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Record W2560765612 · doi:10.1115/detc2016-59104

Identification of Relationships Between Electroencephalography (EEG) Bands and Design Activities

2016· article· en· W2560765612 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicDesign Education and Practice
Canadian institutionsConcordia University
Fundersnot available
KeywordsElectroencephalographyPrincipal component analysisCognitionAlpha (finance)Computer scienceAudiologyIdentification (biology)PsychologyPattern recognition (psychology)Artificial intelligenceNeuroscienceDevelopmental psychologyMedicinePsychometrics

Abstract

fetched live from OpenAlex

Electroencephalography (EEG) study of design activities has been drawing increasing attentions in design cognition research. The aim of this present paper is to identify EEG bands that are associated with design activities through principal component analysis (PCA). Based on the analysis of the data on 32 subjects collected from experiments conducted in the Design Lab at Concordia University, it was found that resting, problem solving and evaluation activities have relations to specific EEG bands. EEG powers of beta-2 (20–30Hz), gamma-1 (20–30Hz), and gamma-2 (30–40Hz) are mostly associated to the design activities whereas resting is mostly associated to alpha band (8–14Hz). In addition, there are differences in frequency above 20Hz between the resting before and after design activities. The work presented in this paper can be used to further quantify designer’s cognitive activities, which will ultimately improve the development of effective design tools and methods.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.743
Threshold uncertainty score0.160

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.035
GPT teacher head0.251
Teacher spread0.216 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Quick stats

Citations16
Published2016
Admission routes1
Has abstractyes

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