Unleashing the potential of fNIRS with machine learning: classification of fine anatomical movements to empower future brain-computer interface

Abstract

In this study, we explore the potential of using functional near-infrared spectroscopy (fNIRS) signals in conjunction with modern machine-learning techniques to classify specific anatomical movements to increase the number of control commands for a possible fNIRS-based brain-computer interface (BCI) applications. The study focuses on novel individual finger-tapping, a well-known task in fNIRS and fMRI studies, but limited to left/right or few fingers. Twenty-four right-handed participants performed the individual finger-tapping task. Data were recorded by using sixteen sources and detectors placed over the motor cortex according to the 10-10 international system. The event's average oxygenated Δ HbO and deoxygenated Δ HbR hemoglobin data were utilized as features to assess the performance of diverse machine learning (ML) models in a challenging multi-class classification setting. These methods include LDA, QDA, MNLR, XGBoost, and RF. A new DL-based model named "Hemo-Net" has been proposed which consists of multiple parallel convolution layers with different filters to extract the features. This paper aims to explore the efficacy of using fNRIS along with ML/DL methods in a multi-class classification task. Complex models like RF, XGBoost, and Hemo-Net produce relatively higher test set accuracy when compared to LDA, MNLR, and QDA. Hemo-Net has depicted a superior performance achieving the highest test set accuracy of 76%, however, in this work, we do not aim at improving the accuracies of models rather we are interested in exploring if fNIRS has the neural signatures to help modern ML/DL methods in multi-class classification which can lead to applications like brain-computer interfaces. Multi-class classification of fine anatomical movements, such as individual finger movements, is difficult to classify with fNIRS data. Traditional ML models like MNLR and LDA show inferior performance compared to the ensemble-based methods of RF and XGBoost. DL-based method Hemo-Net outperforms all methods evaluated in this study and demonstrates a promising future for fNIRS-based BCI applications.

Keywords: brain computer-interface (BCI); classification; deep learning; functional near-infrared spectroscopy (fNIRS); individual finger movements.

Figure 1

(A) Experimental setup demonstrating fNIRS NIRScout (NIRx Medical Technology GmbH, Germany) (B) Sixteen sources and detectors each were placed over the motor cortex according to the 10 − 10 international system.

Figure 2

Experimental paradigm: a single run of the experiment includes three repetitive sessions of each finger-tapping.

Figure 3

Data processing steps followed before application of DL model.

Figure 4

Schematic illustrations of Inception time model adaptation for Hemo-Net (left) and a single Inception block (right).

Figure 5

Confusion matrix C1-C5 represent respective class as shown in Table 1.