Cognitive Workload Detection via Binary Chimp Optimization Algorithm and Machine Learning

被引:0
作者
Sarihaddu, Ch. Kantharao [1 ]
Raaza, Arun [1 ]
Akre, Sandesh [2 ]
机构
[1] Vels Inst Sci Technol & Adv Studies, Dept Elect & Commun Engn, Chennai 600117, Tamil Nadu, India
[2] MET Inst Management, Mumbai 400050, Maharashtra, India
关键词
Cognitive workload; EEG; Feature extraction; Binary chimp optimization algorithm; Machine learning; EMOTION RECOGNITION; FEATURE-SELECTION; STRESS;
D O I
10.1007/s13369-025-10034-y
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Detecting cognitive workload during mental tasks is essential for understanding neural activity responses. Electroencephalograms (EEG) are effective tools for this purpose, particularly in mental arithmetic tasks (MAT). This study utilizes EEG data from public databases, focusing on short-term EEG signals to evaluate cognitive workload. The approach employs circulant singular spectrum analysis (Ci-SSA) to decompose EEG signals into intrinsic mode functions (IMFs), followed by entropy-based feature extraction from these IMFs. Feature selection is performed using the binary chimp optimization algorithm (BCOA), and classification is conducted using supervised machine learning algorithms. The proposed method achieves performance metrics, including an accuracy (AR%) of 96.37, sensitivity (SN%) of 97, precision (PR%) of 96, specificity (SE%) of 96, and F1-Score (F1-S%) of 97. The combination of Ci-SSA for signal decomposition and BCOA for feature selection represents a novel approach to cognitive workload detection. The (Ci-SSA+BCOA+KNN) framework demonstrates high classification accuracy for MAT datasets, offering enhanced precision in cognitive workload detection compared to existing techniques.
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页数:12
相关论文
共 51 条
  • [1] Using Electroencephalography to Measure Cognitive Load
    Antonenko, Pavlo
    Paas, Fred
    Grabner, Roland
    van Gog, Tamara
    [J]. EDUCATIONAL PSYCHOLOGY REVIEW, 2010, 22 (04) : 425 - 438
  • [2] Improving BCI-based emotion recognition by combining EEG feature selection and kernel classifiers
    Atkinson, John
    Campos, Daniel
    [J]. EXPERT SYSTEMS WITH APPLICATIONS, 2016, 47 : 35 - 41
  • [3] Feedback Artificial Shuffled Shepherd Optimization-Based Deep Maxout Network for Human Emotion Recognition Using EEG Signals
    Bhanumathi, K. S.
    Jayadevappa, D.
    Tunga, Satish
    [J]. INTERNATIONAL JOURNAL OF TELEMEDICINE AND APPLICATIONS, 2022, 2022
  • [4] Chaitanya MK., 2023, Biomed. Eng, DOI [10.1080/10255842.2023.2270101, DOI 10.1080/10255842.2023.2270101]
  • [5] Driver stress recognition for smart transportation: Applying multiobjective genetic algorithm for improving fuzzy c-means clustering with reduced time and model complexity
    Chui, Kwok Tai
    [J]. SUSTAINABLE COMPUTING-INFORMATICS & SYSTEMS, 2022, 35
  • [6] Fatimah Binish, 2020, 2020 Proceedings of the International Conference on Communication and Signal Processing (ICCSP), P0046, DOI 10.1109/ICCSP48568.2020.9182149
  • [7] Fatimah B., 2020, 11 INT C COMP COMM N, P1
  • [8] Symbolic Analysis of Brain Dynamics Detects Negative Stress
    Garcia-Martinez, Beatriz
    Martinez-Rodrigo, Arturo
    Zangroniz, Roberto
    Pastor, Jose Manuel
    Alcaraz, Raul
    [J]. ENTROPY, 2017, 19 (05)
  • [9] Application of Entropy-Based Metrics to Identify Emotional Distress from Electroencephalographic Recordings
    Garcia-Martinez, Beatriz
    Martinez-Rodrigo, Arturo
    Zangroniz Cantabrana, Roberto
    Pastor Garcia, Jose Manuel
    Alcaraz, Raul
    [J]. ENTROPY, 2016, 18 (06)
  • [10] Cross-Subject Emotion Recognition Using Flexible Analytic Wavelet Transform From EEG Signals
    Gupta, Vipin
    Chopda, Mayur Dahyabhai
    Pachori, Ram Bilas
    [J]. IEEE SENSORS JOURNAL, 2019, 19 (06) : 2266 - 2274