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128 channel dense array eeg system  (Welcony)


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    Welcony 128 channel dense array eeg system
    Schematic overview of minimum spanning tree (MST) analysis applied to electroencephalogram (EEG) data in this study. (A) Raw EEG data was collected by an <t>EGI</t> <t>128-channel</t> dense array EEG system with Hydrogel Geodesic Sensor Nets. (B) Independent component analysis (ICA) was used to clean the epoched data. In addition to removing artifacts common to children with normal hearing (NH) and children with cochlear implants (CI), ICA was performed repeatedly on data from the CI group to isolate and eliminate the artifacts associated with the cochlear implant (CI artifacts). If the activated scalp showed a centroid on the side of the CI, then the independent component was defined as the CI artifact and removed. (C) Seventy-two artifact-free 2-s epochs were filtered for each frequency band (delta: 0.5–4 Hz, theta: 4–8 Hz, alpha: 8–13 Hz, and beta: 13–30 Hz). (D) A resting-state functional connectivity matrix based on the phase lag index (PLI) was constructed for each frequency band and each epoch. (E) Kruskal's algorithm was used to construct a minimum spanning tree (MST) matrix. (F) Topological characteristics of the tree can be characterized by metrics including maximum node degree (degree), leaf number (leaf), diameter, eccentricity, radius, strength, maximum betweenness centrality (BC), closeness centrality (CC), kappa, and tree hierarchy (Th). (G) Speech perception tests including tone, vowel, and consonant discrimination were complected by participants, and they were required to decide whether two consecutive sounds were the same as accurately and rapidly as possible.
    128 Channel Dense Array Eeg System, supplied by Welcony, used in various techniques. Bioz Stars score: 96/100, based on 1261 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/result/128 channel dense array eeg system/product/Welcony
    Average 96 stars, based on 1261 article reviews
    128 channel dense array eeg system - by Bioz Stars, 2026-06
    96/100 stars

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    1) Product Images from "Resting-state EEG reveals global network deficiency in prelingually deaf children with late cochlear implantation"

    Article Title: Resting-state EEG reveals global network deficiency in prelingually deaf children with late cochlear implantation

    Journal: Frontiers in Pediatrics

    doi: 10.3389/fped.2022.909069

    Schematic overview of minimum spanning tree (MST) analysis applied to electroencephalogram (EEG) data in this study. (A) Raw EEG data was collected by an EGI 128-channel dense array EEG system with Hydrogel Geodesic Sensor Nets. (B) Independent component analysis (ICA) was used to clean the epoched data. In addition to removing artifacts common to children with normal hearing (NH) and children with cochlear implants (CI), ICA was performed repeatedly on data from the CI group to isolate and eliminate the artifacts associated with the cochlear implant (CI artifacts). If the activated scalp showed a centroid on the side of the CI, then the independent component was defined as the CI artifact and removed. (C) Seventy-two artifact-free 2-s epochs were filtered for each frequency band (delta: 0.5–4 Hz, theta: 4–8 Hz, alpha: 8–13 Hz, and beta: 13–30 Hz). (D) A resting-state functional connectivity matrix based on the phase lag index (PLI) was constructed for each frequency band and each epoch. (E) Kruskal's algorithm was used to construct a minimum spanning tree (MST) matrix. (F) Topological characteristics of the tree can be characterized by metrics including maximum node degree (degree), leaf number (leaf), diameter, eccentricity, radius, strength, maximum betweenness centrality (BC), closeness centrality (CC), kappa, and tree hierarchy (Th). (G) Speech perception tests including tone, vowel, and consonant discrimination were complected by participants, and they were required to decide whether two consecutive sounds were the same as accurately and rapidly as possible.
    Figure Legend Snippet: Schematic overview of minimum spanning tree (MST) analysis applied to electroencephalogram (EEG) data in this study. (A) Raw EEG data was collected by an EGI 128-channel dense array EEG system with Hydrogel Geodesic Sensor Nets. (B) Independent component analysis (ICA) was used to clean the epoched data. In addition to removing artifacts common to children with normal hearing (NH) and children with cochlear implants (CI), ICA was performed repeatedly on data from the CI group to isolate and eliminate the artifacts associated with the cochlear implant (CI artifacts). If the activated scalp showed a centroid on the side of the CI, then the independent component was defined as the CI artifact and removed. (C) Seventy-two artifact-free 2-s epochs were filtered for each frequency band (delta: 0.5–4 Hz, theta: 4–8 Hz, alpha: 8–13 Hz, and beta: 13–30 Hz). (D) A resting-state functional connectivity matrix based on the phase lag index (PLI) was constructed for each frequency band and each epoch. (E) Kruskal's algorithm was used to construct a minimum spanning tree (MST) matrix. (F) Topological characteristics of the tree can be characterized by metrics including maximum node degree (degree), leaf number (leaf), diameter, eccentricity, radius, strength, maximum betweenness centrality (BC), closeness centrality (CC), kappa, and tree hierarchy (Th). (G) Speech perception tests including tone, vowel, and consonant discrimination were complected by participants, and they were required to decide whether two consecutive sounds were the same as accurately and rapidly as possible.

