EEG Components Research Articles

Implications of Aperiodic and Periodic EEG Components in Classification of Major Depressive Disorder from Source and Electrode Perspectives.

Electroencephalography (EEG) is useful for studying brain activity in major depressive disorder (MDD), particularly focusing on theta and alpha frequency bands via power spectral density (PSD). However, PSD-based analysis has often produced inconsistent results due to difficulties in distinguishing between periodic and aperiodic components of EEG signals. We analyzed EEG data from 114 young adults, including 74 healthy controls (HCs) and 40 MDD patients, assessing periodic and aperiodic components alongside conventional PSD at both source and electrode levels. Machine learning algorithms classified MDD versus HC based on these features. Sensor-level analysis showed stronger Hedge's g effect sizes for parietal theta and frontal alpha activity than source-level analysis. MDD individuals exhibited reduced theta and alpha activity relative to HC. Logistic regression-based classifications showed that periodic components slightly outperformed PSD, with the best results achieved by combining periodic and aperiodic features (AUC = 0.82). Strong negative correlations were found between reduced periodic parietal theta and frontal alpha activities and higher scores on the Beck Depression Inventory, particularly for the anhedonia subscale. This study emphasizes the superiority of sensor-level over source-level analysis for detecting MDD-related changes and highlights the value of incorporating both periodic and aperiodic components for a more refined understanding of depressive disorders.

Identification of the emotional component of inner pronunciation: EEG-ERP study

The article discusses the problems of identifying the emotional component of inner pronunciation using psychophysiological methods. In the course of preliminary analysis, P200, N400 and LPC were identified, associated with various parameters of prosody regulation during inner pronunciation. An experimental study of inner pronunciation using event-related potentials from EEG was conducted to isolate these components. In addition, a new method of localizing sources of activity using EEG “virtually implanted electrode” was applied in order to study possible sources of the isolated components. The results show the connection of EEG components with various characteristics of prosody (P200 − the beginning of prosody encoding, N400 − the valence of the emotion, LPC − the intensity of the emotion). Based on the results, the participation of various brain structures in the generation of each of the components was also analyzed.

The electrophysiology of sequential adjustments of dual-task order coordination.

Performing two tasks simultaneously involves the coordination of their processing. This task coordination is particularly required in dual-task situations with varying task orders. When task order switches between subsequent trials, task order coordination leads to task order switch costs in comparison with order repetitions. However, it is open, whether task order coordination is exclusively controlled by the relation of the task orders of the current and the previous trials, or whether additional conditions such as task order before the previous trial leads to a behavioral and neural adjustment of task order coordination. To answer this question, we reanalyzed the data of two previously published experiments with order-cued dual-task paradigms. We did so with regard to whether task order switch costs and the EEG component order-switch positivity in the current dual-task trial would be modulated by order switches vs. repetitions in the previous trial (Trial N-1). In Experiment 1, we found a modulation of the task order switch costs in RTs and response reversals; these costs were reduced after an order switch compared with order repetitions in Trial N-1. In Experiment 2, there were no effects on the task order switch costs in the behavioral data. Nonetheless, we found the order-switch positivity to be strongly modulated by the order transition of the previous trial in both experiments. The order-switch positivity was substantially reduced if the previous trial was an order switch (compared to an order repetition) by itself. This implies that order coordination of dual tasks is adjusted in a gradual way depending on trial's history.

Design of an fNIRS-EEG hybrid terminal for wearable BCI systems.

The importance of brain-computer interfaces (BCI) is increasing, and various methods have been developed. Among the developed BCI methods, functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG) are favored due to their non-invasive feature and compact device sizes. EEG monitors the electrical potentials generated by the activation of neurons, and fNIRS monitors the blood flow also generated by neurons, resulting in signals with different properties between the two methods. As the two BCI methods greatly differ in the characteristics of the acquired neural activity signals, for cases of estimating the intention or thought of a subject by BCI, it has been proven that further accurate information may be extracted by utilizing both methods simultaneously. Both systems are powered by electricity, and as EEG systems are greatly sensitive to electrical noises, application of two separate fNIRS and EEG systems together may result in electrical interference as the systems are required to be in contact with the skin and stray currents from the fNIRS system may flow along the surface of the skin into the EEG system. This research proposes a wearable fNIRS-EEG hybrid BCI system, where a single terminal is capable of operating both as a continuous wave fNIRS emitter and as a detector, and also as an EEG electrode. The system has been designed such that the fNIRS and EEG components are electrically separated to avoid electrical interference between each other. It is expected that by utilizing the developed fNIRS-EEG hybrid terminals, the development of BCI analysis may be further accelerated in various fields.

