Abstract

Alzheimer’s Disease (AD) is a prevalent neurodegenerative disorder characterized by protein accumulation, cognitive decline, and irreversible brain damage. With the aging population, AD’s prevalence is rising, necessitating effective diagnostic tools. Electroencephalography (EEG), a non-invasive neuroimaging method, has emerged as a promising tool for early AD detection.

This review summarizes 62 studies published between 2000 and 2023, investigating EEG abnormalities as diagnostic markers for AD. The studies consistently highlight EEG’s role in distinguishing AD and Mild Cognitive Impairment (MCI) from healthy controls. EEG features, patterns, and connectivity measures, often analyzed with machine learning techniques, exhibit promise for discriminating cognitive disorders. While promising, translating EEG diagnostics into clinical practice faces challenges related to individual variability and the progressive nature of AD.

Standardized protocols and diverse datasets are crucial for enhancing the applicability of EEG-based AD diagnosis. Integrating EEG with imaging and genetic markers holds potential for improved diagnostic accuracy and understanding AD pathology. The evolving neuroimaging landscape offers avenues for insights into AD’s origins. Future research should explore the complex relationships between EEG-derived biomarkers, cognitive profiles, and disease processes to advance early interventions.

In conclusion, EEG shows promise for AD detection and diagnosis. While progress has been made, further development is needed for reliable real-world diagnostics. EEG-based approaches offer potential for accurate early AD detection and insights into cognitive decline mechanisms.

Keywords: Alzheimer’s disease; Electroencephalography; Diagnostic markers; Biomarkers.

Citation: Ehteshamzad S. Abnormalities in EEG as alzheimer marker. J Clin Images Med Case Rep. 2023; 4(10): 2633.

Background

Alzheimer’s Disease (AD) is a neurodegenerative disorder that progresses slowly and is the most common type of neurological disorder and dementia in the elderly [1,2]. The disease is characterized by the accumulation of extracellular protein, including the pathological proteins amyloid beta and phosphorylated tau, which leads to plaque deposition and intracellular fibrillary nerve coils in the brain, causing the loss of neurons and synaptic degeneration. This, in turn, leads to brain atrophy and severe cognitive impairment in humans and animals [3,4]. Alzheimer’s is progressive and irreversible condition that causes cognitive and behavioral dysfunction and is often associated with various brain disorders such as aphasia, agnosia, amnesia and apraxia [5]. Globally, about 50 million people suffer from this type of dementia, and it is predicted that this number will multiply by 2050. With the aging of the world’s population, more than 107 million people will be diagnosed with AD (by 2050 [6,7]. To date, no effective treatment has been found for AD [8]. The available treatments can only reduce or delay its symptoms [9]. However, twelve monoclonal antibodies against beta-amyloid have been developed and are currently undergoing clinical trials for the treatment of the disease [10].

EEG is a neuroimaging technique that is completely non-invasive [11]. It records electrical signals generated by the activity of brain neurons during a period of time [12]. EEG allows us to examine changes in brain activity [13]. The state of the individual’s brain may process certain frequencies dominantly [14]. Brain waves include 4 main groups: - beta (>13 Hz), - alpha (best-known human brain rhythm) (8-13 Hz), - theta (4-8 Hz), - delta (0.5-4 Hz). EEG is commonly used to diagnose various brain diseases, including epilepsy, Parkinson’s disease, Alzheimer’s disease, and sleep disorders [15].

Moreover, EEG can be used to assess the effectiveness of treatments made by ADinhibitors [16,17]. The combination of Transcranial Magnetic Stimulation (TMS) and EEG can provide insights into the related features of Alzheimer’s pathophysiology [18]. Overall, it has been determined that the use of EEG is useful and effective for diagnosing and monitoring the progress of Alzheimer’s disease, as well as evaluating the efficacy of treatments [19].

There are several studies related to the examination of abnormalities in the electroencephalogram during the study of Alzheimer’s disease. For instance, some studies show that EEG can detect frequency changes in people with Alzheimer’s disease, which include a decrease in alpha and beta waves and an increase in delta and theta waves [20]. It has also been found that abnormalities in brain frequencies even If they are in the early stages, can be determined by EEG, which shows that EEG can be an efficient tool for early diagnosis of Alzheimer’s disease.

Despite these promising findings, there are still gaps and inconsistencies in the existing literature. For instance, there is a need for standardized EEG protocols and the development of more accurate EEG biomarkers for Alzheimer’s disease. The current review aims to provide a comprehensive overview of existing literature on EEG abnormalities in Alzheimer’s disease, identify gaps and inconsistencies, and propose future directions for research. By doing so, this review will contribute to the existing body of knowledge on EEG abnormalities in Alzheimer’s disease, and potentially facilitate the development of more accurate diagnosis tool for early detection and diagnosis.

Materials and methods

A comprehensive literature search was conducted to identify relevant articles focusing on abnormalities in EEG as potential markers for Alzheimer’s disease. The search covered the period from 2000 to 2023 and encompassed the PubMed, IEEE Xplore, Science Direct, and Google Scholar databases. The search utilized the keywords “Alzheimer,” “EEG,” and “Abnormalities” to ensure a comprehensive retrieval of relevant studies.

The inclusion criteria comprised English-language articles published between 2000 and 2023 that investigated abnormalities in EEG as potential markers for Alzheimer’s disease. Studies that explored the diagnostic potential of EEG abnormalities or alterations in EEG patterns in Alzheimer’s patients were considered. Conference papers, reviews, and non-English articles were excluded.

