AI Article Synopsis
- Current passive brain-computer interfaces (pBCI) provide real-time assessments of cognitive states using EEG measurements, focusing on workload level through band power estimates.
- Mental fatigue, resulting from prolonged task engagement, influences EEG band power features but its effects on workload assessment have not been thoroughly studied.
- This research investigates the interaction between workload (WKL), time-on-task (TOT), and their effects on EEG features and classification performance, revealing that increased TOT decreases the ability to differentiate workload levels and alters alpha power distribution.
Article Abstract
Current mental state monitoring systems, a.k.a. passive brain-computer interfaces (pBCI), allow one to perform a real-time assessment of an operator's cognitive state. In EEG-based systems, typical measurements for workload level assessment are band power estimates in several frequency bands. Mental fatigue, arising from growing time-on-task (TOT), can significantly affect the distribution of these band power features. However, the impact of mental fatigue on workload (WKL) assessment has not yet been evaluated. With this paper we intend to help fill in this lack of knowledge by analyzing the influence of WKL and TOT on EEG band power features, as well as their interaction and its impact on classification performance. Twenty participants underwent an experiment that modulated both their WKL (low/high) and time spent on the task (short/long). Statistical analyses were performed on the EEG signals, behavioral and subjective data. They revealed opposite changes in alpha power distribution between WKL and TOT conditions, as well as a decrease in WKL level discriminability with increasing TOT in both number of statistical differences in band power and classification performance. Implications for pBCI systems and experimental protocol design are discussed.
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| http://dx.doi.org/10.1109/EMBC.2013.6611070 | DOI Listing |
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