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Comparison of Machine Learning Approaches for Motor Imagery Based Optical Brain Computer Interface

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Advances in Neuroergonomics and Cognitive Engineering (AHFE 2018)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 775))

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Abstract

A Brain-computer Interface (BCI) is a system that interprets specific patterns in human brain activity, such as the intention to perform motor functions, in order to generate a signal which can be used for communication or control. Functional near infrared spectroscopy (fNIRS) is an emerging optical neuroimaging technique which is a relatively new modality for BCI systems. As such, the optimal paradigms and classification techniques for the interpretation of fNIRS-BCI systems is an area of active investigation. Presently, most fNIRS BCIs have adopted Linear Discriminant Analysis (LDA) algorithm as the primary classification approach, however other alternative methods may offer increased performance. In order to compare different algorithms, a dataset from a four-class motor imagery-based fNIRS-BCI study was re-analyzed, and we systematically compared the performance of different machine learning algorithms: Naïve Bayes (NB), LDA, Logistic Regression (LR), Support Vector Machines (SVM) and Multi-layer Perception (MLP). Our findings suggest that the LR classifier slightly outperformed other classifiers, unlike most fNIRS-BCI studies which reported LDA or SVM as the best classifier. The results presented here suggest that an LR classifier could be a potential replacement for LDA classifiers in motor imagery tasks.

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Author information

Authors and Affiliations

  1. School of Biomedical Engineering, Science & Health Systems, Drexel University, Philadelphia, PA, USA

    Lei Wang, Adrian Curtin & Hasan Ayaz

  2. Cognitive Neuroengineering and Quantitative Experimental Research (CONQUER) Collaborative, Drexel University, Philadelphia, PA, USA

    Lei Wang, Adrian Curtin & Hasan Ayaz

  3. School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China

    Adrian Curtin

  4. Department of Family Medicine and Community Health, University of Pennsylvania, Philadelphia, PA, USA

    Hasan Ayaz

  5. The Division of General Pediatrics, Children’s Hospital of Philadelphia, Philadelphia, PA, USA

    Hasan Ayaz

Authors
  1. Lei Wang
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  2. Adrian Curtin
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  3. Hasan Ayaz
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Correspondence to Hasan Ayaz .

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Editors and Affiliations

  1. Drexel University, Philadelphia, Pennsylvania, USA

    Hasan Ayaz

  2. University of North Carolina-Chapel Hill, Chapel Hill, North Carolina, USA

    Lukasz Mazur

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Wang, L., Curtin, A., Ayaz, H. (2019). Comparison of Machine Learning Approaches for Motor Imagery Based Optical Brain Computer Interface. In: Ayaz, H., Mazur, L. (eds) Advances in Neuroergonomics and Cognitive Engineering. AHFE 2018. Advances in Intelligent Systems and Computing, vol 775. Springer, Cham. https://doi.org/10.1007/978-3-319-94866-9_12

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