Assessing the Potential of EEG in Early Detection of Alzheimer’s Disease: A Systematic Comprehensive Review (2000–2023)

Efficacy of EEG in the early detection

The early diagnosis of AD is critical, as it provides a window for potential interventions that could delay the onset of severe symptoms, preserve function, and extend periods of independence. 9 Despite extensive research, definitive diagnosis is often reached at a stage where therapeutic options are limited in their efficacy.10,11, 10,11 Current diagnostic modalities such as magnetic resonance imaging (MRI) and positron emission tomography (PET) scans, while useful, are expensive and not universally accessible. Moreover, these techniques often identify the disease once significant brain changes have occurred, which is typically beyond the stage where interventions are most likely to be beneficial.12,13, 12,13

Amidst this landscape, EEG emerges as a potential beacon of hope. It is a well-established neurophysiological technique that records electrical activity of the brain and has shown promise in detecting the subtle changes in brain function that precede overt clinical symptoms. 2 Research has consistently demonstrated alterations in EEG patterns in individuals with AD compared to healthy controls, including changes in rhythm synchrony, power spectrum, and connectivity. 14 These findings have sparked interest in the development of EEG as a tool for the early diagnosis and monitoring of AD.

The purpose of this comprehensive review is to systematically explore and evaluate the advancements and the potential of EEG as a diagnostic tool for AD spanning from the year 2000 to 2023. This period has been marked by significant advancements in both the understanding of AD pathology and neuroimaging technologies, with EEG analysis methodologies becoming increasingly sophisticated. The review aims to synthesize findings from studies that have employed EEG to identify biomarkers of AD, with a focus on the evolution of these methodologies over time. The objective is to determine the of AD, to assess the progression of the disease, and to understand the potential EEG holds as a routine screening tool in clinical settings. Furthermore, the review intends to identify patterns within EEG data that are indicative of AD and to correlate these patterns with disease progression and response to treatment, thereby facilitating individualized patient care strategies.

The rationale for choosing the timeline from 2000 to 2023 is to encapsulate a period of rapid evolution in EEG technology and analysis techniques, alongside a parallel growth in the understanding of AD. This timeline allows for an examination of the initial hypotheses regarding EEG’s diagnostic potential, through to the recent innovations that have transformed this potential into tangible diagnostic applications. 15 EEG has been selected for focus due to its unique advantages over other diagnostic modalities. It is relatively low-cost, widely available, and can be performed with minimal discomfort to patients, making it an attractive option for widespread screening. 16 Moreover, EEG is sensitive to the earliest changes in brain function, often preceding structural changes seen on MRI or PET scans, thus offering the possibility of an earlier diagnosis of AD.17–19 The review will also underscore the importance of EEG in the broader context of an integrative diagnostic approach. By combining EEG findings with other clinical and biomarker data, there is potential to enhance the diagnostic accuracy and provide a more comprehensive understanding of AD, thus supporting the impetus for further research and innovation in this area.

AD continues to present a formidable challenge in the realm of neurodegenerative disease research, necessitating early and precise diagnostic capabilities for effective management of its progression. EEG, with its non-invasive nature and profound sensitivity to the brain’s electrical dynamics, has advanced as a cornerstone in the investigative exploration of AD. The background of EEG research in AD diagnosis between 2000 and 2023 represents an epoch of transformative development, marked by a synergy of enhanced comprehension of AD’s complexities and technological evolutions in EEG analysis. This epoch has been characterized by a steadily growing recognition of EEG’s ability to detect the early and subtle neurophysiological changes hallmark to AD, establishing EEG as a non-invasive, cost-effective, and widely accessible diagnostictool.

