P3 electrodes, with 74.5% classification accuracy on average with individual tests.

Calibrating Deep Learning Classifiers for Patient-Independent Electroencephalogram Seizure Forecasting.

The recent scientific literature abounds in proposals of seizure forecasting methods that exploit machine learning to automatically analyze electroencephalogram (EEG) signals. Deep learning algorithms seem to achieve a particularly remarkable performance, suggesting that the implementation of clinical devices for seizure prediction might be within reach. However, most of the research evaluated the robustness of automatic forecasting methods through randomized cross-validation techniques, while clinical applications require much more stringent validation based on patient-independent testing. In this study, we show that automatic seizure forecasting can be performed, to some extent, even on independent patients who have never been seen during the training phase, thanks to the implementation of a simple calibration pipeline that can fine-tune deep learning models, even on a single epileptic event recorded from a new patient. We evaluate our calibration procedure using two datasets containing EEG signals recorded from a large cohort of epileptic subjects, demonstrating that the forecast accuracy of deep learning methods can increase on average by more than 20%, and that performance improves systematically in all independent patients. We further show that our calibration procedure works best for deep learning models, but can also be successfully applied to machine learning algorithms based on engineered signal features. Although our method still requires at least one epileptic event per patient to calibrate the forecasting model, we conclude that focusing on realistic validation methods allows to more reliably compare different machine learning approaches for seizure prediction, enabling the implementation of robust and effective forecasting systems that can be used in daily healthcare practice.

Brain Networks Modulation during Simple and Complex Gait: A "Mobile Brain/Body Imaging" Study.

Walking encompasses a complex interplay of neuromuscular coordination and cognitive processes. Disruptions in gait can impact personal independence and quality of life, especially among the elderly and neurodegenerative patients. While traditional biomechanical analyses and neuroimaging techniques have contributed to understanding gait control, they often lack the temporal resolution needed for rapid neural dynamics. This study employs a mobile brain/body imaging (MoBI) platform with high-density electroencephalography (hd-EEG) to explore event-related desynchronization and synchronization (ERD/ERS) during overground walking. Simultaneous to hdEEG, we recorded gait spatiotemporal parameters. Participants were asked to walk under usual walking and dual-task walking conditions. For data analysis, we extracted ERD/ERS in α, ß, and γ bands from 17 selected regions of interest encompassing not only the sensorimotor cerebral network but also the cognitive and affective networks. A correlation analysis was performed between gait parameters and ERD/ERS intensities in different networks in the different phases of gait. Results showed that ERD/ERS modulations across gait phases in the α and ß bands extended beyond the sensorimotor network, over the cognitive and limbic networks, and were more prominent in all networks during dual tasks with respect to usual walking. Correlation analyses showed that a stronger α ERS in the initial double-support phases correlates with shorter step length, emphasizing the role of attention in motor control. Additionally, ß ERD/ERS in affective and cognitive networks during dual-task walking correlated with dual-task gait performance, suggesting compensatory mechanisms in complex tasks. This study advances our understanding of neural dynamics during overground walking, emphasizing the multidimensional nature of gait control involving cognitive and affective networks.

A simulation study: comparing independent component analysis and signal-space projection - source-informed reconstruction for rejecting muscle artifacts evoked by transcranial magnetic stimulation.

Introduction: The combination of transcranial magnetic stimulation (TMS) and electroencephalography (EEG) allows researchers to explore cortico-cortical connections. To study effective connections, the first few tens of milliseconds of the TMS-evoked potentials are the most critical. Yet, TMS-evoked artifacts complicate the interpretation of early-latency data. Data-processing strategies like independent component analysis (ICA) and the combined signal-space projection-source-informed reconstruction approach (SSP-SIR) are designed to mitigate artifacts, but their objective assessment is challenging because the true neuronal EEG responses under large-amplitude artifacts are generally unknown. Through simulations, we quantified how the spatiotemporal properties of the artifacts affect the cleaning performances of ICA and SSP-SIR. Methods: We simulated TMS-induced muscle artifacts and superposed them on pre-processed TMS-EEG data, serving as the ground truth. The simulated muscle artifacts were varied both in terms of their topography and temporal profiles. The signals were then cleaned using ICA and SSP-SIR, and subsequent comparisons were made with the ground truth data. Results: ICA performed better when the artifact time courses were highly variable across the trials, whereas the effectiveness of SSP-SIR depended on the congruence between the artifact and neuronal topographies, with the performance of SSP-SIR being better when difference between topographies was larger. Overall, SSP-SIR performed better than ICA across the tested conditions. Based on these simulations, SSP-SIR appears to be more effective in suppressing TMS-evoked muscle artifacts. These artifacts are shown to be highly time-locked to the TMS pulse and manifest in topographies that differ substantially from the patterns of neuronal potentials. Discussion: Selecting between ICA and SSP-SIR should be guided by the characteristics of the artifacts. SSP-SIR might be better equipped for suppressing time-locked artifacts, provided that their topographies are sufficiently different from the neuronal potential patterns of interest, and that the SSP-SIR algorithm can successfully find those artifact topographies from the high-pass-filtered data. ICA remains a powerful tool for rejecting artifacts that are not strongly time locked to the TMS pulse.

