A Novel Approach for the Detection of Drunken Driving using the Power Spectral Density Analysis of EEG

Malar, E.; Gauthaam, M.; Chakravarthy, D. · 2011 · Crossref

DOI: 10.5120/2525-3436

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Summary

This paper addresses the critical safety issue of drunken driving, which contributes to approximately 50% of road accidents globally. The authors argue that conventional detection methods, such as breath analyzers and pulse oximeters, are flawed due to susceptibility to cheating, low sensitivity, and slow response times. To overcome these limitations, the study proposes a novel, non-invasive system that detects alcohol intoxication by analyzing the driver’s electroencephalogram (EEG) signals in real-time, thereby preventing the vehicle from starting if intoxication is detected. The methodology involves acquiring EEG signals using a smart cap with five embedded dry electrodes placed on the forehead and behind the left ear. These signals are transmitted via Bluetooth to an intelligence unit containing a microprocessor. The system employs Stationary Wavelet Transform to decompose the raw EEG data into five frequency bands: delta (0–4 Hz), theta (4–8 Hz), alpha (8–12 Hz), beta (12–20 Hz), and gamma (20+ Hz). Power Spectral Density (PSD) analysis is then performed on these bands to quantify signal power. The experimental design compared EEG data from five normal individuals and five alcoholic individuals, processed using MATLAB to identify distinct spectral patterns associated with alcohol consumption. The results demonstrate clear differences in EEG spectral power between normal and intoxicated subjects. Specifically, the study found that alpha wave activity decreases significantly in alcoholic individuals compared to normal subjects, while theta wave activity increases. Conversely, beta wave activity was observed to enhance in intoxicated subjects, reflecting an excitable state of the cerebral cortex. These findings support the hypothesis that alcohol intake alters neuronal activity in predictable ways, particularly reducing frontal region power and increasing central/occipital region power. The visual plots of PSD for alpha, beta, and theta bands confirm these distinctions, allowing for reliable differentiation between sober and intoxicated drivers. The significance of this work lies in the development of a "Biokey" system that integrates directly with a vehicle’s ignition circuit. The intelligence unit processes the EEG data and drives a relay system that replaces the traditional key. If the algorithm detects abnormalities indicative of alcohol consumption, the relay remains open, preventing the engine from starting. This approach offers a more robust and difficult-to-cheat alternative to existing methods, potentially saving thousands of lives by preventing accidents caused by drunken or drowsy driving. The paper concludes that this biological signal-based detection is more efficient than current chemical or optical methods and represents a viable technological solution for enhancing road safety.

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• alcohol detection systems

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• Empirical Findings: physiological data