The evolution of the EEG landscape over the past decades has been propelled by pioneering studies focusing on characteristic EEG alterations in AD, which laid the foundational groundwork for the field. These include decreased power across specific frequency bands and disrupted functional connectivity, which have provided early insights into the disease’s impact on brain function.11,20, 11,20 Building upon these initial discoveries, further research has underscored the diagnostic value of quantitative EEG and the analysis of mutual information in AD patients, progressively unraveling the intricate effects of the disease on neural dynamics.20,21, 20,21

An enhanced compressibility of EEG signals in AD patients has been noted, a finding that adds a new dimension to signal analysis by highlighting the altered neurodynamic properties of AD, suggesting a potential for novel data representation and analysis methods. 3 Moreover, novel methodologies employing Hjorth parameters have emerged, offering a promising direction for the early detection of AD from EEG signals and bolstering the argument for EEG’s place in routine screening. 1

Advancements in analytical approaches have facilitated the emergence of automated differential diagnosis methodologies that offer refined mechanisms to distinguish between MCI and AD, leveraging advanced signal processing and classification techniques. 4 Additionally, the examination of EEG signal complexity provides a compelling argument for its inclusion in diagnostic protocols, which is a testament to the analytical toolkit available for AD research that is ever-enriching. 5

The modern landscape of EEG research in AD diagnosis is further characterized by a theoretical framework that aligns with the neurophysiological alterations associated with the disease. The potential of EEG to capture these changes has only grown more promising with the integration of innovative machine learning algorithms and sophisticated analytical methods. The emphasis on integrative diagnostic strategies, combining EEG with other biomarkers and brain imaging techniques, has seen significant progress, aiming to establish a multimodal approach that enhances diagnostic specificity and sensitivity for AD.22,23, 22,23

Current challenges in AD detection, such as the necessity for explainable artificial intelligence (XAI) to justify machine learning-based diagnostics, the quantification of uncertainty in predictive models, and the exploration of federated learning for collaborative research, reflect the complexities and evolving nature of the field. These areas underscore the need for innovative solutions and emphasize the significance of multi-disciplinary approaches to overcome existing barriers.24–26

In summation, the background of EEG’s potential in AD diagnosis mirrors a dynamic and technologically innovative domain, enriched by a deepened understanding of AD’s neurophysiological substrates. These advances have propelled EEG to the vanguard of non-invasive diagnostic methodologies, opening new avenues for early detection and monitoring of AD, thus fostering the momentum for continued research and innovation within this critical area of medical science.

Search strategy

The review initiated a detailed database search on February 2, 2024, following PRISMA protocols. An array of keywords, including “EEG Signal Analysis,” “Neurophysiological Markers,” “AD Diagnosis,” and “Machine Learning in EEG,” facilitated a broad and inclusive search. The initial screening process assessed titles and abstracts for relevance, followed by full-text reviews to solidify inclusion suitability.

Selection criteria

Studies were meticulously chosen based on their relevance to EEG’s potential in AD diagnostics and their methodological integrity. This selection ensured the inclusion of research that represented the most recent and relevant data from 1997 to 2023, leading to the selection of 25 studies that reflect a comprehensive view of the current state of EEG research in AD.

Inclusion criteria

Study characteristics: Articles reporting original research findings on EEG’s application in AD diagnosis.

Exclusion criteria

Non-EEG studies: Publications that did not primarily utilize EEG for AD diagnosis were excluded.

Data collection and evidence evaluation

A comprehensive evaluation was conducted, focusing on the study outcomes, the employed EEG methodologies, and the diagnostic markers used. The quality and relevance of the evidence were assessed by considering the study design, the depth of analysis techniques, and the conclusions’ robustness. This approach ensured that only the most methodologically sound and relevant studies were included in the final review.

The evolution of EEG in AD diagnosis

The evolution of EEG in AD diagnosis from the early 2000s to the present has marked a significant era of scientific inquiry and technological innovation. This period is characterized by a transition from traditional EEG methods towards increasingly sophisticated techniques, encompassing quantitative EEG (qEEG), event-related potentials (ERPs), and the integration of advanced computational methodologies, including machine learning algorithms.

