Dataset Open Access

Resting-State High-Density EEG using EGI GES 300 with 256 Channels of Healthy Elders, People with Subjective and Mild Cognitive Impairment and Alzheimer's Disease

Ioulietta Lazarou; Kostas Georgiadis; Spiros Nikolopoulos; Vangelis Oikonomou; Ioannis Kompatsiaris

Project member(s)
Anthoula Tsolaki; Magda Tsolaki

This repository contains Matlab files including 4 samples of resting-state EEG recording for Alzheimer's Disease (AD), Mild Cognitive Impairment (MCI), Subjective Cognitive Decline (SCD), and Healthy Controls (HC) using the HD-EEG EGI GES 300.

[AD: i108, MCI: i100, SCD: i090, HC: s055]

 

Participants & Settings

In total 230 participants have been recruited from the memory and dementia clinic of the Greek Association of Alzheimer’s Disease and Related Disorders (GAADRD) and the 1st Department of Neurology, U.H. AHEPA, Aristotle University of Thessaloniki, Greece.

The full dataset includes:

Healthy Controls Elders (60+ years old): 33 participants

Subjective Cognitive Decline: 34 participants

Mild Cognitive Impairment: 79 participants

Alzheimer's Disease: 48 participants

Healthy Young (25-40 years old): 36 participants

The study was carried out in accordance with the Declaration of Helsinki and received approval by the Scientific and Ethics Committee of GAADRD (No56_27/11/2016), and written informed consent was obtained from all participants prior to their participation in the study. The diagnosis of AD was conducted by a neuropsychiatrist according to their medical history, neuropsychological performance, structural magnetic resonance imaging (MRI), and clinical and neurological examinations.

Participants with AD fulfilled the National Institute of Neurological and Communication Disorders and Stroke/Alzheimer’s Disease and Related Disorders Association (NINCDS-ADRDA) criteria for probable AD, as well as the Diagnostic and Statistical Manual of Mental Disorders (DSM-V) criteria for dementia of Alzheimer’s type (American Psychological Association, 1994). On the other hand, the MCI participants fulfilled the Petersen criteria, while the SCD group met International Working Group-2 guidelines and the recent National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease (NI-AA), as well as the SCD-I Working Group instructions. 

Resting-State EEG Recording

Fifteen-minute resting EEG activity was recorded for all the participants. For the whole duration of the resting state EEG recording, participants were advised to keep themselves relaxed as much as possible, close their eyes and open them after the researcher’s demand, sit still, minimize blinking or mouth movements and let their mind wander. The experimental procedure was monitored by a research assistant aiming to identify cases of horizontal eye movements, continued blinking, or excessive movement by visually inspecting the EEG traces during the experiment. More specifically, an EEG was registered for both resting conditions (eyes open, EO and eyes closed, EC) for at least 2–3 min for each period.

EEG Data Acquisition

The EEG data were collected by using the EGI 300 Geodesic EEG system (GES 300, CERTH-ITI, Thessaloniki, Greece) with a 256-channel HydroCel Geodesic Sensor Net (HCGSN) and a sampling rate of 250 Hz (EGI Eugene, OR). Moreover, the researcher placed the electrodes in accordance with the 256 HCGSN adult 1.0 montage system, while the signals were recorded relative to a vertex reference electrode (Cz), with AFz as the ground electrode with the electrodes’ impedance below 50 kΩ throughout the experimental procedure, as recommended for the high-input impedance amplifier. In detail, the HD-EEG data were analyzed offline in order to detect any artifact, as well as to conduct pre-processing (filtering, segmentation, bad channel replacement) using Net Station 4.3 software (EGI). HD-EEG data were initially filtered with a 5th-order bandpass Butterworth IIR filter of 0.3–30 Hz. Once the segmentation was completed, the detection of artifacts was performed by using the Net Station artifact detection tool for the automatic detection of excessive eye blinking and movement. Afterward, the signals were baseline corrected using 200 msec before the start of the experiment period and average re-referenced to transform them into reference-independent values.

 

Full Dataset Access

More information about the sample dataset and access to the full dataset can be available after request via e-mail:

Ioulietta Lazarou BSc, MSc, PhD candidate

Neuropsychologist - Clinical Research Associate 

Centre for Research and Technology Hellas (CERTH), Information Technologies Institute (ITI)

6th km Charilaou-Thermi Road, P.O. Box 60361, 57001 Thermi-Thessaloniki, Greece

E-mail: iouliettalaz@iti.gr

Files (2.8 GB)
Name Size
i090 20150826 1240.fil.seg..mat
md5:8a0455d8337779db0241c2af5f301700
925.3 MB Download
i100 20150915 1150.fil.seg..mat
md5:cebb718a69f7be99a335d00661c0421c
616.8 MB Download
i108 20150921 1206.fil.seg..mat
md5:71459b97342e551893f7098512df672c
616.8 MB Download
S055 20140228 0922.fil.seg..mat
md5:aa7103239ff44aa0ba357a6607189700
616.9 MB Download
  • https://pubmed.ncbi.nlm.nih.gov/32575641/

  • https://pubmed.ncbi.nlm.nih.gov/30103320/

  • https://pubmed.ncbi.nlm.nih.gov/27810392/

  • https://pubmed.ncbi.nlm.nih.gov/25445998/

  • https://pubmed.ncbi.nlm.nih.gov/27485659/

  • https://pubmed.ncbi.nlm.nih.gov/26738045/

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