Sensitivity, Specificity and Accuracy of a Novel EEG-Based Objective Test, the Cognalyzer®, in Detecting Cannabis Psychoactive Effects
Bibliographic record
Abstract
Current standards for identifying recent cannabis use are based on body fluid testing. The Cognalyzer ® is a novel electroencephalography (EEG) measurement device and algorithm designed to objectively characterize brainwave alterations associated with cannabis. The objective of this study was to assess the accuracy, sensitivity and specificity levels of the Cognalyzer ® to characterize brainwave alterations following cannabis inhalation. Seventy-five participants, aged 19–55 years, were enrolled, and oral fluid samples were collected pre-cannabis inhalation. EEG and subjective drug effects questionnaire (DEQ) were administered pre- and post-ad libitum cannabis inhalation. Fifty participants remained in the clinic for 4 h post-inhalation. Blinded analyses of the EEG files were conducted by Zentrela Inc. using two versions (V1 and V2) of the Cognalyzer ® algorithm. Pre- vs. post-inhalation comparison status was characterized by the Cognalyzer ® and summarized for: sensitivity, specificity, accuracy, percent false positive, percent false negative and positive and negative predictive value. The null hypothesis was tested using McNemar’s test. Cognalyzer ® results pre- and post-inhalation were combined with the oral fluid tetrahydrocannabinol (THC) concentration to evaluate potential to improve current drug testing. The two versions of the Cognalyzer ® algorithm had similar diagnostic results. Diagnostic outcomes were improved when participants with missing EEG recordings or electrode placement errors were removed. The Cognalyzer ® accuracy was 85.5% and 83.9%, sensitivity was 87.1% and 88.7%, and specificity was 83.9% and 79.0% for algorithm V1 and V2, respectively. Combining Cognalyzer ® results with oral fluid concentrations reduced false-positive oral fluid test results by up to 49%. The Cognalyzer ® characterized brainwave alterations associated with cannabis inhalation with high levels of accuracy in a population of participants with varied cannabis inhalation histories, relative to the comparison standard of pre- vs. post-inhalation status. The Cognalyzer ® allows the results to be generalized to the larger population addressing a limitation in currently accepted standards.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".