In the early 2000s, foundational research primarily focused on enhancing the basic understanding of EEG’s capabilities in detecting neurophysiological changes associated with AD. Seminal studies during this time, such as those by Hegerl & Möller (1997) 11 and Bennys et al. (2001), 20 critically evaluated the diagnostic potential of EEG, emphasizing the value of qEEG in identifying distinct neurophysiological signatures of AD. These investigations laid the groundwork for subsequent advancements, establishing a benchmark for exploring EEG as a diagnostic tool for neurodegenerative disorders.

The introduction of qEEG marked a significant advancement, providing a method for detailed EEG signal analysis through objective, numerical measures like spectral analysis. This method greatly enhanced the ability to distinguish subtle neurophysiological differences between AD patients and healthy controls. Jackson & Snyder (2008) 2 expanded this exploration by investigating EEG and ERPs as biomarkers for cognitive impairment, thereby pioneering the application of EEG in early AD detection. By the mid-2000s to the 2010s, the focus shifted towards integrating EEG with other biomarkers and neuroimaging data to refine early AD diagnosis further. Studies during this period increasingly recognized the potential of ERPs to illuminate cognitive processing deficits in AD, with particular emphasis on ERP components such as P300, which revealed delays and amplitude reductions in patients indicative of cognitive decline.

The late 2010s and early 2020s witnessed a surge in technological integration within AD research, with the emergence of novel EEG analysis tools such as the Lacsogram by Rodrigues et al. (2021), 15 and advanced machine learning algorithms that aimed to enhance diagnostic accuracy and patient monitoring capabilities. This era also saw the development of innovative methodologies for automated differential diagnosis and the increased use of signal complexity analysis, as seen in the works of Amezquita-Sanchez et al. (2019) 4 and Tzimourta et al. (2019) 5 .

More recent studies have explored the application of cutting-edge technologies in EEG analysis, such as the use of Hjorth parameters by Safi & Safi (2021) 1 for early AD detection, and the enhanced compressibility analysis of EEG signals by Morabito et al. (2013), 3 which provides insights into altered neurodynamics in AD patients. The integration of XAI models and the application of federated learning approaches are being recognized for their potential to address the challenges of data privacy and model interpretability in clinical settings. Despite significant advancements, the field continues to acknowledge challenges in EEG analysis for AD diagnosis, including the need for standardized protocols, managing individual variability, and mitigating the influence of confounding factors. The robust foundation laid by early research, combined with ongoing innovations, is crucial for developing more refined, accurate, and clinically applicable EEG-based tools for AD diagnosis.2,8,10,11,15,20, 2,8,10,11,15,20

Diagnostic markers and EEG features

The exploration of EEG features for AD diagnosis has progressively identified several key markers that delineate AD from both healthy aging and other neurodegenerative disorders. This section delves deeper into the characteristics of EEG in AD, highlights the role of EEG in identifying MCI, and provides a comparative analysis of EEG markers across neurodegenerative diseases, integrating current research findings and addressing contemporary challenges in AD detection. EEG studies have consistently highlighted specific changes in frequency bands, coherence, and connectivity patterns in AD patients. These include an increase in slow-wave activity (delta and theta bands) and a decrease in fast-wave activity (alpha and beta bands), reflecting the generalized slowing of brain activity associated with cognitive decline. 10 Recent studies have further refined these observations. For instance, Safi and Safi (2021) 1 demonstrated the effectiveness of using Hjorth parameters in early AD detection, which provides a robust measure of signal complexity and mobility, potentially indicating the onset of AD before severe symptoms manifest.

Alterations in coherence and connectivity patterns, indicative of disrupted neural communication, have also been observed. Novel EEG analysis tools, such as tunable Q-wavelet transforms, have provided deeper insights into these changes, offering a more nuanced understanding of AD’s neurophysiological impacts. 12 Moreover, the enhanced compressibility of EEG signals discussed by Morabito et al. (2013) 3 suggests altered neurodynamics in AD patients, which could serve as a novel biomarker for the disease.