The initial search yielded a total of approximately 150 articles. After removing duplicates, the titles and abstracts of the remaining articles were screened for relevance. This led to the identification of approximately 78 potentially relevant articles. Full-text examination was then performed to further refine the selection. Ultimately, 62 English articles met the inclusion criteria and were included in this review article. For each included article, the abstracts were carefully read and analyzed. The main focus was to identify studies that specifically investigated EEG abnormalities as potential markers for Alzheimer’s disease. The articles were assessed for their objectives, methodologies, findings, and implications related to the diagnostic utility of EEG abnormalities in Alzheimer’s disease.

The abstracts of the selected articles were examined to determine whether there were agreements or disagreements between the studies regarding the potential of EEG abnormalities as diagnostic markers for Alzheimer’s disease. Emphasis was placed on identifying consistent findings and divergent results that could provide a comprehensive overview of the current state of research in this field.

The findings from the selected articles were synthesized to provide a comprehensive overview of the studies that have explored EEG abnormalities as potential markers for Alzheimer’s disease. The review article is structured to present the similarities and contrasts across the studies, the methodologies employed, key findings, and the implications for the diagnostic utility of EEG abnormalities in Alzheimer’s disease.

The comprehensive search and selection process ensured the inclusion of relevant studies that investigate EEG abnormalities as potential markers for Alzheimer’s disease. By analyzing the selected articles’ abstracts, this review article aims to provide insights into the consensus and discrepancies among studies and to contribute to our understanding of the diagnostic potential of EEG abnormalities in the context of Alzheimer’s disease.

EEG and alzheimer’s disease: Insights over time

In 1931, Hans Berger initiated Alzheimer’s-EEG studies, observing slowed rhythms and coherence reduction in EEGs of patients. Subsequent research in 1989 and 2001 linked EEG abnormalities to disease severity. EEG usage in Alzheimer’s investigation is motivated by its sensitivity to cortical dysfunction, reflecting functional and anatomical brain defects. EEG coherence offers insights into synaptic plasticity’s connection to cognitive function. Non-linear EEG analysis reveals disease-related complexity reduction and functional impairments, providing unique disease progression insights [21].

In 2000, studies focused on EEG frequency band generators to describe Alzheimer’s-related changes and predict the disease, identifying alterations in power across delta, theta, alpha, and beta bands [22]. The role of the cholinergic system in EEG was explored, linking EEG changes to Alzheimer’s progression and symptoms [23]. Cognitive decline in Alzheimer’s was associated with EEG slowing and defects in cholinergic and monoaminergic systems [24].

In 2001, spectral indices were examined for Alzheimer’s diagnosis, showing accurate results except for frontal regions affected by eye movement [25]. Cross-Mutual Information (CMI) analysis revealed lower information transmission in Alzheimer’s patients, particularly between distant electrodes and across the right hemisphere [26]. Nonlinear dynamic analysis highlighted decreased complexity in EEG patterns of Alzheimer’s patients [27].

In 2003, synchronization probability analysis demonstrated reduced beta band synchrony in mild Alzheimer’s patients, linked to cognitive impairment [28]. EEG studies in 2004 found alpha wave analysis effective in distinguishing mild Alzheimer’s from vascular dementia and healthy elderly individuals [29]. EEG abnormalities in Alzheimer’s were associated with lower frequencies, decreased coherence, and disrupted functional communication in cortical areas [30].

Between 2005 and 2009, research advancements in Alzheimer’s and EEG provided valuable insights. Studies in 2005 explored EEG irregularities in Alzheimer’s, revealing reduced disorganization [31]. Additionally, reduced synchronization in alpha and high beta bands was observed [32]. Artifact removal using ICA improved Alzheimer’s pattern detection [33], while EEG preprocessing using blind source separation enhanced diagnosis [34]. Coherence disruptions in alpha bands indicated potential brain connection issues [35]. Age-related changes in EEG rhythms were discovered in 2006, hinting at dementia prediction [36]. Non-linear analysis unveiled lower complexity in Alzheimer’s brain signals [37]. In 2007, higher theta power and lower alpha response were found in AD patients [38], and algorithms identified AD through EEG [39]. Some EEG rhythms were linked to memory and attention tasks [40]. The ApoE gene was linked to altered brain activity [41], while GBP indicated cognitive dysfunction [42]. Alzheimer’s EEG changes in various frequency ranges were explored in 2008 [43], and qEEG and ERP became biomarkers for early detection [44]. Brain wave analysis revealed potential Alzheimer’s signs [45], and EEG complexity alterations across frequencies were detected [46]. In 2009, EEG incoherence was explored as a diagnostic indicator for early-stage Alzheimer’s [47]. Lastly, communication dynamics between brain regions provided insights into memory disturbances [48].

In 2010, an article highlighted Alzheimer’s effects on EEG: Slowing, complexity reduction, and synchrony disorders. Specific EEG markers were identified, including alterations in frequency bands, connectivity, and synchronization. The article explored EEG’s diagnostic utility, addressing methods, analysis types, and limitations due to signal variability and standardization challenges. Advantages of EEG for Alzheimer’s diagnosis were discussed, including non-invasiveness and potential early detection. Future directions envisioned machine learning integration [49].

A 2010 study investigated synchronization measurement methods for AD diagnosis, suggesting combined methods could enhance accuracy [50]. In 2011, nonlinear complexity measures were applied to EEG signals, showing improved AD detection accuracy when combined [51]. EEG and ERPs were considered for low-cost, non-invasive assessment in high-risk groups [52].

In 2012, a study correlated the alpha3/alpha2 power ratio in EEG markers with hippocampal atrophy in MCI and Alzheimer’s patients, suggesting diagnostic potential [53]. Studies in 2014 recognized the challenge of understanding EEG brain signals and proposed computer-assisted analysis to enhance brain issue identification for effective Alzheimer’s diagnosis [54]. A 2014 study at the Institute of Information Technology in Thessaloniki focused on Alzheimer’s research using EEG and recent studies [55].

