No description was included in this Dataset collected from the OSF

IntroductionDiagnosing Alzheimer's disease (AD) lesions via visual examination of Electroencephalography (EEG) signals poses a considerable challenge. This has prompted the exploration of deep learning techniques, such as Convolutional Neural Networks (CNNs) and Visual Transformers (ViTs), for AD prediction. However, the classification performance of CNN-based methods has often been deemed inadequate. This is primarily attributed to CNNs struggling with extracting meaningful lesion signals from the complex and noisy EEG data.MethodsIn contrast, ViTs have demonstrated proficiency in capturing global signal patterns. In light of these observations, we propose a novel approach to enhance AD risk assessment. Our proposition involves a hybrid architecture, merging the strengths of CNNs and ViTs to compensate for their respective feature extraction limitations. Our proposed Dual-Branch Feature Fusion Network (DBN) leverages both CNN and ViT components to acquire texture features and global semantic information from EEG signals. These elements are pivotal in capturing dynamic electrical signal changes in the cerebral cortex. Additionally, we introduce Spatial Attention (SA) and Channel Attention (CA) blocks within the network architecture. These attention mechanisms bolster the model's capacity to discern abnormal EEG signal patterns from the amalgamated features. To make well-informed predictions, we employ a two-factor decision-making mechanism. Specifically, we conduct correlation analysis on predicted EEG signals from the same subject to establish consistency.ResultsThis is then combined with results from the Clinical Neuropsychological Scale (MMSE) assessment to comprehensively evaluate the subject's susceptibility to AD. Our experimental validation on the publicly available OpenNeuro database underscores the efficacy of our approach. Notably, our proposed method attains an impressive 80.23% classification accuracy in distinguishing between AD, Frontotemporal dementia (FTD), and Normal Control (NC) subjects.DiscussionThis outcome outperforms prevailing state-of-the-art methodologies in EEG-based AD prediction. Furthermore, our methodology enables the visualization of salient regions within pathological images, providing invaluable insights for interpreting and analyzing AD predictions.