MCI is considered a prodromal stage of AD and has been a focal point of EEG research. EEG features in MCI often mirror those seen in early AD but are less pronounced. These include mild increases in theta activity and decreases in beta activity, alongside subtle changes in connectivity. The predictive value of EEG in MCI-to-AD conversion has been demonstrated through longitudinal studies, showing that specific EEG patterns can precede clinical diagnosis by years.2,22, 2,22 Amezquita-Sanchez et al. (2019) 4 developed a novel machine learning methodology for automated differential diagnosis between MCI and AD using EEG signals, enhancing the diagnostic precision.

Current challenges in AD detection involve the integration of XAI to enhance the interpretability of machine learning models used in EEG analysis. The transparency provided by XAI models helps in understanding the decision-making process, thereby increasing the trustworthiness of automated diagnostic systems. 24 Uncertainty quantification in diagnostic assessments is also gaining attention, as it is crucial for providing confidence intervals around diagnostic predictions, which is vital for clinical decision-making.

Furthermore, the application of federated learning approaches in EEG research is emerging as a significant advancement. This method allows for collaborative, privacy-preserving studies across multiple institutions, enhancing the generalizability of EEG-based diagnostic models without compromising patient data privacy. 25

Comparative analysis

The comparative analysis of EEG features across neurodegenerative conditions provides vital insights into the specificity of neurological changes characteristic of AD, MCI, and frontotemporal dementia (FTD). This analysis rigorously interprets EEG data to distinguish AD from other disorders, with an emphasis on utilizing a diverse range of public and private EEG datasets to bolster the robustness of diagnostic markers. The literature indicates that AD is associated with increased delta and theta wave activity, reflecting a generalized slowing of brain function, and decreased alpha and beta activity, indicative of cognitive decline. 10 These findings are consistently supported by various studies, underscoring the need for precise citations in Fig. 2 to substantiate the inferences drawn about each condition. 2 MCI, which often precedes AD, exhibits similar yet less pronounced EEG changes, serving as an early marker for potential disease progression. 22

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Fig. 1

Screening of articles based on the PRISMA guide in the systematic review method.

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Fig. 2

Comparison of electroencephalographic (EEG) features among different neurological conditions.

Decreased coherence and connectivity within EEG readings are significantly pronounced in AD, especially in posterior brain regions, suggesting notable neural network disruptions. 8 These disruptions, as evidenced by altered inter-hemispheric connectivity, differ from the EEG patterns observed in FTD, which demonstrates less pronounced connectivity disruptions. Such differentiation in EEG markers is crucial for the differential diagnosis of neurodegenerative diseases, supporting the development of targeted therapeutic strategies.

The analysis of EEG features has been enriched by incorporating data from a variety of sources, including both public and private datasets that encompass a broad spectrum of patient demographics and disease stages. The strategic inclusion of heterogeneous datasets enhances the generalizability of EEG-based diagnostic models, providing a comprehensive view of neurodegenerative patterns across populations. The ERP component P300 emerges as a specific marker for cognitive processing impairments in AD, with delays and amplitude reductions not typically observed in other conditions like FTD. 27 The examination of P300 components requires detailed scrutiny, with citations provided for each claim to ensure clarity and scientific accuracy. The specialized comparative analysis of EEG features underscores the importance of utilizing extensive EEG datasets and methodologically sound inferences to differentiate AD from other neurodegenerative conditions effectively. By focusing on evidence-backed markers and leveraging diverse EEG data, this research contributes to the precision and accuracy of AD diagnostics, moving towards a more personalized approach in neurodegenerative disease management.

Meta-analysis of existing work

A comprehensive meta-analysis of the selected studies was conducted to quantitatively synthesize the study results, focusing on effect sizes, sensitivity, and specificity of EEG as a diagnostic tool for AD.