In 2015, studies focused on EEG’s potential for early Alzheimer’s diagnosis, revealing intricate signals in AD individuals. Techniques measuring neural synchrony (phase, coherence, cross-correlation) were tested on databases, particularly in temporal brain regions affected early in AD. A study employed deep learning networks on EEG data, improving accuracy [56,57]. In 2016, a new EEG biomarker (ΔEEG A) was proposed for early AD detection, outperforming existing methods (58). Abnormal neural coordination and connectivity were observed in AD and MCI resting-state EEG [59]. Delta and theta band synchronization disturbances were identified as potential AD biomarkers [60]. In 2017, algorithms and machine learning enhanced EEG biomarker classification accuracy, highlighting its potential in clinical trials [61].

In 2018, EEG’s relationship to cognitive disorders and dementia diagnosis was explored, suggesting its usefulness but calling for larger studies [62]. Clinical EEG data was used to differentiate cognitive conditions, achieving high classification accuracy [63]. qEEG power alterations were observed in AD and MCI, correlating with neuropsychological measures [64]. TMS-EEG study linked frontal brain connections to cognitive decline [65]. A multi-task learning approach with EEG images improved AD detection accuracy [66].

From 2020 to 2023, research milestones in AD diagnosis emerged. In 2020, an article underscored AD’s increasing prevalence and suggested EEG’s cost-effective role in monitoring cognitive decline [67]. Electrophysiology’s relevance in understanding AD’s impact on brain activity was highlighted [68]. In 2021, EEG gained prominence for early AD detection and identifying biomarkers [69,70]. “Lacsogram” introduced EEG-based AD diagnosis [71]. EEG studies focused on different stages and disease connections, often combined with MRI. Resting/task-oriented EEG and machine learning aided early detection, revealing cognitive impairment indicators [72,73]. Combining Hjorth parameters and features improved AD diagnosis [74]. In 2022, finite response filters and machine learning improved EEG-based AD diagnosis [75,76]. ERP and deep learning models were explored [77]. In 2023, Deep learning’s impact on AD diagnosis was evident [78]. Early AD diagnosis’s significance was emphasized [79]. Intelligent models like Adazd-Net demonstrated high AD diagnosis accuracy [80-82]. EEG-based models distinguished between AD, mild cognitive impairment, and healthy aging [81]. EEG in clinical trials and AD monitoring platforms highlighted its potential [75,80].

