Lower limb Movements' Classifications using Hemodynamic Response:fNIRS Study

Functional near-infrared spectroscopy (fNIRS) has become a viable approach for brain function investigation and is an interesting modality for brain-machine interfaces (BMIs) due to its portability and resistance to electromagnetic noise. In this work, a hemodynamic response based on fNIRS signals was utilized to classify the right and left ankle joint movements. To achieve this objective, 32 optodes (emitters and detectors) were used to measure the hemodynamic responses in the motor cortex area during the motor execution task of the ankle joint movements. Two-channel sets were formed one including the channels directly related to the movement task, and another including all of the proposed channels. The results of this study reveal that the scheme based only on the selected channels outperformed the scheme that uses all channels. The classification accuracies were 91.38 % and 89.86 % respectively. These results demonstrated that fNIRS signal classification can be enhanced by eliminating the redundant channels.

I. Introduction

Brain-Machine Interface (BMI) is designed to facilitate interaction between human cognitive processes and the machine, with the goal of supporting disabled patients with limited motor control due to illness or injury, but whose mental functions are not seriously affected [1]–[3]. Interpreting the neuronal signals from the brain to the peripheral devices can be achieved using invasive techniques - by inserting microelectrodes into the brain. However, noninvasive procedures are needed in order to prevent the inherent medical risks of microelectrode insertion. Electroencephalography (EEG) [4], [5] magnetoencephalography (MEG) [6], [7], functional magnetic resonance imaging (fMRI) [8], [9], and functional near-infrared spectroscopy (fNIRS) are the main non-invasive modalities used for BMI [10], [11]. Each brain imaging modality has its own special strengths and drawbacks. Many factors contribute to the selection of a BMI system, including the cost of the device, its weight, portability, as well as the temporal and spatial resolution required for a certain task or application.