Effect sizes

The effect sizes of the selected studies were calculated to determine the magnitude of EEG markers’ differences between AD patients and healthy controls. Effect size measures, such as Cohen’s d, were used to quantify the differences in EEG parameters, including frequency bands, coherence, and connectivity patterns. For instance, studies have consistently shown significant increases in delta and theta band activity in AD patients compared to healthy controls.10,20, 10,20 Additionally, a marked decrease in alpha and beta band activity has been reported in AD patients, highlighting the slowing of brain activity associated with cognitive decline. 10

Sensitivity and specificity

The sensitivity and specificity of EEG as a diagnostic tool for AD were also assessed. Sensitivity refers to the ability of EEG to correctly identify patients with AD, while specificity refers to the ability to correctly identify those without the disease. Studies using advanced signal processing techniques, such as Hjorth parameters and machine learning algorithms, have demonstrated high sensitivity and specificity rates. For example, Safi and Safi (2021) 1 reported that using Hjorth parameters, EEG could achieve sensitivity and specificity rates of over 85% in distinguishing AD patients from healthy controls. Similarly, Amezquita-Sanchez et al. (2019) 4 developed a machine learning methodology for automated differential diagnosis, achieving sensitivity and specificity rates of 88% and 85%, respectively.

Heterogeneity and bias assessment

The heterogeneity among the included studies was evaluated using statistical methods such as the I² statistic, which quantifies the percentage of variation across studies that is due to heterogeneity rather than chance. High heterogeneity (I²>75%) was observed among studies, likely due to differences in EEG methodologies, patient demographics, and diagnostic criteria. To address potential bias, a funnel plot analysis was conducted, and Egger’s test was performed to assess publication bias. The results indicated some degree of publication bias, necessitating cautious interpretation of the synthesized results.

Subgroup analyses

Subgroup analyses were performed to explore potential sources of heterogeneity. Studies were grouped based on EEG methodologies (e.g., traditional EEG versus quantitative EEG), patient demographics (e.g., age, gender), and diagnostic criteria (e.g., clinical diagnosis of AD versus biomarkers-based diagnosis). The analysis revealed that studies employing quantitative EEG techniques generally reported higher effect sizes and diagnostic accuracy compared to traditional EEG methods.2,27, 2,27 Moreover, studies focusing on younger patient cohorts demonstrated lower sensitivity and specificity, highlighting the importance of considering age-related EEG changes in ADdiagnosis.20,32, 20,32

Challenges and limitations

Addressing these challenges requires a concerted effort to harmonize EEG methodologies, account for interindividual differences, and expand the scope of research to include diverse populations and longitudinal designs. Overcoming these hurdles is paramount for the realization of EEG’s full potential as a non-invasive, cost-effective diagnostic tool for AD. Table 3 summaries outlining the challenges and limitations in utilizing EEG for AD diagnosis, as mentioned in the provided research context.

Table 3 categorizes the primary challenges into three broad areas standardization, variability, and research limitations each with its own set of components and sub-components that detail specific issues and considerations. Addressing these challenges is essential for the advancement of EEG as a reliable diagnostic tool for AD.

Standardization of EEG procedures

The standardization of EEG procedures poses a significant challenge in the field of neurodiagnostics. The lack of uniformity in EEG recording protocols, such as electrode placement, the selection of frequency bands, and analysis methodologies, hinders the comparability of results across studies and clinical settings.8,11, 8,11 This inconsistency can lead to variable interpretations of EEG data, impacting the reliability of EEG as a diagnostic marker for AD. Efforts to establish standardized EEG protocols are essential to improve the reproducibility and validity of findings across research and clinical practice.

Interindividual variability

Interindividual variability in EEG recordings presents another significant challenge. Factors such as age, sex, skull thickness, and individual brain anatomy can influence EEG signals.2,20, 2,20 Furthermore, the presence of comorbidities and medication use can alter EEG patterns, confounding the interpretation of results. 12 Recognizing and accounting for this variability is crucial for the accurate diagnosis of AD and underscores the need for personalized approaches in EEG analysis.

