EEG Signal Analysis for Monitoring Concentration of Operators

Rykała, Łukasz · 2023 · Crossref

DOI: 10.14313/jamris/1-2023/4

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Summary

This paper addresses the challenge of monitoring the concentration levels of machine operators, particularly those controlling unmanned ground vehicles (UGVs) or heavy machinery, who often work under stressful and unfavorable conditions. The primary research goal was to develop an algorithm capable of determining the state of brain activity by analyzing electroencephalography (EEG) signals. The motivation stems from the critical need to maintain high operator concentration to prevent errors that could lead to significant damage or mission failure. The study aims to provide a technical foundation for using EEG-based biofeedback in non-medical, industrial settings. The methodology involved a comprehensive review of EEG signal acquisition and processing techniques, including electrode types, placement standards (International 10–20 system), and equipment specifications. The authors utilized a 32-channel ALIEN EEG recorder and TruScan software to acquire signals, employing Ag/AgCl contact electrodes mounted in a cap. A significant portion of the work focused on identifying and mitigating artifacts, such as muscle potentials, eye movements, and 50 Hz electrical interference. The core contribution is an algorithm implemented in the LabVIEW environment. This algorithm processes raw EEG data by applying band-pass filters to isolate specific frequency bands (Delta, Theta, Alpha, SMR, Beta, and Gamma). It then calculates the Root Mean Square (RMS) values, amplitudes, and Fourier transform percentages for each band to assess brain activity states. The results demonstrate the algorithm's ability to accurately interpret EEG signals from an adult male subject. The study recorded and analyzed signals during states of relaxation, hand movement, blinking, and concentration (solving a crossword puzzle). The analysis revealed that during concentration, the signal exhibited dominant low-frequency components, with Delta waves comprising approximately 33% and Theta waves 14% of the spectrum, partly attributed to hardware delays. Crucially, the calculated Theta/Beta ratio was 1.2, falling within the optimal range for adults, indicating no concentration disorders. The system successfully distinguished between physiological brain waves and artifacts, such as the 50 Hz network interference and muscle-induced noise, validating the algorithm's robustness in real-time signal interpretation. The significance of this work lies in its provision of a practical, implementable tool for monitoring operator mental states. By establishing a clear link between specific EEG frequency bands and cognitive states like concentration, the study supports the development of biofeedback systems for industrial applications. The findings suggest that EEG analysis can effectively detect deviations from normal concentration levels, offering a potential method to enhance safety and performance in high-stakes operational environments. The detailed documentation of artifacts and signal processing steps provides a valuable reference for future research in non-invasive brain-computer interfaces and operator monitoring systems.

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• neuro workload indices

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• Empirical Findings: physiological data