Assessing an 8-Channel Electroencephalogram’s Ability to Evaluate Cue Reactivity in Nicotine Users
Bibliographic record
Abstract
With the increasing popularity of cigarettes, e-cigarettes, and nicotine pouches among youth, novel cessation approaches are needed. Cue-induced cravings occur in response to stimuli that encourage smoking behaviour, like nicotine product promotional material or in media. Such cravings can promote addiction and lead to relapse but responses to such cues are often neglected in nicotine cessation approaches. Cue-reactivity models provide insight into addiction by examining neural responses to stimuli related drugs like nicotine. P300, an event-related potential, can be used as a measure of cue-reactivity, as it correlates with cognitive engagement and craving intensity. Bu et al.’s (2021) study demonstrated the feasibility of a P300-based neurofeedback system to regulate cue-reactivity using a 64-channel EEG. However, these setups are costly and impractical for widespread clinical and commercial applications. Thus, this literature review was conducted to evaluate current literature, looking at an assessment of the ability of an 8-channel portable EEG to measure cue-reactivity and explore its applicability in nicotine cessation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".