Investigation of the behavior of tinnitus patients under varying listening conditions with simultaneous electroencephalography and pupillometry.

This study aims to control all hearing thresholds, including extended high frequencies (EHFs), presents stimuli of varying difficulty levels, and measures electroencephalography (EEG) and pupillometry responses to determine whether listening difficulty in tinnitus patients is effort or fatigue-related. Twenty-one chronic tinnitus patients and 26 matched healthy controls having normal pure-tone averages with symmetrical hearing thresholds were included. Subjects were evaluated with 0.125-20kHz pure-tone audiometry, Montreal Cognitive Assessment Test (MoCA), Tinnitus Handicap Inventory (THI), EEG, and pupillometry. Pupil dilatation and EEG alpha power during the "encoding" phase of the presented sentence in tinnitus patients were less in all listening conditions (p<.05). Also, there was no statistically significant relationship between EEG and pupillometry components for all listening conditions and THI or MoCA (p>.05). EEG and pupillometry results under various listening conditions indicate potential listening effort in tinnitus patients even if all frequencies, including EHFs, are controlled. Also, we suggest that pupillometry should be interpreted with caution in autonomic nervous system-related conditions such as tinnitus.

PSAEEGNet: pyramid squeeze attention mechanism-based CNN for single-trial EEG classification in RSVP task.

Accurate classification of single-trial electroencephalogram (EEG) is crucial for EEG-based target image recognition in rapid serial visual presentation (RSVP) tasks. P300 is an important component of a single-trial EEG for RSVP tasks. However, single-trial EEG are usually characterized by low signal-to-noise ratio and limited sample sizes. Given these challenges, it is necessary to optimize existing convolutional neural networks (CNNs) to improve the performance of P300 classification. The proposed CNN model called PSAEEGNet, integrates standard convolutional layers, pyramid squeeze attention (PSA) modules, and deep convolutional layers. This approach arises the extraction of temporal and spatial features of the P300 to a finer granularity level. Compared with several existing single-trial EEG classification methods for RSVP tasks, the proposed model shows significantly improved performance. The mean true positive rate for PSAEEGNet is 0.7949, and the mean area under the receiver operating characteristic curve (AUC) is 0.9341 (p < 0.05). These results suggest that the proposed model effectively extracts features from both temporal and spatial dimensions of P300, leading to a more accurate classification of single-trial EEG during RSVP tasks. Therefore, this model has the potential to significantly enhance the performance of target recognition systems based on EEG, contributing to the advancement and practical implementation of target recognition in this field.

The role of the target language culture on Arabic learners' fondness for Arabic poetry.

As an important carrier of culture, poetry plays a significant role in deepening language learners' understanding of the target language culture as well as enhancing their language skills; however, the effect of the target language culture on language learners' enjoyment of poetry remains unclear. The study served as an attempt to shed light on the point of whether the target language culture has different effects on high- and low-level Chinese Arabic learners' fondness for Arabic poetry with the use of pictures related to Arabic culture and those not related to Arabic culture. In the current study, 40 Arabic learners (20 high-level and 20 low-level) scored the Arabic poem line based on their fondness for it after viewing two kinds of picture with electroencephalogram (EEG) recording. Frontal alpha asymmetry index as a correlate of approach and avoidance related motivation measured by EEG power in the alpha band (8-13 Hz) was calculated for examining whether the behavioral results of Arabic learners' fondness for poetry are in line with the results of changes in the related EEG components. Behavioral results illustrated that low-level subjects showed significantly less liking for Arabic poetry after viewing pictures related to Arabic culture compared to those not related to Arabic culture. The high-level subjects did not show a significant difference in the level of liking for Arabic poetry between the two cases. FAA results demonstrated that low-level subjects presented a significant avoidance-related responses to Arabic poetry after viewing pictures related to Arabic culture in comparison to viewing pictures not related to Arabic culture; while the FAA values did not differ significantly between the two cases in high-level subjects, which is in line with behavioral results. The findings of this research can benefit teachers in motivating students to learn poetry in foreign language curriculum and also contribute to the literature on the effect of target language culture on language learners' enjoyment of poetry.

Diversity and limitations of electroencephalogram and event-related potential applications in nursing research: A scoping review.