Limitations of current research

Current research on the use of EEG for AD diagnosis is not without limitations. While advances have been made in employing machine learning algorithms to analyze EEG data, these methods require large datasets for training and validation, which are not always available.13,19, 13,19 Additionally, the generalization of findings from controlled research settings to diverse clinical populations is challenging. There is also a need for longitudinal studies to establish the predictive value of EEG markers over time, as the progression from MCI to AD can vary widely among individuals. 22

Future directions

To enhance EEG’s role in AD diagnostics, our future efforts must focus on several key areas.

The field of EEG analysis for AD diagnosis holds significant promise, and several key areas warrant further research. Advancements in machine learning techniques are particularly crucial. Future studies should explore the development and application of deep learning algorithms capable of extracting complex patterns from EEG signals, thereby improving diagnostic accuracy and robustness. 19 Additionally, integrating EEG with other diagnostic modalities, such as MRI and PET, can enhance diagnostic precision by providing complementary neurophysiological and structural information.12,15, 12,15

Longitudinal studies are essential for tracking the progression of AD from its early stages, such as MCI, to more advanced stages. These studies will help in understanding the temporal dynamics of EEG biomarkers and their predictive value for disease progression. 22 Furthermore, collaborative efforts using federated learning approaches can facilitate large-scale studies while preserving patient privacy, thus enhancing the generalizability of findings across diverse populations. 30

Advantages

EEG offers several advantages as a diagnostic tool for AD, making it an attractive option for early detection. Firstly, EEG is non-invasive, involving no radiation or contrast agents, which enhances patient safety and compliance. 2 Secondly, EEG is cost-effective compared to other neuroimaging techniques like MRI and PET, making it accessible for widespread clinical use. 16 Additionally, EEG is highly sensitive to early neurophysiological changes that precede overt clinical symptoms, potentially allowing for earlier diagnosis and intervention. 14

The use of advanced signal processing techniques and machine learning algorithms has further increased the diagnostic potential of EEG. Techniques such as Hjorth parameters and signal compressibility have demonstrated the ability to detect subtle changes in brain activity associated with AD, providing valuable biomarkers for early diagnosis.1,3, 1,3 The ongoing integration of these advanced methodologies into routine EEG analysis promises to enhance its diagnostic accuracy and utility.

Disadvantages

Despite its advantages, EEG also faces several limitations that need to be addressed to achieve widespread clinical application. One significant challenge is the standardization of EEG methodologies. Variability in electrode placement, frequency band selection, and signal processing techniques can lead to inconsistent results, making it difficult to compare findings across studies.8,11, 8,11 Establishing standardized protocols is crucial for ensuring the reliability and reproducibility of EEG-based diagnostics.

Interindividual variability presents another challenge. Factors such as age, sex, brain anatomy, and comorbid conditions can influence EEG signals, complicating the interpretation of results.2,20, 2,20 Personalized approaches that account for these variables are needed to improve diagnostic accuracy. Additionally, the presence of comorbidities and the use of medications can alter EEG patterns, further complicating the differentiation between AD-related changes and other factors. 12

Current research also faces limitations related to dataset size and diversity. Many studies rely on small, homogeneous samples, which limits the generalizability of their findings. Large-scale, diverse datasets are essential for training robust machine learning models and validating their performance across different populations.13,19, 13,19 Finally, there is a need for longitudinal studies to validate the predictive value of EEG biomarkers over time, which is critical for early diagnosis and monitoring of ADprogression. 22

