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Record W2378279093

A Multi-channel Phase-space Reconstruction Method and its Application to EEG

2005· article· en· W2378279093 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
FieldComputer Science
TopicAdvanced Decision-Making Techniques
Canadian institutionsCAE (Canada)
Fundersnot available
KeywordsCorrelation dimensionChannel (broadcasting)Noise (video)EmbeddingDimension (graph theory)SIGNAL (programming language)ElectroencephalographyComputer sciencePhase spaceCorrelationAlgorithmPattern recognition (psychology)Artificial intelligenceMathematicsFractal dimensionPsychologyTelecommunicationsPhysicsFractalImage (mathematics)
DOInot available

Abstract

fetched live from OpenAlex

A multi-channel phase-space reconstruction method was proposed in the paper based on rearranging signal serials by the correlation coefficients and selecting time delay by signal determinism. The study of determinism and calculation of correlation dimension on simulative data indicated the recoustraotion method exhibit a good performance to the noise disturbance, the preferences of time delay and embedding dimension,the analysis results were stable and reliable.The nonlinear analysis of mental EEG gave the relationship between EEG signals and mental complexity. This method can be useful for analyzing multi-channel signals with noise,short time series and real-time EEG on-line.

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: Other design · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.953
Threshold uncertainty score0.393

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 communication0.0000.001
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 head0.032
GPT teacher head0.377
Teacher spread0.345 · 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

Citations0
Published2005
Admission routes1
Has abstractyes

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