Discussion

While the primary diagnosis of cognitive disorders relies on clinical assessment, EEG plays a crucial role in evaluating, classifying, and monitoring specific conditions [82-92]. EEG’s potential has been demonstrated in appraising neurological disorders like AD, autism, Parkinson’s Disease (PD), and ADHD [83]. Changes in Quantitative EEG (QEEG) have been observed in PD and ADHD, with QEEG variables correlating over time with cognitive assessment tools and predicting PD-related dementia [84]. Notably, distinct EEG spectrum and Event-Related Potential (ERP) changes emerge in motor abnormalities and cognitive decline, particularly in PD, while connectivity disruptions manifest in schizophrenia spectrum disorders [85,86]. Despite EEG’s reliability and noninvasiveness, its clinical use requires advancements in analytical techniques and technologies [87]. Recent validation of the theta/beta ratio as an ADHD diagnostic marker highlights EEG’s role, albeit with specificity challenges [88]. In Autism Spectrum Disorders (ASD), EEG research uncovers power anomalies, functional connectivity disruptions, and the impacts of GABAergic transmission [89,90]. Furthermore, dementia reveals individual-level biomarkers in EEG, such as decreased dominant frequency and increased θ power, signifying cognitive decline [91]. Although EEG holds promise for understanding brain function and clinical applications, further research is vital to establish its diagnostic utility in conditions like dementia and mild cognitive impairment [92-94]. Alzheimer’s disease is a global healthcare challenge characterized by cognitive decline and neurodegeneration. The introduction of the literature underscores the urgency and importance of finding effective diagnostic tools and therapeutic strategies for this condition [21]. EEG, as a non-invasive and cost-effective neuroimaging technique, has gained prominence in the field of AD research. The literature synthesizes the findings from 64 reviewed studies, collectively reinforcing its role in understanding the pathophysiology and diagnosis of AD [21]. One key finding highlighted in the literature is the presence of EEG abnormalities as potential AD biomarkers. These abnormalities encompass rhythmic slowing, reduced interregional coherence, increased theta/delta, and decreased alpha/beta activities [21]. These EEG changes are shown to correlate with the severity of AD and offer diagnostic potential [23]. Additionally, altered EEG generators, particularly global field power in various frequency bands, have been identified as discriminators between AD patients and control subjects [22]. This emphasizes the potential of EEG in aiding the diagnosis of AD. Longitudinal studies mentioned in the literature suggest that EEG has predictive power for tracking AD progression, especially from the stage of Mild Cognitive Impairment (MCI) [22]. This indicates the potential utility of EEG as a tool for early diagnosis and monitoring of the disease. The literature also highlights the connection between EEG abnormalities and the cholinergic system, offering insights into both diagnostic and therapeutic avenues [23]. Cognitive symptoms in AD are strongly associated with EEG slowness and cholinergic deficits [24], suggesting that targeting these aspects could be beneficial in managing AD. Furthermore, the involvement of the monoaminergic system suggests potential treatment options alongside cholinergic therapies [24], indicating a multifaceted approach to tackling AD. EEG spectral indices, such as fast/slow power ratios, are shown to have high diagnostic accuracy [25]. This indicates the potential for EEG to serve as a reliable diagnostic tool in the clinical setting. Cross-information analysis reveals impaired cortical connectivity in AD [26], emphasizing the importance of understanding the neural network disruptions associated with the disease. Non-linear EEG analysis also identifies complexity reductions in AD patterns, advocating for nonlinear approaches [27], which could provide novel insights into the disease’s mechanisms. EEG synchronization measures show promise in both diagnosis and assessing disease progression [28], further highlighting the versatility of EEG as a diagnostic tool. Individual EEG frequencies, such as decreased alpha and increased delta/theta, are identified as differentiators between different types of dementia [29], underlining the specificity of EEG patterns in AD. The literature also underscores the diagnostic and neuropathological value of EEG, particularly in cortical involvement and synaptic dysfunction [30]. This suggests that EEG could be a valuable tool for understanding the pathological changes in AD. Moving forward, the literature discusses several advanced techniques and approaches in EEG research. Approximate entropy (ApEn) analysis is mentioned as a potential tool for understanding brain dysfunction in AD patients [31]. However, cautious optimism is warranted due to sample size limitations. EEG synchronization dynamics are explored, revealing lower synchronization levels in specific frequency bands, which shed light on functional connectivity disorders and self-organized criticality in neural networks [32]. Advanced preprocessing techniques like Independent Component Analysis (ICA) are mentioned, which can improve EEG data interpretability and diagnostic accuracy, particularly in early AD stages [33]. Blind Source Separation (BSS)-based preprocessing is also highlighted as a method that enhances EEG classification [34]. Altered functional connectivity patterns, especially reduced coherence in the alpha band, are found to contribute to cognitive decline in AD [35]. Classification algorithms using EEG features are shown to demonstrate high diagnostic accuracy [36], suggesting the potential for machine learning in improving AD diagnosis. The complex relationship between EEG measures and cognitive decline is explored [37], emphasizing the need for a nuanced understanding of the data. Quantifying EEG slowing and analyzing individual frequencies show promise for early AD detection and differentiating dementia types [38,39], underlining the specificity of EEG markers. Genetic risk factors in AD underscore EEG’s diagnostic and research value [40,41], highlighting the potential for personalized medicine in AD management. The literature also discusses the diagnostic potential of gamma band power enhancement [42,43] and quantitative EEG (qEEG) as a reliable biomarker for early AD cognitive impairment [44]. Signal processing and machine learning techniques are presented as essential for enhancing AD diagnosis accuracy, revealing neural synchronization patterns and complexity changes [45-50]. Combining nonlinear signal complexity measures with machine learning is shown to greatly improve diagnostic accuracy [51], promoting a multimodal approach. EEG is positioned as a valuable tool for understanding normal and abnormal brain aging, aiding individual-level assessment [52]. Correlations between EEG markers and hippocampal atrophy are highlighted, underscoring diagnostic and prognostic potential [53]. Computational methods, data mining, and machine learning are found to support automated AD diagnosis [54], offering potential for streamlining the diagnostic process. EEG’s versatility spans differential diagnosis, prognosis, and drug effectiveness monitoring [55]. Advanced signal processing and deep learning networks achieve high accuracy in AD classification [56-58]. Disturbances in functional connectivity are identified as potential neurophysiological biomarkers of AD [60]. The literature presents an EEG-based index utilizing 14 biomarkers, achieving significant classification accuracy and showcasing the potential for improved diagnostic accuracy in clinical trials [61]. Distinct EEG patterns in cortical oscillatory activity and functional connectivity are revealed in different cognitive disorders, such as Frontal Dementia (FTD) and AD [62]. A novel approach involving epoch-based entropy and spike modeling achieves efficient discrimination between various patient groups, enhancing early AD detection [63]. Early changes in qEEG power, particularly relative theta power, are discussed as potential markers for AD-related cognitive decline and differentiation between AD, MCI, and healthy controls [64]. The integration of EEG with other neuroimaging methods, such as transcranial magnetic stimulation, offers a deeper understanding of AD pathophysiology and personalized therapeutic possibilities [65]. Deep learning techniques, including Convolutional Neural Networks (CNN) and Deep Neural Networks (DNN), show promise in AD diagnosis and differentiation from other cognitive impairments [76]. A multi-task learning strategy with a discriminant convolutional high-order Boltzmann machine proves effective for feature extraction and classification accuracy [66]. The literature emphasizes the importance of non-invasive and cost-effective diagnostic tools, like EEG, for early AD detection and tracking cognitive decline [67]. EEG’s integration with MEG biomarkers in clinical trials is advocated to provide insights into neural dynamics, network behavior, and support drug discovery initiatives [68]. EEG is positioned as a practical and accessible biomarker for early AD detection, highlighting its advantages over molecular markers [69]. The economic and social consequences of AD underscore the significance of EEG in early diagnosis [70]. The “Lacsogram” tool is introduced for EEG signal processing to describe AD activity and diagnose disease stages, showcasing promising machine learning-based classification accuracy [71]. Combining Hjorth parameters with various features and analysis methods in early AD detection from EEG signals yielded high classification accuracy [74]. Standardization in EEG recording methods and data analysis is emphasized for monitoring disease progression and interventions, with EEG feature extraction and supervised machine learning achieving high classification accuracy between healthy subjects, MCI, and AD patients [77]. A computer-based system for AD diagnosis using EEG signals from a limited number of electrodes, implementing deep learning techniques, achieves high accuracy [78]. Combining EEG signal processing with gait analysis through a deep learning strategy demonstrates potential for comprehensive AD patient monitoring and diagnosis [79]. Focusing on periodic components in EEG analysis shows promise in distinguishing between healthy subjects, MCI patients, and AD patients [80]. The Adazd-Net framework for automatic AD detection using EEG signals achieves high accuracy and provides explanations for predictions [81]. An interdisciplinary approach combining EEG signals and gait monitoring offers potential benefits for early AD diagnosis and monitoring [82]. The literature provides a comprehensive overview of the role of EEG in understanding and diagnosing Alzheimer’s disease. It highlights the potential of EEG as a valuable biomarker, emphasizing its versatility, diagnostic accuracy, and integration with advanced technologies like machine learning. While there is a wealth of promising findings, the literature also acknowledges the need for further research, larger sample sizes, and standardization to fully unlock the diagnostic and prognostic potential of EEG in the context of AD. EEG research in AD is a dynamic field, offering hope for earlier and more accurate diagnosis, as well as insights into the underlying pathophysiological mechanisms of the disease.

