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Investigation of the Performance of fNIRS-based BCIs for Assistive Systems in the Presence of Acute Pain

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Signal Processing in Medicine and Biology

Abstract

A brain-computer interface (BCI) enables the control of a peripheral device using signals acquired from the brain. A key application of BCIs is in assistive systems for patients with motor and communication disabilities. To be able to control an assistive device using the patient’s thoughts, the BCI first needs to be trained to respond to the patient’s cortical signals. Pain is a condition prevalent in patients with motor and communication disabilities. Pain is expected to impact and modify cortical signals. This means that if the patient does not experience any pain while training the BCI for a certain task, but experiences pain later, while using the BCI for the same task, the performance of the BCI could be negatively impacted due to the potential influence of pain on the cortical activity related to the task. As a result, the assistive device connected to the BCI may fail to function as intended. This work aims to study the impact of the presence of acute pain on the performance of a functional near-infrared spectroscopy (fNIRS)-based BCI. The performance of the BCI is measured by the classification accuracy of the BCI in response to two mental arithmetic tasks, under 4 scenarios: (1) the BCI is trained and tested on pain-free data; (2) the BCI is trained on pain-free data and tested on data obtained in the presence of pain; (3) the BCI is trained and tested on data obtained in the presence of pain; and (4) the BCI is trained on data obtained in the presence of pain and tested on pain-free data. Classification is performed using two types of algorithms – a support vector machine (SVM), which is a classical supervised learning algorithm to which hand-crafted features from the frequency-domain representation of the cortical fNIRS signals are fed as input, and a convolutional neural network (CNN), that automatically learns features from the input data. These two classifiers were chosen to study the effects of the features used (manual vs autogenerated) and the complexity of the classifier on the BCI’s performance in the presence of pain. It is observed that the classification accuracy of the BCI is high for scenarios 1 and 3 irrespective of the classifier used, as not much difference in the cortical activity is expected between the training and application stages. However, the accuracy drops to the chance level in scenarios 2 and 4 for both classifiers. Our results indicate that the presence of pain could have a significant impact on the performance of the fNIRS-based BCIs, suggesting that it is critical to consider the effects of the presence of pain when using BCIs in assistive systems for patients.

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Acknowledgments

This material is based upon work supported by the National Science Foundation (NSF) under Grant No. 1841087. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering, Rutgers University, Piscataway, NJ, USA

    Ashwini Subramanian & Laleh Najafizadeh

  2. Department of Psychology, Northeastern University, Boston, MA, USA

    Foroogh Shamsi

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  1. Ashwini Subramanian
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  2. Foroogh Shamsi
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  3. Laleh Najafizadeh
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Correspondence to Laleh Najafizadeh .

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  1. ECE, Temple University, Philadelphia, PA, USA

    Iyad Obeid

  2. ECE, Temple University, Philadelphia, PA, USA

    Joseph Picone

  3. Tandon School of Engineering, New York University, Brooklyn, NY, USA

    Ivan Selesnick

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Subramanian, A., Shamsi, F., Najafizadeh, L. (2023). Investigation of the Performance of fNIRS-based BCIs for Assistive Systems in the Presence of Acute Pain. In: Obeid, I., Picone, J., Selesnick, I. (eds) Signal Processing in Medicine and Biology. Springer, Cham. https://doi.org/10.1007/978-3-031-21236-9_3

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