Classification of motor imagery and execution signals with population-level feature sets: implications for probe design in fNIRS based BCI

Abstract

Objective. The aim of this study was to introduce a novel methodology for classification of brain hemodynamic responses collected via functional near infrared spectroscopy (fNIRS) during rest, motor imagery (MI) and motor execution (ME) tasks which involves generating population-level training sets. Approach. A 48-channel fNIRS system was utilized to obtain hemodynamic signals from the frontal (FC), primary motor (PMC) and somatosensory cortex (SMC) of ten subjects during an experimental paradigm consisting of MI and ME of various right hand movements. Classification accuracies of random forest (RF), support vector machines (SVM), and artificial neural networks (ANN) were computed at the single subject level by training each classifier with subject specific features, and at the group level by training with features from all subjects for ME versus Rest, MI versus Rest and MI versus ME conditions. The performances were also computed for channel data restricted to FC, PMC and SMC regions separately to determine optimal probe location. Main results. RF, SVM and ANN had comparably high classification accuracies for ME versus Rest (%94, %96 and %98 respectively) and for MI versus Rest (%95, %95 and %98 respectively) when fed with group level feature sets. The accuracy performance of each algorithm in localized brain regions were comparable (>%93) to the accuracy performance obtained with whole brain channels (>%94) for both ME versus Rest and MI versus Rest conditions. Significance. By demonstrating the feasibility of generating a population level training set with a high classification performance for three different classification algorithms, the findings pave the path for removing the necessity to acquire subject specific training data and hold promise for a novel, real-time fNIRS based BCI system design which will be most effective for application to disease populations for whom obtaining data to train a classification algorithm is not possible.

An Augmented-Reality fNIRS-Based Brain-Computer Interface: A Proof-of-Concept Study

“…fNIRS Data Acquisition fNIRS data was recorded using a continuous-wave system (NIRScout-816, NIRx, Medizintechnik GmbH, Berlin, Germany). The optode setup consisted of nine sources and eight detectors which were placed on the left hemisphere that cover areas commonly associated with motor imagery, i.e., premotor cortex and part of the supplementary motor area, primary motor cortex, somatosensory motor cortex and part of the parietal cortex following the extended 10/10 EEG system (see Figure 3; Sorger et al, 2012;Abdalmalak et al, 2016;Batula et al, 2017;Klein and Kranczioch, 2019;Erdogan et al, 2019). An in-house SDC was created by placing source S9 as close as the optodes would allow (∼13 mm away) to detector D5 on the same sagittal plane that connects D5 and source S6 (see Figure 3).…”

The Potential Role of fNIRS in Evaluating Levels of Consciousness

“…The former, in particular, has been widely adopted since, unlike motor execution, motor imagery does not require intact thalamocortical tracts (Fernández-Espejo et al, 2015 ), making it preferable for patients with severe physical impairment. In previous fNIRS studies, classification accuracies in healthy controls have ranged from 63% to 98% (Coyle et al, 2004 , 2007 ; Sitaram et al, 2007 ; Erdoĝan et al, 2019 ). The discrepancy between studies is likely due to multiple factors.…”

“…This adds to the complexity of assessing residual brain function and raises an important question of whether the absence of brain function could result from detection insensitivity or fluctuations in awareness, rather than reflecting the absence of residual awareness. In addition to hardware improvements, more sophisticated machine learning approaches such as artificial neural networks (ANN) could help improve the sensitivity of fNIRS (Erdoĝan et al, 2019 ). Considering that fNIRS is portable, it has the advantage of testing at different times of the day and on multiple days to better capture a patient’s full state of awareness.…”

Recognition of Flexion and Extension Imagery Involving the Right and Left Arms Based on Deep Belief Network and Functional Near-Infrared Spectroscopy

“…In addition, it is highly portable and suitable for monitoring dynamic changes of oxygenation and deoxygenated hemoglobin concentration in brain tissue during movement and imagining; it has also been applied successfully in many fields [14][15][16][17][18][19][20][21][22][23]. Studies have shown that MI-BCI based on fNIRS (fNIRS-MI-BCI) is feasible and has several potential applications [24][25][26].…”