This dataset provides complementary material to the previously published dataset named “A dataset of EEG recordings from: Alzheimer's disease, Frontotemporal dementia and Healthy subjects” with doi:10.18112/openneuro.ds004504.v1.0.8. It is consisted of eyes-open EEG recordings in multiple photic stimulation settings, according to the clinical protocol of the 2nd department of Neurology, AHEPA University of Thessaloniki, Greece. The participant numbers match the respective participant numbers of the aforementioned dataset. In the clinical protocol, the 1st datasets recordings came first, followed by the recordings of this dataset. The dataset is designed to complement a previously published dataset in which the same cohort underwent EEG recordings with their eyes closed. During the recordings, participants were seated with their eyes open while being exposed to photic stimulation. The stimulation was administered at incremental frequencies, beginning at 5 Hz, progressing to 10 Hz, 15 Hz, and in some cases, extending up to 30 Hz, with increments of 5 Hz at each level. This study compared cognitive function in 36 individuals with Alzheimer's disease (AD), 23 with Frontotemporal Dementia (FTD), and 29 healthy controls (CN). Cognitive function was measured using the Mini-Mental State Examination (MMSE), where lower scores indicate greater cognitive impairment. The AD group had an average MMSE score of 17.75 (standard deviation of 4.5), the FTD group averaged 22.17 (standard deviation of 8.22), and the CN group scored 30. The average age was 66.4 (standard deviation of 7.9) for the AD group, 63.6 (standard deviation of 8.2) for the FTD group, and 67.9 (standard deviation of 5.4) for the CN group. The median disease duration was 25 months, with an interquartile range of 24 to 28.5 months. Notably, the AD group had no reported dementia-related comorbidities. Recordings: Recordings were aquired from the 2nd Department of Neurology of AHEPA General Hospital of Thessaloniki by an experienced team of neurologists. For recording, a Nihon Kohden EEG 2100 clinical device was used, with 19 scalp electrodes (Fp1, Fp2, F7, F3, Fz, F4, F8, T3, C3, Cz, C4, T4, T5, P3, Pz, P4, T6, O1, and O2) according to the 10-20 international system and 2 additional ectrodes (A1 and A2) placed on the mastoids for impendance check, according to the manual of the device. Each recording was performed according to the clinical protocol with participants being in a sitting position having their eyes closed. Before the initialization of each recording, the skin impedance value was ensured to be below 5k?. The sampling rate was 500 Hz with 10uV/mm resolution. The recording montages were anterior-posterior bipolar and referential montage using Cz as the common reference. The referential montage was included in this dataset. The recordings were received under the range of the following parameters of the amplifier: Sensitivity: 10uV/mm, time constant: 0.3s, and high frequency filter at 70 Hz. Each recording lasted approximately 4.86 minutes for AD group (min=1.30 minutes , max= 8.77 minutes), 4.42 minutes for FTD group (min=1.25 minutes, max=10.05 minutes) and 6.43 minutes for CN group (min=3.17 minutes, max= 9.17 minutes). In total, 174.94 minutes of AD, 101.56 minutes of FTD and 186.50 minutes of CN recordings were collected and are included in the dataset. Preprocessing: The EEG recordings were exported in .eeg format and are transformed to BIDS accepted .set format for the inclusion in the dataset. Automatic annotations of the Nihon Kohden EEG device marking artifacts (muscle activity, blinking, swallowing) have not been included for language compatibility purposes (If this is an issue, please use the preprocessed dataset in Folder: derivatives). The unprocessed EEG recordings are included in folders named: sub-0XX. Folders named sub-0XX in the subfolder derivatives contain the preprocessed and denoised EEG recordings. The preprocessing pipeline of the EEG signals is as follows. First, a Butterworth band-pass filter 0.5-45 Hz was applied and the signals were re-referenced to A1-A2. Then, the Artifact Subspace Reconstruction routine (ASR) which is an EEG artifact correction method included in the EEGLab Matlab software was applied to the signals, removing bad data periods which exceeded the max acceptable 0.5 second window standard deviation of 15, which is considered a conservative window. Next, the Independent Component Analysis (ICA) method (RunICA algorithm) was performed, transforming the 19 EEG signals to 19 ICA components. ICA components that were classified as “eye artifacts” or “jaw artifacts” by the automatic classification routine “ICLabel” in the EEGLAB platform were automatically rejected. It should be noted that, even though the recording was performed in a resting state, eyes-closed condition, eye artifacts of eye movement were still found at some EEG recordings.

BackgroundMost patients with Alzheimer's disease (AD) have an insidious onset and frequently atypical clinical symptoms, which are considered a normal consequence of aging, making it difficult to diagnose AD medically. But then again, accurate diagnosis is critical to prevent degeneration and provide early treatment for AD patients.ObjectiveThis study aims to establish a novel EEG-based classification framework with deep learning methods for AD recognition.MethodsFirst, considering the network interactions in different frequency bands (δ, θ, α, β, and γ), multiplex networks are reconstructed by the phase synchronization index (PSI) method, and fourteen topology features are extracted subsequently, forming a high-dimensional feature vector. However, in feature combination, not all features can provide effective information for recognition. Moreover, combining features by manual selection is time-consuming and laborious. Thus, a feature selection optimization algorithm called MOPSO-GDM was proposed by combining multi-objective particle swarm optimization (MOPSO) algorithm with Gaussian differential mutation (GDM) algorithm. In addition to considering the classification error rates of support vector machine, naive bayes, and discriminant analysis classifiers, our algorithm also considers distance measure as an optimization objective.ResultsFinally, this method proposed achieves an excellent classification error rate of 0.0531 (5.31%) with the feature vector size of 8, by a ten-fold cross-validation strategy.ConclusionThese findings show that our framework can adaptively combine the best brain network features to explore network synchronization, functional interactions, and characterize brain functional abnormalities, which can improve the recognition efficiency of diseases. While improving the classification accuracy of application algorithms, we aim to expand our understanding of the brain function of patients with neurological disorders through the analysis of brain networks.