Conclusion

In conclusion, EEG holds promise as a valuable tool for diagnosing and monitoring AD. The reviewed studies demonstrate consistent findings in using EEG features, patterns, and connectivity to distinguish AD and MCI from healthy controls. Machine learning techniques enhance diagnostic accuracy, but challenges in clinical translation remain. Integration of EEG with other biomarkers and advancements in technology offer potential for more accurate diagnosis and understanding of AD. However, further research is needed to address individual variability, comorbidities, and the dynamic nature of AD progression. EEG-based approaches show potential for early AD detection and insights into cognitive decline mechanisms.

References

1. Calabrò M, Rinaldi C, Santoro G, Crisafulli C. The biological pathways of Alzheimer disease: A review. AIMS neuroscience. 2021; 8: 86.
2. Li W, Sun L, Yue L, Xiao S. Alzheimer’s disease and COVID-19: Interactions, intrinsic linkages, and the role of immunoinflammatory responses in this process. Front Immunol. 2023 Feb 9; 14: 1120495.
3. Tzioras M, McGeachan RI, Durrant CS, et al. Synaptic degeneration in Alzheimer disease. Nat Rev Neurol. 2023; 19: 19-38.
4. Vogt AS, Jennings GT, Mohsen MO, Vogel M, Bachmann MF. Alzheimer’s Disease: A Brief History of Immunotherapies Targeting Amyloid β. International journal of molecular sciences. 2023; 24: 3895.
5. Li W, Sun L, Yue L, Xiao S. Alzheimer’s disease and COVID-19: Interactions, intrinsic linkages, and the role of immunoinflammatory responses in this process. Frontiers in immunology. 2023; 14: 1120495.
6. Vogt AS, Jennings GT, Mohsen MO, Vogel M, Bachmann MF. Alzheimer’s Disease: A Brief History of Immunotherapies Targeting Amyloid β. International journal of molecular sciences. 2023; 24: 3895.
7. Calabrò M, Rinaldi C, Santoro G, Crisafulli C. The biological pathways of Alzheimer disease: A review. AIMS neuroscience. 2021; 8: 86.
8. Borah K, Bora K, Mallik S, Zhao Z. Potential Therapeutic Agents on Alzheimer’s Disease through Molecular Docking and Molecular Dynamics Simulation Study of Plant-Based Compounds. Chemistry & biodiversity. 2023; 20: e202200684.
9. Vogt A-CS, Jennings GT, Mohsen MO, Vogel M, Bachmann MF. Alzheimer’s Disease: A Brief History of Immunotherapies Targeting Amyloid β. International Journal of Molecular Sciences. 2023; 24: 3895.
10. Vitek GE, Decourt B, Sabbagh MN. Lecanemab (BAN2401): An anti-beta-amyloid monoclonal antibody for the treatment of Alzheimer disease. Expert Opinion on Investigational Drugs. 2023; 32: 89-94.
11. Teplan M. Fundamentals of EEG measurement. Measurement science review. 2002; 2: 1-1.
12. Soufineyestani M, Dowling D, Khan A. Electroencephalography (EEG) Technology Applications and Available Devices. Applied Sciences. 2020; 10: 7453.
13. Zhang N, Pan Y, Chen Q, Zhai Q, Liu N, et al. Application of EEG in migraine. Frontiers in human neuroscience. 2023; 17: 1082317.
14. Teplan M. Fundamentals of EEG measurement. Measurement science review. 2002; 2: 1-1.
15. Ehteshamzad S, Shkreli R. Abnormalities in EEG as migraine marker: a mini review. Journal of Biological Studies. 2023; 6: 220-229.
16. Rodríguez-Martín JL, Mauri-Llerda JA, Rivera-Gómez J. Effects of acetylcholinesterase inhibitors on quantitative EEG in Alzheimer’s disease patients. Neuropsychobiology. 2005; 52: 9-16.
17. Stam CJ, van der Made Y, Pijnenburg YA, Scheltens P. EEG synchronization in mild cognitive impairment and Alzheimer’s disease. Acta neurologica Scandinavica. 2003; 108: 90-96.
18. Tăuƫan AM, Casula EP, Pellicciari MC, Borghi I, Maiella M, et al. TMS-EEG perturbation biomarkers for Alzheimer’s disease patients classification. Scientific Reports. 2023; 13: 7667.
19. AlSharabi K, Salamah YB, Abdurraqeeb AM, Aljalal M, Alturki FA. EEG signal processing for Alzheimer’s disorders using discrete wavelet transform and machine learning approaches. IEEE Access. 2022 Aug 16; 10: 89781-97.
20. Babiloni C, Lizio R, Marzano N, Capotosto P, Soricelli A, et al. Brain neural synchronization and functional coupling in Alzheimer’s disease as revealed by resting state EEG rhythms. International Journal of Psychophysiology. 2016; 103: 88-102.
21. Jeong J. EEG dynamics in patients with Alzheimer’s disease. Clinical neurophysiology. 2004; 115: 1490-505.
22. Huang C, Wahlund LO, Dierks T, Julin P, Winblad B, et al. Discrimination of Alzheimer’s disease and mild cognitive impairment by equivalent EEG sources: A cross-sectional and longitudinal study. Clinical Neurophysiology. 2000; 111: 1961-7.
23. Adler G. The EEG as an indicator of cholinergic deficit in Alzheimer’s disease. Fortschritte der Neurologie-psychiatrie. 2000; 68: 352-6.
24. Dringenberg HC. Alzheimer’s disease: more than a ‘cholinergic disorder’-evidence that cholinergic-monoaminergic interactions contribute to EEG slowing and dementia. Behavioural brain research. 2000; 115: 235-49.
