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Record W2922280747 · doi:10.31117/neuroscirn.v2i1.21

Commercial wireless versus standard stationary EEG systems for personalized emotional brain-computer interfaces: a preliminary reliability check

2019· article· en· W2922280747 on OpenAlexafffund
Derrick Matthew Buchanan, Jeremy Grant, Amedeo D’Angiulli

Bibliographic record

VenueNeuroscience Research Notes · 2019
Typearticle
Languageen
FieldNeuroscience
TopicEEG and Brain-Computer Interfaces
Canadian institutionsCarleton University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsElectroencephalographyArousalValence (chemistry)Reliability (semiconductor)Computer scienceEmotional valenceWirelessHuman–computer interactionPsychologyCognitionNeuroscienceTelecommunications

Abstract

fetched live from OpenAlex

We present a preliminary data-based assessment of measurement reliability of the commercial 14-electrode Emotiv EPOCTM EEG wireless system in distinguishing between electrophysiological states of emotional function, as compared to a standard research-lab stationary 32-electrode EEG system for personalized single-individual use. Individual observers completed two tasks designed to elicit neural changes in emotional arousal and valence while simultaneously recording their EEGs with both systems in separate sessions. Participants observed emotion-laden words from the ANEW database and images from the IAPS database, both widely used and validated databases for emotional processes in multidisciplinary research. The pattern of results distinguished between high and low arousal and valence states using the stationary traditional system, but not the commercial device. Also, the latter device recorded EEG band frequencies at a much lower resolution and frequency range than the standard system. These findings suggest poor validity when using the commercial device and therefore should be cautioned against in a research setting.

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.

How this classification was reachedexpand

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)0.003
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.715
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0000.001
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 head0.143
GPT teacher head0.404
Teacher spread0.261 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations6
Published2019
Admission routes2
Has abstractyes

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