This dataset contains the EEG resting state-closed eyes recordings from 88 subjects in total.
Participants: 36 of them were diagnosed with Alzheimer's disease (AD group), 23 were diagnosed with Frontotemporal Dementia (FTD group) and 29 were healthy subjects (CN group). Cognitive and neuropsychological state was evaluated by the international Mini-Mental State Examination (MMSE). MMSE score ranges from 0 to 30, with lower MMSE indicating more severe cognitive decline. The duration of the disease was measured in months and the median value was 25 with IQR range (Q1-Q3) being 24 - 28.5 months. Concerning the AD groups, no dementia-related comorbidities have been reported. The average MMSE for the AD group was 17.75 (sd=4.5), for the FTD group was 22.17 (sd=8.22) and for the CN group was 30. The mean age of the AD group was 66.4 (sd=7.9), for the FTD group was 63.6 (sd=8.2), and for the CN group was 67.9 (sd=5.4).

Recordings: Recordings were aquired from the 2nd Department of Neurology of AHEPA General Hispital of Thessaloniki by an experienced team of neurologists. For recording, a Nihon Kohden EEG 2100 clinical device was used, with 19 scalp electrodes (Fp1, Fp2, F7, F3, Fz, F4, F8, T3, C3, Cz, C4, T4, T5, P3, Pz, P4, T6, O1, and O2) according to the 10-20 international system and 2 reference electrodes (A1 and A2) placed on the mastoids for impendance check, according to the manual of the device. Each recording was performed according to the clinical protocol with participants being in a sitting position having their eyes closed. Before the initialization of each recording, the skin impedance value was ensured to be below 5k?. The sampling rate was 500 Hz with 10uV/mm resolution. The recording montages were anterior-posterior bipolar and referential montage using Cz as the common reference. The referential montage was included in this dataset. The recordings were received under the range of the following parameters of the amplifier: Sensitivity: 10uV/mm, time constant: 0.3s, and high frequency filter at 70 Hz. Each recording lasted approximately 13.5 minutes for AD group (min=5.1, max=21.3), 12 minutes for FTD group (min=7.9, max=16.9) and 13.8 for CN group (min=12.5, max=16.5). In total, 485.5 minutes of AD, 276.5 minutes of FTD and 402 minutes of CN recordings were collected and are included in the dataset.

Preprocessing: The EEG recordings were exported in .eeg format and are transformed to BIDS accepted .set format for the inclusion in the dataset. Automatic annotations of the Nihon Kohden EEG device marking artifacts (muscle activity, blinking, swallowing) have not been included for language compatibility purposes (If this is an issue, please use the preprocessed dataset in Folder: derivatives). The unprocessed EEG recordings are included in folders named: sub-0XX. Folders named sub-0XX in the subfolder derivatives contain the preprocessed and denoised EEG recordings. The preprocessing pipeline of the EEG signals is as follows. First, a Butterworth band-pass filter 0.5-45 Hz was applied and the signals were re-referenced to A1-A2. Then, the Artifact Subspace Reconstruction routine (ASR) which is an EEG artifact correction method included in the EEGLab Matlab software was applied to the signals, removing bad data periods which exceeded the max acceptable 0.5 second window standard deviation of 17, which is considered a conservative window. Next, the Independent Component Analysis (ICA) method (RunICA algorithm) was performed, transforming the 19 EEG signals to 19 ICA components. ICA components that were classified as “eye artifacts” or “jaw artifacts” by the automatic classification routine “ICLabel” in the EEGLAB platform were automatically rejected. It should be noted that, even though the recording was performed in a resting state, eyes-closed condition, eye artifacts of eye movement were still found at some EEG recordings.

A complete analysis of this dataset can be found in the published Data Descriptor paper "A Dataset of Scalp EEG Recordings of Alzheimer’s Disease, Frontotemporal Dementia and Healthy Subjects from Routine EEG", https://doi.org/10.3390/data8060095

Comparing individual EEG/ERP measures: Normalized effect size of the difference between the MCI and HC group and analysis of variance for each single EEG/ERP measure.