This scoping review was conducted to provide a comprehensive summary of how electroencephalogram (EEG) and event-related potentials (ERPs) have been used in nursing research, with the goal of mapping the themes and methods of nursing research involving EEGs or ERPs as a measurement tool. The eligibility criteria were determined according to the Population, Concept, and Context principle. A systematic electronic search of articles in the PubMed, Web of Science, Embase, CINAHL, APA PsycInfo, and Scopus databases was carried out for the period from database establishment to November 21, 2022. The included studies were analyzed using descriptive statistics and content analysis. The review process culminated in 45 articles, evidencing an increasing trend and dispersion characteristics of EEG in nursing research and reflecting five thematic domains of inquiry related to nursing. There was a deficiency of detailed reports of EEG recording and data analysis parameters in nursing research. The common EEG bands in nursing research were Delta, Theta, Alpha, Beta, Gamma. The ERP components used frequently were P3, P2, N1, N2, P1, N170, and feedback-related negativity. The wide variety of EEG components used show broad potential for studying nursing questions. In the future, it will be necessary to increase the depth of the research content, the repeatability of the experiment and the standardization of the report. Nursing researchers should give full play to the characteristics of nursing and establish a systematic and complete EEG research system for nursing.

D‐Unet: A symmetric architecture of convolutional neural network with two auxiliary outputs for dementia recognition

AbstractDementia‐associated disorders cause damage to the brains of patients and bring huge burdens to individuals and families. Electroencephalogram (EEG) monitoring is friendly to patients on account of low cost, non‐invasion, and objective. Event‐related potential (ERP) is a component of EEG that has huge potential to evaluate the cognitive function of the brain. In this study, we recorded the ERP from patients with dementia and healthy people, then proposed an ERP‐based deep learning method to realize dementia recognition via the model Dementia‐Unet (D‐Unet). To improve the performance of the model, on the base of the decoder and the primary classifier, we added new structures including a symmetric decoder and two auxiliary outputs. One of the auxiliary outputs was input reconstruction, and the other one was aimed at working like the primary classifier with the same task. The results of the experiment of four‐fold cross‐validation demonstrated the two auxiliary outputs improved the performance of the model effectively. When compared with some other machine learning methods and deep learning methods, our model obtained the best performance with an accuracy of 0.815, a precision of 0.829, a recall of 0.797, and an f1‐score of 0.812. Besides, we put up a complex training strategy with all outputs involved, but a simple testing strategy with only a primary classifier working to keep high performance but cut down the complexity burden during testing.

Emotion recognition with EEG-based brain-computer interfaces: a systematic literature review

Electroencephalography (EEG)-based Brain-Computer Interface (BCI) systems for emotion recognition have the potential to assist the enrichment of human–computer interaction with implicit information since they can enable understanding of the cognitive and emotional activities of humans. Therefore, these systems have become an important research topic today. This study aims to present trends and gaps on this topic by performing a systematic literature review based on the 216 published scientific literature gathered from various databases including ACM, IEEE Xplore, PubMed, Science Direct, and Web of Science from 2016 to 2020. This review gives an overview of all the components of EEG based BCI system from the signal stimulus module which includes the employed device, signal stimuli, and data processing modality, to the signal processing module which includes signal acquisition, pre-processing, feature extraction, feature selection, classification algorithms, and performance evaluation. Thus, this study provides an overview of all components of an EEG-based BCI system for emotion recognition and examines the available evidence in a clear, concise, and systematic way. In addition, the findings are aimed to inform researchers about the issues on what are research trends and the gaps in this field and guide them in their research directions.

Exploring the neural underpinnings of chord prediction uncertainty: an electroencephalography (EEG) study

Predictive processing in the brain, involving interaction between interoceptive (bodily signal) and exteroceptive (sensory) processing, is essential for understanding music as it encompasses musical temporality dynamics and affective responses. This study explores the relationship between neural correlates and subjective certainty of chord prediction, focusing on the alignment between predicted and actual chord progressions in both musically appropriate chord sequences and random chord sequences. Participants were asked to predict the final chord in sequences while their brain activity was measured using electroencephalography (EEG). We found that the stimulus preceding negativity (SPN), an EEG component associated with predictive processing of sensory stimuli, was larger for non-harmonic chord sequences than for harmonic chord progressions. Additionally, the heartbeat evoked potential (HEP), an EEG component related to interoceptive processing, was larger for random chord sequences and correlated with prediction certainty ratings. HEP also correlated with the N5 component, found while listening to the final chord. Our findings suggest that HEP more directly reflects the subjective prediction certainty than SPN. These findings offer new insights into the neural mechanisms underlying music perception and prediction, emphasizing the importance of considering auditory prediction certainty when examining the neural basis of music cognition.