    Techniques Used: Functional Assay, Construct



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    Schematic overview of minimum spanning tree (MST) analysis applied to electroencephalogram (EEG) data in this study. (A) Raw EEG data was collected by an <t>EGI</t> <t>128-channel</t> dense array EEG system with Hydrogel Geodesic Sensor Nets. (B) Independent component analysis (ICA) was used to clean the epoched data. In addition to removing artifacts common to children with normal hearing (NH) and children with cochlear implants (CI), ICA was performed repeatedly on data from the CI group to isolate and eliminate the artifacts associated with the cochlear implant (CI artifacts). If the activated scalp showed a centroid on the side of the CI, then the independent component was defined as the CI artifact and removed. (C) Seventy-two artifact-free 2-s epochs were filtered for each frequency band (delta: 0.5–4 Hz, theta: 4–8 Hz, alpha: 8–13 Hz, and beta: 13–30 Hz). (D) A resting-state functional connectivity matrix based on the phase lag index (PLI) was constructed for each frequency band and each epoch. (E) Kruskal's algorithm was used to construct a minimum spanning tree (MST) matrix. (F) Topological characteristics of the tree can be characterized by metrics including maximum node degree (degree), leaf number (leaf), diameter, eccentricity, radius, strength, maximum betweenness centrality (BC), closeness centrality (CC), kappa, and tree hierarchy (Th). (G) Speech perception tests including tone, vowel, and consonant discrimination were complected by participants, and they were required to decide whether two consecutive sounds were the same as accurately and rapidly as possible.
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    Schematic overview of minimum spanning tree (MST) analysis applied to electroencephalogram (EEG) data in this study. (A) Raw EEG data was collected by an EGI 128-channel dense array EEG system with Hydrogel Geodesic Sensor Nets. (B) Independent component analysis (ICA) was used to clean the epoched data. In addition to removing artifacts common to children with normal hearing (NH) and children with cochlear implants (CI), ICA was performed repeatedly on data from the CI group to isolate and eliminate the artifacts associated with the cochlear implant (CI artifacts). If the activated scalp showed a centroid on the side of the CI, then the independent component was defined as the CI artifact and removed. (C) Seventy-two artifact-free 2-s epochs were filtered for each frequency band (delta: 0.5–4 Hz, theta: 4–8 Hz, alpha: 8–13 Hz, and beta: 13–30 Hz). (D) A resting-state functional connectivity matrix based on the phase lag index (PLI) was constructed for each frequency band and each epoch. (E) Kruskal's algorithm was used to construct a minimum spanning tree (MST) matrix. (F) Topological characteristics of the tree can be characterized by metrics including maximum node degree (degree), leaf number (leaf), diameter, eccentricity, radius, strength, maximum betweenness centrality (BC), closeness centrality (CC), kappa, and tree hierarchy (Th). (G) Speech perception tests including tone, vowel, and consonant discrimination were complected by participants, and they were required to decide whether two consecutive sounds were the same as accurately and rapidly as possible.

    Journal: Frontiers in Pediatrics

    Article Title: Resting-state EEG reveals global network deficiency in prelingually deaf children with late cochlear implantation

    doi: 10.3389/fped.2022.909069

    Figure Lengend Snippet: Schematic overview of minimum spanning tree (MST) analysis applied to electroencephalogram (EEG) data in this study. (A) Raw EEG data was collected by an EGI 128-channel dense array EEG system with Hydrogel Geodesic Sensor Nets. (B) Independent component analysis (ICA) was used to clean the epoched data. In addition to removing artifacts common to children with normal hearing (NH) and children with cochlear implants (CI), ICA was performed repeatedly on data from the CI group to isolate and eliminate the artifacts associated with the cochlear implant (CI artifacts). If the activated scalp showed a centroid on the side of the CI, then the independent component was defined as the CI artifact and removed. (C) Seventy-two artifact-free 2-s epochs were filtered for each frequency band (delta: 0.5–4 Hz, theta: 4–8 Hz, alpha: 8–13 Hz, and beta: 13–30 Hz). (D) A resting-state functional connectivity matrix based on the phase lag index (PLI) was constructed for each frequency band and each epoch. (E) Kruskal's algorithm was used to construct a minimum spanning tree (MST) matrix. (F) Topological characteristics of the tree can be characterized by metrics including maximum node degree (degree), leaf number (leaf), diameter, eccentricity, radius, strength, maximum betweenness centrality (BC), closeness centrality (CC), kappa, and tree hierarchy (Th). (G) Speech perception tests including tone, vowel, and consonant discrimination were complected by participants, and they were required to decide whether two consecutive sounds were the same as accurately and rapidly as possible.

    Article Snippet: Five min of EO resting-state and 5 min EC resting-state EEG data were recorded at a 1,000 Hz sampling rate using an EGI 128-channel dense array EEG system with the Hydrogel Geodesic Sensor Nets EGI 128-channel system (Electrical Geodesics Inc., USA).

    Techniques: Functional Assay, Construct