While several biomarkers have been developed for the detection of Alzheimer's disease (AD), not many are available for the prediction of disease severity, particularly for patients in the mild stages of AD. In this paper, we explore the multimodal prediction of Mini-Mental State Examination (MMSE) scores using resting-state electroencephalography (EEG) and structural magnetic resonance imaging (MRI) scans. Analyses were carried out on a dataset comprised of EEG and MRI data collected from 89 patients diagnosed with minimal-mild AD. Three feature selection algorithms were assessed alongside four machine learning algorithms. Results showed that while MRI features alone outperformed EEG features, when both modalities were combined, improved results were achieved. The top-selected EEG features conveyed information about amplitude modulation rate-of-change, whereas top-MRI features comprised information about cortical area and white matter volume. Overall, a root mean square error between predicted MMSE values and true MMSE scores of 1.682 was achieved with a multimodal system and a random forest regression model.

A growing number of studies apply deep neural networks (DNNs) to recordings of human electroencephalography (EEG) to identify a range of disorders. In many studies, EEG recordings are split into segments, and each segment is randomly assigned to the training or test set. As a consequence, data from individual subjects appears in both the training and the test set. Could high test-set accuracy reflect data leakage from subject-specific patterns in the data, rather than patterns that identify a disease? We address this question by testing the performance of DNN classifiers using segment-based holdout (in which segments from one subject can appear in both the training and test set), and comparing this to their performance using subject-based holdout (where all segments from one subject appear exclusively in either the training set or the test set). In two datasets (one classifying Alzheimer's disease, and the other classifying epileptic seizures), we find that performance on previously-unseen subjects is strongly overestimated when models are trained using segment-based holdout. Finally, we survey the literature and find that the majority of translational DNN-EEG studies use segment-based holdout. Most published DNN-EEG studies may dramatically overestimate their classification performance on new subjects.

A Polish Electroencephalography, Alzheimer’s Risk-genes, Lifestyle and Neuroimaging (PEARL-Neuro) Database

DOI of the data descriptor publication:

• doi.org/10.1038/s41597-024-03106-5 (https://www.nature.com/articles/s41597-024-03106-5)

Description of the database

Full cohort: 192 healthy middle-aged (50-63) individuals, balanced female and male ratio.

• Genetic data (N = 192):
  • Apolipoprotein E (APOE)
  • Phosphatidylinositol binding clathrin assembly protein (PICALM)
• Basic demographic and health data.
• Psychometric data (memory, intelligence, mood, personality, stress coping strategies).

Cohort subgroup: 79 healthy middle-aged (50-63) individuals, balanced female and male ratio.

• Neuroimaging data:
  • Functional data — electroencefalography (EEG) and functional magnetic resonance imaging (fMRI):
    • Resting-state protocol (with two conditions: eyes open and eyes closed)
    • Cognitive tasks: multi-source interference task (MSIT) and Sternberg’s memory task
• Blood tests data (blood count, lipid profile, HSV virus).

Please cite the following references if you use these data:

• Dzianok, P., Kublik, E. PEARL-Neuro Database: EEG, fMRI, health and lifestyle data of middle-aged people at risk of dementia. Sci Data 11, 276 (2024). https://doi.org/10.1038/s41597-024-03106-5
• Dzianok, O., Kublik, E. A Polish Electroencephalography, Alzheimer’s Risk-genes, Lifestyle and Neuroimaging (PEARL-Neuro) Database (2024). doi: 10.18112/openneuro.ds004796.v1.0.5

Additional info:

The data are described as a data descriptor article: * doi.org/10.1038/s41597-024-03106-5 (https://www.nature.com/articles/s41597-024-03106-5)

Publications related to this dataset:

• Dzianok P, Kublik E. Altered granulocyte count and erythrocyte measures in middle-aged, healthy carriers of APOE and PICALM risk genes for Alzheimer's disease. Acta Neurobiol Exp. 2023;83(2):127-139. doi:10.55782/ane-2023-012
• Preprint available soon: Effect of APOE/PICALM risk-genes on resting-state EEG in cognitively intact and healthy middle-aged individuals is in-line with Alzheimer’s disease EEG hallmarks (data used: EEG & fMRI resting-state eyes-closed data, behavioural and psychometric/health data in regard to the genetic groups)
• Preprint available soon: Cognitive resilience or vulnerability? The role of APOE/PICALM variants in attention and memory tasks among healthy individuals (data used: EEG & fMRI MSIT and Sternberg data, behavioural and psychometric/health data in regard to the genetic groups)