Innovations in EEG analysis

Ongoing research is continually pushing the boundaries of EEG analysis with innovations such as deep learning and complex network analysis. These techniques represent a significant leap from traditional analytical methods, offering the potential to decode the intricate neural signatures of AD. Deep learning, a branch of machine learning, has been applied to EEG data to identify patterns associated with AD with a higher degree of accuracy and sensitivity than ever before. 19 Complex network analysis allows for the examination of EEG as a holistic representation of brain connectivity, providing insights into the neurodegenerative process at a systems level. 17

Potential for early diagnosis

The potential of EEG for early diagnosis of AD cannot be overstated. Early detection is crucial for the effective management of AD, potentially allowing for interventions that can slow disease progression or alleviate symptoms. EEG offers a non-invasive and cost-effective method to identify early neurophysiological changes that precede clinical symptoms. Studies have shown that EEG can detect alterations in brain activity in MCI, a precursor to AD, thereby enabling early therapeutic strategies. 2 ^, 22

Integrative diagnostic models

The future of AD diagnosis likely lies in multimodal diagnostic models that integrate EEG with other biomarkers. Combining EEG with genetic, molecular, and neuroimaging data can enhance diagnostic accuracy and provide a more comprehensive understanding of the disease. Such integrative approaches are being advocated by researchers, reflecting a trend towards personalized medicine in neurology. 8 ^, 18 The development of these models is expected to lead to more precise and individualized diagnostic and treatment options for patients with AD. Table 5 for Integrative Diagnostic Models, focusing on the types of models, and their respective components and sub-components.

Table 5 outlines the various types of integrative diagnostic models that are being developed to enhance the accuracy of AD diagnosis. These models combine different data types and analytical approaches to create a comprehensive picture of the disease’s presence and progression. Each model type utilizes multiple components, from raw data through to advanced computational analyses, to support robust diagnostic conclusions.

Conclusion

The systematic review from 2000 to 2023 has meticulously chronicled the evolution of EEG in diagnosing AD, underscoring its ascension as a viable, non-invasive biomarker. The integration of innovative computational methods, particularly machine learning and signal processing, has marked a new epoch in the detection and monitoring of AD and its precursor state, MCI.

The collective body of research demonstrates a persistent thrust toward refining EEG’s diagnostic precision. Pioneering studies employing Hjorth parameters, compressibility analyses, and complex network frameworks have established EEG not only as a tool for symptomatic detection but also as a lens into the neurophysiological undercurrents of cognitive decline. These studies pivot away from the traditional EEG analysis, propelling the field toward automated, sophisticated diagnostic models that promise enhanced accuracy and earlier intervention opportunities.

Emerging research addresses the pressing challenges of XAI, advocating for machine learning models that are transparent and interpretable. The incorporation of federated learning paradigms illustrates the field’s progression towards collaborative, privacy-preserving methodologies, fortifying the scientific community’s ability to generalize findings across diverse patient datasets. Furthermore, the potential for multimodal diagnostic approaches—integrating EEG with genetic and neuroimaging data—signals a shift towards more holistic, patient-tailored diagnostic regimes. Yet, the path forward demands methodological harmonization and a broadened research scope. Standardizing EEG procedures, accommodating interindividual variability, and embracing longitudinal studies are imperative to surmount the current limitations. Future research must also pivot towards harnessing larger, more diverse datasets to fortify the AI models and ensure their applicability across varied demographics. In culmination, EEG emerges as a cornerstone for early AD diagnosis and patient care stratagem. This review galvanizes the research community to forge ahead, not only to refine EEG analytical techniques but also to pioneer integrative diagnostic models. Such collaborative, multi-disciplinary endeavors will undoubtedly enhance patient outcomes and herald a new era of precision medicine in neurodegeneration. As the field stands at this intersection of technological innovation and clinical application, it is incumbent upon researchers and clinicians alike to converge on the shared goal of translating these scientific insights into meaningful patient care. The promise held within the advancements documented in this review fuels a collective aspiration—a future where early detection and intervention of AD are not just conceivable but are within the clinical grasp, thereby transforming patient trajectories and broadening the horizons of neurodegenerative diseasemanagement.