Diagnosis support of major depressive disorder using event-related potentials during affective priming tasks.

Major Depressive Disorder (MDD) is a global problem. Currently, the most common diagnosis is based on criteria susceptible to the subjectivity of the patient and the clinician. A possible solution to this problem is to look for diagnostic biomarkers that can accurately and early detect this mental condition. Some researchers have focused on electroencephalogram (EEG) analysis to identify biomarkers. In this study we used a dataset composed of EEG recordings from 24 subjects with MDD and 29 healthy controls (HC), during the execution of affective priming tasks with three different emotional stimuli (images): fear, sadness, and happiness. We investigated abnormalities in depressed patients using a novel technique, by directly comparing Event-Related Potential (ERP) waveforms to find statistically significant differences between the MMD and HC groups. Compared to the control group (healthy subjects), we found out that for the emotions fear and happiness there is a decrease in cortical activity at temporal regions in MDD patients. Just the opposite, for the emotion sadness, an increase in MDD brain activity occurs in frontal and occipital regions. Our findings suggest that emotions regulate the attentional control of cognitive processing and are promising for clinical application in diagnosing patients with MDD more objectively.

Neurodevelopment of children exposed to prolonged anesthesia in infancy: GABA study interim analysis of resting-state brain networks at 2, 4, and 10-months old.

BACKGROUND: Previous studies have raised concerns regarding neurodevelopmental impacts of early exposures to general anesthesia and surgery. Electroencephalography (EEG) can be used to study ontogeny of brain networks during infancy. As a substudy of an ongoing study, we examined measures of functional connectivity in awake infants with prior early and prolonged anesthetic exposures and in control infants. METHODS: EEG functional connectivity was assessed using debiased weighted phase lag index at source and sensor levels and graph theoretical measures for resting state activity in awake infants in the early anesthesia (n = 26 at 10 month visit, median duration of anesthesia = 4 [2, 7 h]) and control (n = 38 at 10 month visit) groups at ages approximately 2, 4 and 10 months. Theta and low alpha frequency bands were of primary interest. Linear mixed models incorporated impact of age and cumulative hours of general anesthesia exposure. RESULTS: Models showed no significant impact of cumulative hours of general anesthesia exposure on debiased weighted phase lag index, characteristic path length, clustering coefficient or small-worldness (conditional R2 0.05-0.34). An effect of age was apparent in many of these measures. CONCLUSIONS: We could not demonstrate significant impact of general anesthesia in the first months of life on early development of resting state brain networks over the first postnatal year. Future studies will explore these networks as these infants grow older.

Getting closer to social interactions using electroencephalography in developmental cognitive neuroscience.

The field of developmental cognitive neuroscience is advancing rapidly, with large-scale, population-wide, longitudinal studies emerging as a key means of unraveling the complexity of the developing brain and cognitive processes in children. While numerous neuroscientific techniques like functional magnetic resonance imaging (fMRI), functional near-infrared spectroscopy (fNIRS), magnetoencephalography (MEG), and transcranial magnetic stimulation (TMS) have proved advantageous in such investigations, this perspective proposes a renewed focus on electroencephalography (EEG), leveraging underexplored possibilities of EEG. In addition to its temporal precision, low costs, and ease of application, EEG distinguishes itself with its ability to capture neural activity linked to social interactions in increasingly ecologically valid settings. Specifically, EEG can be measured during social interactions in the lab, hyperscanning can be used to study brain activity in two (or more) people simultaneously, and mobile EEG can be used to measure brain activity in real-life settings. This perspective paper summarizes research in these three areas, making a persuasive argument for the renewed inclusion of EEG into the toolkit of developmental cognitive and social neuroscientists.

Fast Fractional Fourier Transform-Aided Novel Graphical Approach for EEG Alcoholism Detection.

Given its detrimental effect on the brain, alcoholism is a severe disorder that can produce a variety of cognitive, emotional, and behavioral issues. Alcoholism is typically diagnosed using the CAGE assessment approach, which has drawbacks such as being lengthy, prone to mistakes, and biased. To overcome these issues, this paper introduces a novel paradigm for identifying alcoholism by employing electroencephalogram (EEG) signals. The proposed framework is divided into various steps. To begin, interference and artifacts in the EEG data are removed using a multiscale principal component analysis procedure. This cleaning procedure contributes to information quality improvement. Second, an innovative graphical technique based on fast fractional Fourier transform coefficients is devised to visualize the chaotic character and complexities of the EEG signals. This elucidates the properties of regular and alcoholic EEG signals. Third, thirty-four graphical features are extracted to interpret the EEG signals' haphazard behavior and differentiate between regular and alcoholic trends. Fourth, we propose an ensembled feature selection method for obtaining an effective and reliable feature group. Following that, we study many neural network classifiers to choose the optimal classifier for building an efficient framework. The experimental findings show that the suggested method obtains the best classification performance by employing a recurrent neural network (RNN), with 97.5% accuracy, 96.7% sensitivity, and 98.3% specificity for the sixteen selected features. The proposed framework can aid physicians, businesses, and product designers to develop a real-time system.