Additional data:

• Structural MRI data are protected and available upon request

Release history:

• 10/2023: Initial release
• 02/2024: Public release

Scientific Research for publishing the article

Additional file 1: Supplementary Table 1. Difference in functional connectivity (FC) between SCD and AD subjects estimated by ANOVA model 1 (correction for age and gender). Supplementary Table 2. Difference in functional connectivity (FC) between SCD and AD subjects estimated by ANOVA model 2 (correction for age, gender and global relative power). Supplementary Figure S1. Topographical distribution of the median difference in Z-score of the PLI and AEC-c between the SCD and AD subjects. Supplementary Figure S2A & 2B. Summary of observed differences in ANOVA model 1, shown as effect size (Cohen’s d), between AD and SCD subjects in different regions for the AEC-c and PLI in each bandwidth. Supplementary Figure S3A-C. Correlation coefficients (r) between functional connectivity measures in the theta (A), alpha (B) and beta (C) bandwidths. In each figure, the coefficients on the right are corrected for changes in relative power and the coefficients on the left are the uncorrected values.

Connectivity matrices of patients with Alzheimer´s disease computed with conditional mutual information

The EEG biosensors market is experiencing robust growth, driven by advancements in neuroscience research, increasing demand for neurofeedback therapy, and the rising prevalence of neurological disorders. The market size in 2025 is estimated at $500 million, exhibiting a Compound Annual Growth Rate (CAGR) of 12% from 2025 to 2033. This growth is fueled by several key factors. Firstly, the ongoing development of more portable, wearable, and user-friendly EEG biosensors is expanding their applications beyond clinical settings. Secondly, the increasing adoption of EEG technology in brain-computer interfaces (BCIs) for gaming, assistive technologies, and neuroprosthetics is creating substantial market opportunities. Thirdly, the rising awareness and diagnosis of neurological conditions such as epilepsy, Alzheimer's disease, and Parkinson's disease are driving demand for accurate and accessible diagnostic tools, further bolstering market growth. However, certain restraints are anticipated to slightly impede market expansion. High initial costs associated with acquiring EEG systems, along with the complexity of data analysis and interpretation, might present barriers to entry for smaller clinics and researchers. Moreover, the need for skilled professionals to operate and interpret EEG data could limit widespread adoption in certain regions. Despite these limitations, the overall market outlook remains positive. The development of sophisticated algorithms for signal processing and analysis, coupled with advancements in artificial intelligence (AI), is streamlining data interpretation, making EEG technology more accessible and efficient. This trend, coupled with ongoing research and development, is likely to drive further market expansion throughout the forecast period. Segmentation within the market includes various device types (dry, wet, wireless), application areas (clinical diagnostics, research, neurofeedback), and end-users (hospitals, research institutions, and private practices). Key players like NeuroSky, iMotions, and g.tec are actively shaping this dynamic market landscape through innovation and strategic partnerships.

Research data

Research data

The global Electroencephalography (EEG) Devices market is experiencing robust growth, projected to reach $425.6 million in 2025 and maintain a Compound Annual Growth Rate (CAGR) of 5.3% from 2025 to 2033. This expansion is fueled by several key drivers. The increasing prevalence of neurological disorders like epilepsy, sleep disorders, and Alzheimer's disease necessitates advanced diagnostic tools, driving demand for EEG devices. Technological advancements, such as portable and wireless EEG systems, are enhancing accessibility and improving diagnostic accuracy, further stimulating market growth. Furthermore, the rising adoption of EEG in various healthcare settings, including hospitals, clinics, and research institutions, contributes significantly to market expansion. The segment of portable EEG devices is expected to witness faster growth due to the increasing need for convenient and cost-effective diagnostics, particularly in remote areas. However, certain factors restrain market growth. The high cost of advanced EEG devices can pose a barrier to adoption, especially in resource-constrained settings. Moreover, the need for skilled professionals to operate and interpret EEG data necessitates specialized training and expertise, potentially limiting widespread accessibility. Despite these challenges, the market's growth trajectory remains positive, driven by ongoing technological innovation, increased healthcare spending, and a growing awareness of neurological disorders. North America currently holds a substantial market share due to advanced healthcare infrastructure and a high prevalence of neurological diseases; however, emerging economies in Asia Pacific are expected to exhibit significant growth potential in the coming years due to increasing healthcare investments and rising awareness of neurological health. The market is segmented by application (hospital, physical examination center) and type (stationary, portable), offering varied opportunities for stakeholders across the value chain.

