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  • Open Access


    Measuring Mental Workload Using ERPs Based on FIR, ICA, and MARA

    Yu Sun1, Yi Ding2,*, Junyi Jiang3, Vincent G. Duffy4

    Computer Systems Science and Engineering, Vol.41, No.2, pp. 781-794, 2022, DOI:10.32604/csse.2022.016387

    Abstract Mental workload is considered to be strongly linked to human performance, and the ability to measure it accurately is key for balancing human health and work. In this study, brain signals were elicited by mental arithmetic tasks of varying difficulty to stimulate different levels of mental workload. In addition, a finite impulse response (FIR) filter, independent component analysis (ICA), and multiple artifact rejection algorithms (MARAs) were used to filter event-related potentials (ERPs). Then, the data consisting of ERPs, subjective ratings of mental workload, and task performance, were analyzed through the use of variance and Spearman’s… More >

  • Open Access


    Classification of Epileptic Electroencephalograms Using Time-Frequency and Back Propagation Methods

    Sengul Bayrak1,2,*, Eylem Yucel2, Hidayet Takci3, Ruya Samli2

    CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 1427-1446, 2021, DOI:10.32604/cmc.2021.015524

    Abstract Today, electroencephalography is used to measure brain activity by creating signals that are viewed on a monitor. These signals are frequently used to obtain information about brain neurons and may detect disorders that affect the brain, such as epilepsy. Electroencephalogram (EEG) signals are however prone to artefacts. These artefacts must be removed to obtain accurate and meaningful signals. Currently, computer-aided systems have been used for this purpose. These systems provide high computing power, problem-specific development, and other advantages. In this study, a new clinical decision support system was developed for individuals to detect epileptic seizures… More >

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