Implementation of Tools for Lessening the Influence of Artifacts in EEG Signal Analysis

This manuscript describes an implementation of scripts of code aimed at reducing the influence of artifacts, specifically focused on ocular artifacts, in the measurement and processing of electroencephalogram (EEG) signals. This process is of importance because it benefits the analysis and study of long trial samples when the appearance of ocular artifacts cannot be avoided by simply discarding trials. The implementations provided to the reader illustrate, with slight modifications, previously proposed methods aimed at the partial or complete elimination of EEG channels or components obtained after independent component analysis (ICA) of EEG signals. These channels or components are those that resemble the electro-oculogram (EOG) signals in which artifacts are detected. In addition to the description of each of the provided functions, examples of utilization and illustrative figures will be included to show the expected results and processing pipeline.

Physiologic Network-Based Brain-Heart Interaction Quantification During Visual Emotional Elicitation.

In recent years, there has been a surge in interest regarding the intricate physiological interplay between the brain and the heart, particularly during emotional processing. This has led to the development of various signal processing techniques aimed at investigating Brain-Heart Interactions (BHI), reflecting a growing appreciation for their bidirectional communication and influence on each other. Our study contributes to this burgeoning field by adopting a network physiology approach, employing time-delay stability as a quantifiable metric to discern and measure the coupling strength between the brain and the heart, specifically during visual emotional elicitation. We extract and transform features from EEG and ECG signals into a 1 Hz format, facilitating the calculation of BHI coupling strength through stability analysis on their maximal cross-correlation. Notably, our investigation sheds light on the critical role played by low-frequency components in EEG, particularly in the δ , θ , and α bands, as essential mediators of information transmission during the complex processing of emotion-related stimuli by the brain. Furthermore, our analysis highlights the pivotal involvement of frontal pole regions, emphasizing the significance of δ - θ coupling in mediating emotional responses. Additionally, we observe significant arousal-dependent changes in the θ frequency band across different emotional states, particularly evident in the prefrontal cortex. By offering novel insights into the synchronized dynamics of cortical and heartbeat activities during emotional elicitation, our research enriches the expanding knowledge base in the field of neurophysiology and emotion research.

Deep Neural Network-Based Empirical Mode Decomposition for Motor Imagery EEG Classification.

Motor imagery refers to the brain's response during the mental simulation of physical activities, which can be detected through electroencephalogram (EEG) signals. However, EEG signals exhibit a low signal-to-noise ratio (SNR) due to various artifacts originating from other physiological sources. To enhance the classification performance of motor imagery tasks by increasing the SNR of EEG signals, several signal decomposition approaches have been proposed. Empirical mode decomposition (EMD) has shown promising results in extracting EEG components associated with motor imagery tasks more effectively than traditional linear decomposition algorithms such as Fourier and wavelet methods. Nevertheless, the EMD-based algorithm suffers from a significant challenge known as mode mixing, where frequency components intertwine with the intrinsic mode functions obtained through EMD. This issue severely hampers the accuracy of motor imagery classification. Despite numerous algorithms proposed, mode mixing remains a persistent issue. In this paper, we propose the Deep-EMD algorithm, a deep neural network-based approach to mode mixing problem. We employ two datasets to compare the motor imagery classification and mode mixing improvement achieved by the conventional EMD algorithms. Our experimental results demonstrate that the Deep-EMD algorithm effectively mitigates the mode mixing problem in decomposed EEG components, leading to improved motor imagery classification performance.

EEG components of inhibitory control ability in internet gaming disorder: A systematic review and meta-analysis of randomized controlled trials.

