access icon free Classification of working memory loads using hybrid EEG and fNIRS in machine learning paradigm

Single modality brain–computer interface (BCI) systems often mislabel the electroencephalography (EEG) signs as a command, even though the participant is not executing some task. In this Letter, the classification of different working memory load levels is presented using a hybrid BCI system. N-back cognitive tasks such as 0-back, 2-back, and 3-back are used to create working memory load on participants while recording EEG and functional near-infrared spectroscopy (fNIRS) signals simultaneously. A combination of statistically significant features obtained from EEG and fNIRS corresponding to frontal region channels are used to classify different N-back commands. Kernel-based support vector machine (SVM) classifiers are employed with and without cross-validation schemes. Classification accuracy of 100% is achieved for binary classification of 0-back against 2-back and 0-back against 3-back using linear SVM, quadratic SVM, and cubic SVM under holdout data division protocol.

Inspec keywords: statistical analysis; signal classification; medical signal processing; learning (artificial intelligence); support vector machines; brain-computer interfaces; infrared spectra; neurophysiology; electroencephalography; cognition

Other keywords: single modality brain-computer interface; cross-validation schemes; functional near-infrared spectroscopy signals; fNIRS; kernel-based support vector machine classifiers; quadratic SVM; working memory load classification; binary classification; statistically significant features; electroencephalography; N-back commands; cubic SVM; linear SVM; holdout data division protocol; classification accuracy; hybrid BCI system; hybrid EEG; working memory load levels; frontal region channels; N-back cognitive tasks; machine learning paradigm

Subjects: Probability theory, stochastic processes, and statistics; Electrodiagnostics and other electrical measurement techniques; Knowledge engineering techniques; Other topics in statistics; Optical and laser radiation (medical uses); Optical and laser radiation (biomedical imaging/measurement); Patient diagnostic methods and instrumentation; Biology and medical computing; Digital signal processing; User interfaces; Electrical activity in neurophysiological processes; Signal processing and detection; Bioelectric signals; Other topics in statistics

References

    1. 1)
    2. 2)
    3. 3)
      • 2. Cîmpanu, C., Ungureanu, F., Manta, V.I., et al: ‘A comparative study on classification of working memory tasks using EEG signals’. 2017 21st Int. Conf. on Control Systems and Computer Science (CSCS), Bucharest, Romania, May 2017, pp. 245251.
    4. 4)
    5. 5)
    6. 6)
    7. 7)
    8. 8)
    9. 9)
    10. 10)
      • 10. Shin, J., Von Lühmann, A., Kim, D.W., et al: ‘Simultaneous acquisition of EEG and NIRS during cognitive tasks for an open access dataset’, Sci. Data, 2018, 5, p. 180003. Available at https://doi.org/10.1038/sdata.2018.3.
    11. 11)
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