Cannabis Effect Research Driven by EEG technology and Machine Learning @medicinal.genomics
Cannabis Effect Research Driven by EEG technology and Machine Learning  @medicinal.genomics
Uploaded August 2025 | Updated September 2026, 2 weeks ago
Tags: Artificial Intelligence, Product Effects, Product Development, Cannabis Research, Product Efficacy, Cannabis Product

Israel Gasperin, Engineering Scientist at Zentrela, Inc. (@Zentrelaresearch) presents, “Cannabis Effect Research Driven by EEG technology and Machine Learning” at CannMed 2025.

Wearable EEG technology combined with AI-based analysis offer a reliable, cost-effective alternative to traditional THC percentage-based testing, enabling the objective measurement of cannabis product effects, regardless of THC content.

Results from the world’s largest multi-site EEG study with +1,000 research participations (+10K EEG scans) and in collaboration with multiple cannabis scientists across Canada, the U.S. and Europe, will be presented to further demonstrate the objectivity and effectiveness of the PEL (Psychoactive Effect Levels) metric for assessing the quality and efficacy of cannabis products, showcasing how this innovative testing method can evaluate innovative formulations with terpenes, minor cannabinoids, drug delivery systems, and extraction methods.

The application of Machine Learning to large EEG datasets recorded under specific mental states presents a significant opportunity to standardize and streamline cannabis effect research, leading to more consistent and actionable data in the industry.

Learning Objectives:

⦾ EEG scans for measuring the PEL (Psychoactive Effect Levels) of cannabis products offer a new, more reliable quality metric
⦾ Results from over 10,000 EEG scans of consumers using a wide range of cannabis products will be presented

0:00 - Introduction
1:01 - The Challenges of Current Research Methods
2:37 - The Impact of Research Gaps on the Cannabis Industry
4:42 - Why EEG is the Best Tool for Cannabis Effect Research
5:48 - The Role of AI and a Standardized EEG Database
7:26 - Introducing the Cognalyzer: An AI-Powered EEG Solution
9:16 - Independent Validation of the AI's Accuracy
11:21 - New Metrics for Measuring Cannabis Effects
12:12 - Crossover Trials: Objectively Comparing Products
13:49 - The Technology's Impact on Licensed Producers
15:05 - Case Study: The Power of a Low-THC Strain
16:40 - The Entourage Effect: Distillate vs. Live Rosin
18:05 - The Arousal Metric: Measuring Relaxation and Mental State
19:54 - Dosing and Personalization: Differences in Gender Response
21:20 - The Valence Metric: Quantifying Mood and Enjoyment
23:44 - Conclusion: The Future of Cannabis Product Labeling

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The CannMed 25 Innovation & Investment Summit showcased its first international venue, the Wyndham Grand Rio Mar Golf & Beach Resort in Puerto Rico from June 17 - 20, 2025. Since its inception at Harvard Medical School in 2016, CannMed has earned a reputation as the premier destination for cutting-edge research, emerging technologies, and investment opportunities. CannMed 25 continues this tradition with medicinal plant researchers, clinicians and venture capitalists who view this Summit as the industry’s number one showcase for the latest advances in plant-based science, medicine, cultivation, and safety.
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Cannabis Effect Research Driven by EEG technology and Machine Learning

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