Abstract

Functional near-infrared spectroscopy (fNIRS) has become an established tool to investigate brain function and is, due to its portability and resistance to electromagnetic noise, an interesting modality for brain-machine interfaces (BMIs). BMIs have been successfully realized using the decoding of movement kinematics from intra-cortical recordings in monkey and human. Recently, it has been shown that hemodynamic brain responses as measured by fMRI are modulated by the direction of hand movements. However, quantitative data on the decoding of movement direction from hemodynamic responses is still lacking and it remains unclear whether this can be achieved with fNIRS, which records signals at a lower spatial resolution but with the advantage of being portable. Here, we recorded brain activity with fNIRS above different cortical areas while subjects performed hand movements in two different directions. We found that hemodynamic signals in contralateral sensorimotor areas vary with the direction of movements, though only weakly. Using these signals, movement direction could be inferred on a single-trial basis with an accuracy of ∼65% on average across subjects. The temporal evolution of decoding accuracy resembled that of typical hemodynamic responses observed in motor experiments. Simultaneous recordings with a head tracking system showed that head movements, at least up to some extent, do not influence the decoding of fNIRS signals. Due to the low accuracy, fNIRS is not a viable alternative for BMIs utilizing decoding of movement direction. However, due to its relative resistance to head movements, it is promising for studies investigating brain activity during motor experiments.

Highlights

  • Functional near-infrared spectroscopy has recently attracted the interest of researchers working on motor control [1,2] and brain-machine interfaces (BMIs; [3])

  • For the pilot experiment, comprising four movement directions and five subjects, we only calculated the time-resolved decoding accuracy with regularized linear discriminant analysis (RLDA) and found a maximum average accuracy of 36% (binomial test: p,0.01, 3% standard error of the mean (s.e.m.)) around movement end (ME)

  • Using RLDA to decode singletrial Functional near-infrared spectroscopy (fNIRS) signals recorded above contralateral sensorimotor areas during left- and downward movements of the right hand, we found the decoding performance to vary across subjects (Table 1 and Figure 6A)

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Summary

Introduction

Functional near-infrared spectroscopy (fNIRS) has recently attracted the interest of researchers working on motor control [1,2] and brain-machine interfaces (BMIs; [3]). Several studies have assessed the capabilities of fNIRS to investigate brain activity or as a potential control signal for BMIs [4,5,6]. We investigated the characteristics of movement related fNIRS signals recorded above motor areas. FNIRS allows to distinguish between left and right hand movements (performed or imagined), i.e. between left and right hemispheric motor activity [3,11,12]. Until now it has been unknown whether the spatial resolution and the signal-to-noise ratio of fNIRS are sufficient to investigate cortical activity related to different movements of the same limb FNIRS signals have been shown to vary between rest and motor execution or imagery [7,8] and to reflect motor task complexity [9] or force levels exerted in isometric hand/finger contractions [10]. fNIRS allows to distinguish between left and right hand movements (performed or imagined), i.e. between left and right hemispheric motor activity [3,11,12].

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