Caffeine on the Mind: EEG and Cardiovascular Signatures of Cortical Arousal Revealed by Wearable Sensors and Machine Learning – A Pilot Study on a Male Group

Shabbir Chowdhury¹Ahmed Alanazi²Eyad Attar^2*

• ¹RMIT University, Melbourne, Victoria, Australia
• ²King Abdulaziz University, Jeddah, Saudi Arabia

Abstract Caffeine is the most widely consumed psychoactive substance, and its stimulant properties are well documented, but few investigations have examined its acute effects on brain and cardiovascular responses during cognitively demanding tasks under ecologically valid conditions. Method: This study used wearable biosensors and machine learning analysis to evaluate the effects of moderate caffeine (162 mg) on heart rate variability (HRV), entropy, pulse transit time (PTT), blood pressure, and EEG activity. Twelve healthy male participants (20–30 years) completed a within-subjects protocol with pre-caffeine and post-caffeine sessions. EEG was recorded from seven central electrodes (C3, Cz, C4, CP1, CP2, CP5, CP6) using the EMOTIV EPOC Flex system, and heart rate (HR) and blood pressure (BP) were continuously monitored via the Huawei Watch D. Data analysis included power spectral density (PSD) estimation, FOOOF decomposition, and unsupervised k-means clustering. Results: Paired-sample t-tests assessed physiological and EEG changes. Although systolic and diastolic BP showed a non-significant upward trend, HR decreased significantly after caffeine intake (77 ± 5.3 bpm to 72 ± 2.5 bpm, p = 0.027). There was a significant increase in absolute alpha power suppression (from −5.1 ± 0.8 dB to −6.9 ± 0.9 dB, p = 0.04) and beta power enhancement (−4.7 ± 1.2 dB to –2.3 ± 1/1, p = 0.04). The surface data from FOOOF shows these are real oscillatory changes. Based on the changes in clustering prior and post-caffeine, a machine-learning change in the brain activity differentiated pre/post-caffeine states with unsupervised clustering. The study results show that moderate caffeine resulted in synchronized EEG and cardiovascular changes, indicating increased arousal and cortical activation that are detectable with wearable biosensors and classifiable with machine learning. Conclusion: A fully integrated, non-invasive methodology based on a wearable device for real-time monitoring of cognitive states holds promise in the context of digital health, fatigue detection, and public health awareness efforts.