The global neurological diagnostic equipment market is experiencing steady growth, projected to reach $1109 million in 2025 and maintain a Compound Annual Growth Rate (CAGR) of 5.6% from 2025 to 2033. This growth is fueled by several key factors. The rising prevalence of neurological disorders like Alzheimer's disease, Parkinson's disease, and stroke, coupled with an aging global population, significantly increases the demand for accurate and timely diagnosis. Technological advancements, such as the development of sophisticated neuroimaging techniques (EEG, fMRI, PET), portable and user-friendly devices, and improved data analytics capabilities, are enhancing diagnostic accuracy and efficiency, further driving market expansion. Increased healthcare spending, particularly in developed economies, and the rising adoption of minimally invasive procedures are also contributing positively. However, high costs associated with advanced equipment, stringent regulatory approvals, and the need for skilled professionals to operate and interpret the data pose some challenges to market growth. Competition among established players like Nihon Kohden, Natus Medical Incorporated, DePuy Synthes, Tristan Technologies, Neurosign Surgical, and EMS Biomedical is likely to intensify as the market matures. The market segmentation, although not explicitly provided, likely includes various equipment types (e.g., EEG machines, EMG machines, nerve conduction study devices, neuroimaging systems), based on end-users (hospitals, clinics, research centers), and geographical regions. The market's future trajectory will depend on continued technological innovation, regulatory landscape changes, reimbursement policies, and the efficacy of ongoing research in neurological disorders. Strategic partnerships and collaborations between manufacturers and healthcare providers are expected to play a crucial role in shaping the market's future dynamics and accessibility of advanced diagnostic solutions. The expansion into emerging markets will also significantly impact market growth, driving affordability and accessibility in underserved regions.

The global quantitative digital electroencephalograph (qEEG) market is experiencing robust growth, driven by the increasing prevalence of neurological and mental health disorders, advancements in brain-computer interface technology, and the rising adoption of qEEG in neuroscience research. The market, estimated at $500 million in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 7% from 2025 to 2033, reaching approximately $850 million by 2033. This growth is fueled by several key factors. The rising demand for accurate and objective diagnostic tools for neurological conditions like epilepsy, Alzheimer's disease, and traumatic brain injuries is significantly boosting market expansion. Furthermore, the burgeoning field of brain-computer interfaces (BCIs) relies heavily on qEEG technology for signal acquisition and processing, driving substantial investment and innovation in the sector. The development of advanced qEEG systems with higher channel counts (e.g., 64 channels) offers improved spatial resolution and diagnostic capabilities, further fueling market growth. While high initial investment costs and the need for specialized expertise might pose some challenges, the overall market outlook remains positive, driven by technological advancements and increasing healthcare expenditure globally. The market segmentation reveals a significant share held by the neuroscience research application, reflecting the technology's crucial role in understanding brain function and developing new therapies. The 64-channel systems dominate the market due to their superior diagnostic capabilities, offering more detailed brainwave data. Geographically, North America currently holds a leading position, owing to the strong presence of key players, advanced healthcare infrastructure, and significant research funding. However, the Asia-Pacific region is expected to witness the fastest growth over the forecast period, driven by rising healthcare expenditure, growing awareness of neurological disorders, and increasing adoption of advanced medical technologies. Key players such as Brain Products, Compumedics Neuroscan, and ANT Neuro are actively involved in product innovation and expansion into emerging markets, fostering competition and further driving market growth. The continued focus on developing portable and user-friendly qEEG systems will enhance accessibility and contribute significantly to the market's expansion in the coming years.

