Recently, operations research methods have been utilized for biological data analysis as a huge amount of biological data becomes available. One of popular applications of the data analysis is inattention detection of...
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Recently, operations research methods have been utilized for biological data analysis as a huge amount of biological data becomes available. One of popular applications of the data analysis is inattention detection of operators in human-machine interaction systems using electroencephalography (EEG) signal. Most of the previous studies on the inattention detection employed supervised learning approaches, but their results have potential bias since they rely on imperfect assumptions for the acquisition of mental state labels, attention and inattention, due to the absence of the standardized measure for the mental states. Instead, we consider unsupervised learning approach, where no labeled data is required. In order to address the low performance of unsupervised learning approaches, attention duration for which an operator sustains his/her attention from the beginning of performing a task and relevance levels between four attributes of EEG signal and mental states are exploited. In this regard, we propose a semi-supervised inattention detection method (SID), in which attention duration and attributes-weights of EEG signal are respectively utilized as a small portion of labeled data for semi-supervised learning and weights for similarity calculation. Specifically, cumulative sum algorithm is used for the determination of the attention duration, and constrained attributes-weighting clustering algorithm is used for the estimation of attributes-weights. From experiments using real-world dataset, SID outperformed the compared methods, and it is expected that the adoption of SID will contribute to the enhancement of the operators' safety.
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