On the Reliability of Frequency-Domain Features for fNIRS BCIs in the Presence of Pain

In this paper, we study the effects of the presence of pain on the classification accuracy of mental arithmetic tasks in functional near infrared spectroscopy (fNIRS)-based brain computer interfaces (BCIs). fNIRS recordings from prefrontal and motor cortices are obtained during the execution of two mental arithmetic tasks in the presence and absence of external pain stimuli. Various frequency-domain parameters of the fNIRS signals, under pain-free and pain conditions, are extracted for each task and used as features. A support vector machine with a quadratic kernel (QSVM) is used as the classifier. Four scenarios for training and testing the classifier are considered: (1) train and test using pain-free data, (2) train and test using under-pain data, (3) train using pain-free data and test using under-pain data, and (4) train using under-pain data and test using pain-free data. Results show that the classification accuracy of the model trained on pain-free data is significantly reduced when the model is tested on data obtained in the presence of pain. Similarly, the accuracy drops when the model is trained on data obtained in the presence of pain but tested on pain-free data. These results highlight the importance of considering pain-induced changes in cortical activity when developing BCIs for patients in need of them.