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A semi-supervised inattention detection method using biological signal

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  • Yerim Choi

    (Kyonggi University)

  • Jonghun Park

    (Seoul National University)

  • Dongmin Shin

    (Hanyang University)

Abstract

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.

Suggested Citation

  • Yerim Choi & Jonghun Park & Dongmin Shin, 2017. "A semi-supervised inattention detection method using biological signal," Annals of Operations Research, Springer, vol. 258(1), pages 59-78, November.
  • Handle: RePEc:spr:annopr:v:258:y:2017:i:1:d:10.1007_s10479-017-2406-6
    DOI: 10.1007/s10479-017-2406-6
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    References listed on IDEAS

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    1. Wanpracha Chaovalitwongse & Oleg Prokopyev & Panos Pardalos, 2006. "Electroencephalogram (EEG) time series classification: Applications in epilepsy," Annals of Operations Research, Springer, vol. 148(1), pages 227-250, November.
    2. Sangyeol Lee & Jeongcheol Ha & Okyoung Na & Seongryong Na, 2003. "The Cusum Test for Parameter Change in Time Series Models," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 30(4), pages 781-796, December.
    3. Venkatesh Balasubramanian & K. Adalarasu & A. Gupta, 2011. "EEG based analysis of cognitive fatigue during simulated driving," International Journal of Industrial and Systems Engineering, Inderscience Enterprises Ltd, vol. 7(2), pages 135-149.
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    Cited by:

    1. Jerzy Grobelny & Rafal Michalski & Gerhard-Wilhelm Weber, 2021. "Modeling human thinking about similarities by neuromatrices in the perspective of fuzzy logic," WORking papers in Management Science (WORMS) WORMS/21/09, Department of Operations Research and Business Intelligence, Wroclaw University of Science and Technology.
    2. Lucia Reis Peixoto Roselli & Leydiana de Sousa Pereira & Anderson Lucas Carneiro de Lima Silva & Adiel Teixeira Almeida & Danielle Costa Morais & Ana Paula Cabral Seixas Costa, 2020. "Neuroscience experiment applied to investigate decision-maker behavior in the tradeoff elicitation procedure," Annals of Operations Research, Springer, vol. 289(1), pages 67-84, June.

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