Inhibitory control ability is a crucial cognitive function that enables individuals to regulate their impulses and behaviors in a goal-directed manner. However, with the increasing prevalence of internet gaming disorder (IGD), there has been growing concern about the impact of excessive gaming on inhibitory control ability. Despite the accumulating evidence on this topic, the research conclusion on whether people with IGD have worse inhibition control ability than healthy controls remains inconsistent, and the lack of effective electroencephalography prediction indicators further complicates this issue. To address this research gap, the present study aimed to investigate whether N2 event-related potential (ERP) and P3 ERP components could serve as reliable indicators of inhibitory control ability in individuals with IGD. To achieve this goal, a systematic literature search was conducted in several databases, including Web of Science, ScienceDirect (EBSCO), SpringerLink, PubMed, and Wiley Online Library. The inclusion criteria were strictly implemented to ensure the quality of the studies included in the meta-analysis. In the end, a total of 5 studies, with 139 participants diagnosed with IGD and 139 healthy controls, were included in the analysis. Meta-analysis revealed large effect sizes of N2 and P3 amplitudes in individuals with IGD, indicating that these two ERP components could be potential indicators of inhibitory control ability. Specifically, the N2 and P3 amplitude was significantly larger in individuals with IGD than in the healthy control group, suggesting deficits in inhibitory control function and increased impulsivity in the IGD group. In the inhibition control task, the IGD group required more cognitive resources to suppress impulsive responses. Overall, the findings of this meta-analysis shed light on the potential use of N2 and P3 amplitudes as reliable indicators of inhibitory control ability in individuals with IGD. The results provide crucial insights into the neural mechanisms underlying inhibitory control impairment in IGD, which could inform the development of effective interventions for this condition. Further research is needed to explore the functional significance of these ERP components and their potential clinical applications in the diagnosis and treatment of IGD.

Subject-Specific Modeling of EEG-fNIRS Neurovascular Coupling by Task-Related Tensor Decomposition.

Neurovascular coupling (NVC) connects neural activity with hemodynamics and plays a vital role in sustaining brain function. Combining electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) is a promising way to explore the NVC. However, the high-order property of EEG data and variability of hemodynamic response function (HRF) across subjects have not been well considered in existing NVC studies. In this study, we proposed a novel framework to enhance the subject-specific parametric modeling of NVC from simultaneous EEG-fNIRS measurement. Specifically, task-related tensor decomposition of high-order EEG data was performed to extract the underlying connections in the temporal-spectral-spatial structures of EEG activities and identify the most relevant temporal signature within multiple trials. Subject-specific HRFs were estimated by parameters optimization of a double gamma function model. A canonical motor task experiment was designed to induce neural activity and validate the effectiveness of the proposed framework. The results indicated that the proposed framework significantly improves the reproducibility of EEG components and the correlation between the predicted hemodynamic activities and the real fNIRS signal. Moreover, the estimated parameters characterized the NVC differences in the task with two speeds. Therefore, the proposed framework provides a feasible solution for the quantitative assessment of the NVC function.

Apathy scores in Parkinson's disease relate to EEG components in an incentivized motor task.

Apathy is one of the most prevalent non-motor symptoms of Parkinson's disease and is characterized by decreased goal-directed behaviour due to a lack of motivation and/or impaired emotional reactivity. Despite its high prevalence, the neurophysiological mechanisms underlying apathy in Parkinson's disease, which may guide neuromodulation interventions, are poorly understood. Here, we investigated the neural oscillatory characteristics of apathy in Parkinson's disease using EEG data recorded during an incentivized motor task. Thirteen Parkinson's disease patients with apathy and 13 Parkinson's disease patients without apathy as well as 12 healthy controls were instructed to squeeze a hand grip device to earn a monetary reward proportional to the grip force they used. Event-related spectral perturbations during the presentation of a reward cue and squeezing were analysed using multiset canonical correlation analysis to detect different orthogonal components of temporally consistent event-related spectral perturbations across trials and participants. The first component, predominantly located over parietal regions, demonstrated suppression of low-beta (12-20 Hz) power (i.e. beta desynchronization) during reward cue presentation that was significantly smaller in Parkinson's disease patients with apathy compared with healthy controls. Unlike traditional event-related spectral perturbation analysis, the beta desynchronization in this component was significantly correlated with clinical apathy scores. Higher monetary rewards resulted in larger beta desynchronization in healthy controls but not Parkinson's disease patients. The second component contained gamma and theta frequencies and demonstrated exaggerated theta (4-8 Hz) power in Parkinson's disease patients with apathy during the reward cue and squeezing compared with healthy controls (HCs), and this was positively correlated with Montreal Cognitive Assessment scores. The third component, over central regions, demonstrated significantly different beta power across groups, with apathetic groups having the lowest beta power. Our results emphasize that altered low-beta and low-theta oscillations are critical for reward processing and motor planning in Parkinson's disease patients with apathy and these may provide a target for non-invasive neuromodulation.