25. Bennys K, Rondouin G, Vergnes C, Touchon J. Diagnostic value of quantitative EEG in Alzheimer’s disease. Neurophysiologie Clinique/Clinical Neurophysiology. 2001; 31: 153-60.
26. Jeong J, Gore JC, Peterson BS. Mutual information analysis of the EEG in patients with Alzheimer’s disease. Clinical neurophysiology. 2001; 112: 827-35.
27. Jeong J. Nonlinear dynamics of EEG in Alzheimer’s disease. Drug development research. 2002; 56: 57-66.
28. Stam CJ, Van Der Made Y, Pijnenburg YA, Scheltens PH. EEG synchronization in mild cognitive impairment and Alzheimer’s disease. Acta neurologica scandinavica. 2003; 108: 90-6.
29. Moretti DV, Babiloni C, Binetti G, Cassetta E, Dal Forno G, et al. Individual analysis of EEG frequency and band power in mild Alzheimer’s disease. Clinical Neurophysiology. 2004; 115: 299-308.
30. Jeong J. EEG dynamics in patients with Alzheimer’s disease. Clinical neurophysiology. 2004; 115: 1490-505.
31. Abásolo D, Hornero R, Espino P, Poza J, Sánchez CI, et al. Analysis of regularity in the EEG background activity of Alzheimer’s disease patients with Approximate Entropy. Clinical neurophysiology. 2005; 116: 1826-34.
32. Stam CJ, Montez T, Jones BF, Rombouts SA, Van Der Made Y, et al. Disturbed fluctuations of resting state EEG synchronization in Alzheimer’s disease. Clinical neurophysiology. 2005; 116: 708-15.
33. Melissant C, Ypma A, Frietman EE, Stam CJ. A method for detection of Alzheimer’s disease using ICA-enhanced EEG measurements. Artificial Intelligence in Medicine. 2005; 33: 209-22.
34. Cichocki A, Shishkin SL, Musha T, Leonowicz Z, Asada T, et al. EEG filtering based on blind source separation (BSS) for early detection of Alzheimer’s disease. Clinical Neurophysiology. 2005; 116: 729-37.
35. Zheng-Yan J. Abnormal cortical functional connections in Alzheimer’s disease: Analysis of inter-and intra-hemispheric EEG coherence. Journal of Zhejiang University Science B. 2005; 6: 259-64.
36. Babiloni C, Binetti G, Cassarino A, Dal Forno G, Del Percio C, et al. Sources of cortical rhythms in adults during physiological aging: A multicentric EEG study. Human brain mapping. 2006; 27: 162-172.
37. Abásolo D, Hornero R, Gómez C, García M, López M. Analysis of EEG background activity in Alzheimer’s disease patients with Lempel-Ziv complexity and central tendency measure. Medical engineering & physics. 2006; 28: 315-22.
38. Van der Hiele K, Vein AA, Reijntjes RH, Westendorp RG, Bollen EL, et al. EEG correlates in the spectrum of cognitive decline. Clinical neurophysiology: Official journal of the International Federation of Clinical Neurophysiology. 2007; 118: 1931-1939
39. Lehmann C, Koenig T, Jelic V, Prichep L, John RE, et al. Application and comparison of classification algorithms for recognition of Alzheimer’s disease in electrical brain activity (EEG). Journal of neuroscience methods. 2007; 161: 342-50.
40. Babiloni C, Cassetta E, Binetti G, Tombini M, Del Percio C, et al. Resting EEG sources correlate with attentional span in mild cognitive impairment and Alzheimer’s disease. European Journal of Neuroscience. 2007; 25: 3742-57.
41. Ponomareva NV, Korovaitseva GI, Rogaev EI. EEG alterations in non-demented individuals related to apolipoprotein E genotype and to risk of Alzheimer disease. Neurobiology of aging. 2008; 29: 819-27.
42. Van Deursen JA, Vuurman EF, Verhey FR, van Kranen-Mastenbroek VH, Riedel WJ. Increased EEG gamma band activity in Alzheimer’s disease and mild cognitive impairment. Journal of neural transmission. 2008; 115: 1301-11.
43. Jelles B, Scheltens P, Van der Flier WM, Jonkman EJ, da Silva FL, and et al. Global dynamical analysis of the EEG in Alzheimer’s disease: Frequency-specific changes of functional interactions. Clinical neurophysiology. 2008; 119: 837-41.
44. Jackson CE, Snyder PJ. Electroencephalography and event-related potentials as biomarkers of mild cognitive impairment and mild Alzheimer’s disease. Alzheimer’s & Dementia. 2008; 4: S137-43.
45. Adeli H, Ghosh-Dastidar S, Dadmehr N. A spatio-temporal wavelet-chaos methodology for EEG-based diagnosis of Alzheimer’s disease. Neuroscience letters. 2008; 444: 190-4.
46. Czigler B, Csikós D, Hidasi Z, Gaál ZA, Csibri É, et al. Quantitative EEG in early Alzheimer’s disease patients-power spectrum and complexity features. International Journal of Psychophysiology. 2008; 68: 75-80.
47. Dauwels J, Vialatte F, Latchoumane C, Jeong J, Cichocki A. EEG synchrony analysis for early diagnosis of Alzheimer’s disease: A study with several synchrony measures and EEG data sets. In2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society. 2009; 2224-2227.