The high-density EEG (hdEEG) market is experiencing robust growth, driven by the increasing prevalence of neurological disorders, advancements in EEG technology, and the rising demand for accurate and detailed brain activity monitoring. The market, estimated at $500 million in 2025, is projected to witness a Compound Annual Growth Rate (CAGR) of 7% from 2025 to 2033, reaching approximately $850 million by 2033. This growth is fueled by several key factors. Firstly, the aging global population is leading to an increased incidence of neurological conditions like epilepsy, Alzheimer's disease, and stroke, all of which necessitate accurate diagnostic tools like hdEEG. Secondly, technological advancements, such as improved sensor technology and advanced signal processing algorithms, are enhancing the accuracy and efficiency of hdEEG systems, attracting broader adoption among healthcare professionals. Furthermore, the rising adoption of minimally invasive procedures and the increasing preference for ambulatory EEG monitoring are contributing to market expansion. Market segmentation reveals that 128-channel systems currently dominate the market, but 256-channel and other higher-density systems are gaining traction due to their superior diagnostic capabilities. Hospitals and diagnostic centers account for the largest portion of market share, owing to the availability of skilled professionals and advanced infrastructure for EEG testing. However, the "Others" segment is also exhibiting significant growth potential, reflecting the expanding use of hdEEG in research settings and specialized clinics. Geographic variations exist, with North America and Europe currently leading the market due to high healthcare expenditure and well-established healthcare infrastructure; however, the Asia-Pacific region, particularly China and India, shows significant growth potential due to increasing healthcare investments and a rising prevalence of neurological disorders. Competitive dynamics within the hdEEG market are shaped by a mix of established players and emerging companies. Key players like Nihon Kohden, Natus Medical, Medtronic (Covidien), Compumedics, Philips Healthcare, Micromed S.p.A., Cadwell, and NCC Medical are continuously innovating to enhance their product offerings, expand their market reach, and strengthen their competitive positions. The market is likely to witness increasing mergers and acquisitions as companies strive for a larger market share and seek to broaden their product portfolios. Strategic partnerships between manufacturers and healthcare providers are also becoming increasingly common, facilitating wider access to hdEEG technology and fostering collaborative research and development efforts. The market’s future growth is contingent on continued technological advancements, favorable regulatory environments, and the overall expansion of neurological healthcare services globally.

The global adult EEG cap market, valued at $26 million in 2025, is projected to experience a compound annual growth rate (CAGR) of 1.1% from 2025 to 2033. This relatively modest growth reflects a mature market with established players and a relatively stable technological landscape. However, several factors are influencing market dynamics. The increasing prevalence of neurological disorders like epilepsy, Alzheimer's disease, and sleep disorders is driving demand for EEG technology in clinical settings. Furthermore, advancements in EEG cap design, such as improved comfort and ease of use, are contributing to market growth. The incorporation of dry electrodes is reducing the need for messy gels, making EEG procedures more convenient and accessible, particularly in ambulatory settings and home healthcare. Research applications, requiring high-purity tin and Ag/AgCl electrodes, also fuel market expansion. The market is segmented by electrode type (High-purity Tin Electrode, Ag/AgCl Electrode) and application (Medical, Research), with the medical segment holding the largest market share due to the rising prevalence of neurological conditions and the increasing adoption of EEG in diagnostics. Regional variations exist, with North America and Europe currently dominating the market due to higher healthcare expenditure and technological advancements. However, growth opportunities exist in emerging markets in Asia-Pacific as healthcare infrastructure improves and awareness of EEG technology increases. The competitive landscape is characterized by numerous established players including Brain Products, ANT Neuro, and Compumedics Neuroscan, alongside several smaller companies specializing in specific segments. Competition is primarily based on factors such as electrode quality, data acquisition systems integration, ease of use, and after-sales service. Technological innovations, particularly in electrode materials and signal processing, will likely play a crucial role in shaping future market dynamics. Continued improvements in EEG cap design to enhance patient comfort and reduce artifact contamination are expected to influence market growth. The expansion into remote monitoring applications and telemedicine is also poised to create further growth potential. In summary, while growth is projected to be moderate, the market remains vibrant and dynamic, driven by health trends and technological advancements.