Oxytocin modulates neural activity during early perceptual salience attribution

Leading hypotheses of oxytocin’s (OT) role in human cognition posit that it enhances salience attribution. However, whether OT exerts its effects predominantly in social (vs non-social) contexts remains debatable, and the time-course of intranasal OT’s effects’ on salience attribution processing is still unknown. We used the social Salience Attribution Task modified (sSAT) in a double-blind, placebo-controlled intranasal OT (inOT) administration, between-subjects design, with 54 male participants, to test existing theories of OT’s role in cognition. Namely, we aimed to test whether inOT would differently affect salience attribution processing of social stimuli (expressing fearfulness) and non-social stimuli (fruits) made relevant via monetary reinforcement, and its neural processing time-course. During electroencephalography (EEG) recording, participants made speeded responses to emotional social (fearful faces) and non-emotional non-social (fruits) stimuli - which were matched for task-relevant motivational salience through their (color-dependent) probability of monetary reinforcement. InOT affected early (rather than late, P3b and LPP) EEG components, increasing N170 amplitude (p = .041) and P2b latency (p .001; albeit not of P1), regardless of stimuli’s (emotional) socialness or reinforcement probability. Fear-related socialness affected salience attribution processing EEG (p .05) across time (N170, P2b and P3b), being later modulated by reinforcement probability (LPP). Our data suggest that OT’s effects on neural activity during early perception, may exist irrespective of fear-related social- or reward-contexts. This partially supports the tri-phasic model of OT (which posits OT enhances salience attribution in an early perception stage regardless of socialness), and not the social salience nor the general approach-withdrawal hypotheses of OT, for early salience processing event-related potentials.

Enhancing EEG-based cross-day mental workload classification using periodic component of power spectrum

Objective. The day-to-day variability of electroencephalogram (EEG) poses a significant challenge to decode human brain activity in EEG-based passive brain-computer interfaces (pBCIs). Conventionally, a time-consuming calibration process is required to collect data from users on a new day to ensure the performance of the machine learning-based decoding model, which hinders the application of pBCIs to monitor mental workload (MWL) states in real-world settings. Approach. This study investigated the day-to-day stability of the raw power spectral density (PSD) and their periodic and aperiodic components decomposed by the Fitting Oscillations and One-Over-F algorithm. In addition, we validated the feasibility of using periodic components to improve cross-day MWL classification performance. Main results. Compared to the raw PSD (69.9% ± 18.5%) and the aperiodic component (69.4% ± 19.2%), the periodic component had better day-to-day stability and significantly higher cross-day classification accuracy (84.2% ± 11.0%). Significance. These findings indicate that periodic components of EEG have the potential to be applied in decoding brain states for more robust pBCIs.

Beta to Theta Power Ratio in Electroencephalogram Periodic Components to Discriminate Mild Cognitive Impairment and Alzheimer’s Dementia

AbstractBackgroundAlzheimer’s disease dementia (AD) and mild cognitive impairment (MCI) are often associated with abnormalities in full power spectrum of electroencephalogram (EEG), including the ratio of beta/theta. Full spectrum EEG consists of aperiodic and periodic components with the latter being better associated with cognition. We investigated whether the aperiodic and periodic power components of EEGs are disrupted differently in individuals with MCI vs. AD vs. healthy controls (HC), and whether a periodic based beta/theta ratio better differentiates the three groups than a ratio based on the full spectrum.MethodData were collected in 199 participants ‐ 44 with HC (mean (SD) age: 69.1 (5.3) years), 114 with MCI (72.3 (7.5)), and 41 with AD (75.6 (6.5)). We cleaned the data using a band‐pass filter with cut‐off frequencies 1 and 45 Hz and then independent component analysis. We then used the “fooof” toolbox to decompose the EEGs into their aperiodic and periodic components. We used the area under the receiver operating characteristic curve (AUCROC) of a logistic regression classifier to distinguish HC from MCI and AD participants, and MCI from AD participants, using beta/theta ratios based on the periodic power spectrum vs. full power spectrum.ResultThere was an increase in full spectrum powers for delta, theta, and gamma, and a decrease of relative power for beta in AD participants compared to HC and MCI participants. In contrast, there were no differences in aperiodic background EEG components among HC, MCI, and AD participants. Overall, the periodic and full spectrum comparisons among the three groups were comparable except for the periodic based analysis that showed a difference between MCI and HC in the occipital beta/theta ratio (Bonferroni corrected p = 0.036). Classifiers based on beta/theta power ratio in EEG periodic components distinguished AD from HC and MCI with high AUCROC values (0.94 and 0.83, respectively), and outperformed classifiers based on beta/theta power ratio in EEG all oscillations (0.078 and 0.67, respectively).ConclusionThis study supports an advantage of a periodic based analysis over a full EEG spectrum analysis and the use of occipital beta/theta power ratio based on periodic components as a screening tool for AD.