48. Babiloni C, Ferri R, Binetti G, Vecchio F, Frisoni GB, et al. Directionality of EEG synchronization in Alzheimer’s disease subjects. Neurobiology of aging. 2009; 30: 93-102.
49. Dauwels J, Vialatte F, Cichocki A. Diagnosis of Alzheimer’s disease from EEG signals: Where are we standing?. Current Alzheimer Research. 2010; 7: 487-505.
50. Dauwels J, Vialatte F, Musha T, Cichocki A. A comparative study of synchrony measures for the early diagnosis of Alzheimer’s disease based on EEG. NeuroImage. 2010; 49: 668-93.
51. Staudinger T, Polikar R. Analysis of complexity based EEG features for the diagnosis of Alzheimer’s disease. In2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society. 2011; 2033-2036.
52. Lizio R, Vecchio F, Frisoni GB, Ferri R, Rodriguez G, et al. Electroencephalographic rhythms in Alzheimer’s disease. International journal of Alzheimer’s disease. 2011; 927573.
53. Moretti DV, Prestia A, Fracassi C, Binetti G, Zanetti O, et al. Specific EEG changes associated with atrophy of hippocampus in subjects with mild cognitive impairment and Alzheimer’s disease. International journal of Alzheimer’s disease. 2012; 2012: 253153.
54. Fiscon G, Weitschek E, Felici G, Bertolazzi P, De Salvo S, et al. Alzheimer’s disease patients classification through EEG signals processing. In 2014 IEEE Symposium on Computational Intelligence and Data Mining (CIDM). 2014; 105-112.
55. Tsolaki A, Kazis D, Kompatsiaris I, Kosmidou V, Tsolaki M. Electroencephalogram and Alzheimer’s disease: Clinical and research approaches. International journal of Alzheimer’s disease. 2014; 2014: 349249.
56. Al-Jumeily D, Iram S, Vialatte FB, Fergus P, Hussain A. A novel method of early diagnosis of Alzheimer’s disease based on EEG signals. The Scientific World Journal. 2015; 2015: 931387.
57. Zhao Y, He L. Deep learning in the EEG diagnosis of Alzheimer’s disease. InComputer Vision-ACCV 2014 Workshops: Singapore, Singapore, November 1-2, 2014, Revised Selected Papers, Part I 12 2015. Springer International Publishing. 2014; 340-353.
58. Al-Nuaimi, A. H., Jammeh, E., Lingfen Sun, & Ifeachor, E. (2016). Changes in the EEG amplitude as a biomarker for early detection of Alzheimer’s disease. Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference. 2016; 993-996.
59. Babiloni C, Lizio R, Marzano N, Capotosto P, Soricelli A, et al. Brain neural synchronization and functional coupling in Alzheimer’s disease as revealed by resting state EEG rhythms. International Journal of Psychophysiology. 2016; 103: 88-102.
60. Hata M, Kazui H, Tanaka T, Ishii R, Canuet L, et al. Functional connectivity assessed by resting state EEG correlates with cognitive decline of Alzheimer’s disease-An eLORETA study. Clinical Neurophysiology. 2016; 127: 1269-78.
61. Simpraga S, Alvarez-Jimenez R, Mansvelder HD, Van Gerven JM, Groeneveld GJ, et al. EEG machine learning for accurate detection of cholinergic intervention and Alzheimer’s disease. Scientific reports. 2017; 7: 5775.
62. Nardone R, Sebastianelli L, Versace V, Saltuari L, Lochner P, et al. Usefulness of EEG Techniques in Distinguishing Frontotemporal Dementia from Alzheimer’s Disease and Other Dementias. Disease markers. 2018; 6581490.
63. Houmani N, Vialatte F, Gallego-Jutglà E, Dreyfus G, Nguyen-Michel VH, et al. Diagnosis of Alzheimer’s disease with Electroencephalography in a differential framework. PloS one. 2018; 13: e0193607.
64. Musaeus CS, Engedal K, Høgh P, Jelic V, Mørup M, et al. EEG theta power is an early marker of cognitive decline in dementia due to Alzheimer’s disease. Journal of Alzheimer’s Disease. 2018; 64: 1359-71.
65. Bagattini C, Mutanen TP, Fracassi C, Manenti R, Cotelli M, et al. Predicting Alzheimer’s disease severity by means of TMS-EEG coregistration. Neurobiology of aging. 2019; 80: 38-45.
66. Bi X, Wang H. Early Alzheimer’s disease diagnosis based on EEG spectral images using deep learning. Neural Networks. 2019; 114: 119-35.
67. Rossini PM, Di Iorio R, Vecchio F, Anfossi M, Babiloni C, et al. Early diagnosis of Alzheimer’s disease: the role of biomarkers including advanced EEG signal analysis. Report from the IFCN-sponsored panel of experts. Clinical Neurophysiology. 2020; 131: 1287-310.
68. Babiloni C, Blinowska K, Bonanni L, Cichocki A, De Haan W, et al. What electrophysiology tells us about Alzheimer’s disease: A window into the synchronization and connectivity of brain neurons. Neurobiology of aging. 2020; 85: 58-73.
69. Perez-Valero E, Lopez-Gordo MA, Morillas C, Pelayo F, Vaquero-Blasco MA. A review of automated techniques for assisting the early detection of Alzheimer’s disease with a focus on EEG. Journal of Alzheimer’s disease. 2021; 80: 1363-76.
70. Monllor P, Cervera-Ferri A, Lloret MA, Esteve D, Lopez B, et al. Electroencephalography as a Non-Invasive Biomarker of Alzheimer’s Disease: A Forgotten Candidate to Substitute CSF Molecules?. International Journal of Molecular Sciences. 2021; 22: 10889.
