-
Categories
-
Pharmaceutical Intermediates
-
Active Pharmaceutical Ingredients
-
Food Additives
- Industrial Coatings
- Agrochemicals
- Dyes and Pigments
- Surfactant
- Flavors and Fragrances
- Chemical Reagents
- Catalyst and Auxiliary
- Natural Products
- Inorganic Chemistry
-
Organic Chemistry
-
Biochemical Engineering
- Analytical Chemistry
-
Cosmetic Ingredient
- Water Treatment Chemical
-
Pharmaceutical Intermediates
Promotion
ECHEMI Mall
Wholesale
Weekly Price
Exhibition
News
-
Trade Service
Essay topic:
A resource for assessing dynamic binary choices in the adult brain using EEG and mouse-trackingImpact factor:
-
2-year impact factor: 6.
-
5-year impact factor: 9.
Published: July 2022
Authors: Kun Chen, Ruien Wang, Jiamin Huang, Fei Gao, Zhen Yuan, Yanyan Qi & Haiyan Wu
Publishing URL: https://doi.
Summary:
We present a dataset combining high-density electroencephalography (HD-EEG, 128 channels) and mouse tracking, intended as a data resource for examining the dynamic decision-making processes of semantic and preference choice in the human brain
experimental method:
Thirty-one college students (18-33 years old, mean: 20.
Experimental procedure
The subjects sat in an adjustable chair with their eyes approximately 60 cm from the monitor (dell, resolution: 1,920 × 1,080 pixels, vertical refresh rate: 60 Hz)
Stimulus presentation and manual response measurements were presented and recorded by PsychoPy Standalone (2020.
The experiment has a resting task and three decision-related tasks, as shown in the following figure:
Schematic diagram of the experimental task
data record:
All data are publicly accessible in the Brain Imaging Data Structure (BIDS) format under the OpenNeuro platform (https://openneuro.
EEG data collection
Experimental Acquisition As shown in the figure below, EEG data was acquired using a 128-channel cap based on a standard 10/20 system and an EGI EEG system
Schematic diagram of data collection
data analysis
After data preprocessing, resting-state EEG data were analyzed using the Microstate plugin in EEGLAB
Schematic diagram of microstates
Select Task EEGComparison of left-right selection during food tasks, and comparison of presence and absence attributes of image-choice and word-choice tasks on electrode Cz
The time-frequency plots of the three tasks were averaged at the group level
Decoding selection using EEG
Use support vector machine classifier (with SVC function) to perform decoding analysis in MNE-Python combined with Scikit-learn
Average topographic distributions before stimulus onset - 100 ms, 200 ms, 500 ms and 800 ms across the three tasks
Data and code ease of use:
Most software or packages for analyzing these data are freely available
references:
Chen, K.
The above part of the content refers to the above literature.