71. Rodrigues PM, Bispo BC, Garrett C, Alves D, Teixeira JP, et al. Lacsogram: A new EEG tool to diagnose Alzheimer’s disease. IEEE Journal of Biomedical and Health Informatics. 2021; 25: 3384-95.
72. Ouchani M, Gharibzadeh S, Jamshidi M, Amini M. A review of methods of diagnosis and complexity analysis of alzheimer’s disease using EEG signals. BioMed Research International. 2021; 2021: 1-5.
73. Vandana J, Nirali N. A review of EEG signal analysis for diagnosis of neurological disorders using machine learning. Journal of Biomedical Photonics & Engineering. 2021; 7: 40201.
74. Safi MS, Safi SM. Early detection of Alzheimer’s disease from EEG signals using Hjorth parameters. Biomedical Signal Processing and Control. 2021; 65: 102338.
75. Babiloni C, Arakaki X, Azami H, Bennys K, Blinowska K, et al. Measures of resting state EEG rhythms for clinical trials in Alzheimer’s disease: Recommendations of an expert panel. Alzheimer’s & Dementia. 2021; 17: 1528-53.
76. Pirrone D, Weitschek E, Di Paolo P, De Salvo S, De Cola MC. Eeg signal processing and supervised machine learning to early diagnose alzheimer’s disease. Applied sciences. 2022; 12: 5413.
77. Wang C, Xu T, Yu W, Li T, Han H, et al. Early diagnosis of Alzheimer’s disease and mild cognitive impairment based on electroencephalography: From the perspective of event related potentials and deep learning. International journal of psychophysiology: Official journal of the International Organization of Psychophysiology. 2022; 182: 182-189.
78. Fouad IA, Labib FE. Identification of Alzheimer’s disease from central lobe EEG signals utilizing machine learning and residual neural network. Biomedical Signal Processing and Control. 2023; 86: 105266.
79. kumar Ravikanti D, Saravanan S. EEG Alzheimer’s Net: Development of transformer-based attention long short term memory network for detecting Alzheimer disease using EEG signal. Biomedical Signal Processing and Control. 2023; 86: 105318.
80. Kim M, Jang S, Lee D, Lee S, Gwak J, et al. A comprehensive research setup for monitoring Alzheimer’s disease using EEG, fNIRS, and Gait analysis. Biomedical Engineering Letters. 2023 Aug 9: 1-9.
81. Azami H, Zrenner C, Brooks H, Zomorrodi R, Blumberger DM, et al. Beta to theta power ratio in EEG periodic components as a potential biomarker in mild cognitive impairment and Alzheimer’s dementia. Alzheimer’s Research & Therapy. 2023; 15: 133.
82. Khare SK, Acharya UR. Adazd-Net: Automated adaptive and explainable Alzheimer’s disease detection system using EEG signals. Knowledge-Based Systems. 2023 Jul 29: 110858.
83. Petit D, Gagnon JF, Fantini ML, Ferini-Strambi L, Montplaisir J. Sleep and quantitative EEG in neurodegenerative disorders. Journal of psychosomatic research. 2004; 56: 487-96.
84. Cozac VV, Gschwandtner U, Hatz F, Hardmeier M, Rüegg S, et al. Quantitative EEG and Cognitive Decline in Parkinson’s Disease. Parkinsons Dis. 2016; 2016: 9060649.
85. Wang Q, Meng L, Pang J, Zhu X, Ming D. Characterization of EEG data revealing relationships with cognitive and motor symptoms in Parkinson’s disease: A systematic review. Frontiers in aging neuroscience. 2020; 12: 587396.
86. Perrottelli A, Giordano GM, Brando F, Giuliani L, Pezzella P, et al. Unveiling the associations between EEG indices and cognitive deficits in schizophrenia-Spectrum disorders: A systematic review. Diagnostics. 2022; 12: 2193.
87. Lenartowicz A, Loo SK. Use of EEG to diagnose ADHD. Current psychiatry reports. 2014; 16: 1-1.
88. Saad JF, Kohn MR, Clarke S, Lagopoulos J, Hermens DF. Is the theta/beta EEG marker for ADHD inherently flawed?. Journal of attention disorders. 2018; 22: 815-26.
89. Wang J, Barstein J, Ethridge LE, Mosconi MW, Takarae Y, et al. Resting state EEG abnormalities in autism spectrum disorders. Journal of neurodevelopmental disorders. 2013; 5: 1-4.
90. Milovanovic M, Grujicic R. Electroencephalography in assessment of autism spectrum disorders: a review. Frontiers in psychiatry. 2021; 12: 686021.
91. Moretti DV, Prestia A, Fracassi C, Binetti G, Zanetti O, et al. Specific EEG changes associated with atrophy of hippocampus in subjects with mild cognitive impairment and Alzheimer’s disease. International journal of Alzheimer’s disease. 2012, 253153.
92. Kanda PA, Anghinah R, Smidth MT, Silva JM. The clinical use of quantitative EEG in cognitive disorders. Dementia & Neuropsychologia. 2009; 3: 195-203.
93. Gavaret M, Iftimovici A, Pruvost-Robieux E. EEG: Current relevance and promising quantitative analyses. Rev Neurol (Paris). 2023; 179: 352-360.
94. Jelic V, Kowalski J. Evidence-based evaluation of diagnostic accuracy of resting EEG in dementia and mild cognitive impairment. Clinical EEG and Neuroscience. 2009; 40: 129-42.