Hemodynamic Response Detection Using Integrated EEG-fNIRS-VPA for BCI

Abstract

For BCI systems, it is important to have an accurate and less complex architecture to control a device with enhanced accuracy. In this paper, a novel methodology for more accurate detection of the hemodynamic response has been developed using a multimodal brain-computer interface (BCI). An integrated classifier has been developed for achieving better classification accuracy using two modalities. An integrated EEG-fNIRS-based vector-phase analysis (VPA) has been conducted. An open-source dataset collected at the Technische Universität Berlin, including simultaneous electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) signals of 26 healthy participants during n-back tests, has been used for this research. Instrumental and physiological noise removal has been done using preprocessing techniques followed by individually detecting activity in both modalities. With resting state threshold circle, VPA has been used to detect a hemodynamic response in fNIRS signals, whereas phase plots for EEG signals have been constructed using Hilbert Transform to detect the activity in each trial. Multiple threshold circles are drawn in the vector plane, where each circle is drawn after task completion in each trial of EEG signal. Finally, both processes are integrated into one vector-phase plot to get combined detection of hemodynamic response for activity. Results of this study illustrate that the combined EEG-fNIRS VPA yields considerably higher average classification accuracy, that is 91.35%, as compared to other classifiers such as support vector machine (SVM), convolutional neural networks (CNN), deep neural networks (DNN) and VPA (with dual-threshold circles) with classification accuracies 82%, 89%, 87% and 86% respectively. Outcomes of this research demonstrate that improved classification performance can be feasibly achieved using multimodal VPA for EEG-fNIRS hybrid data.

Novel Feature Generation for Classification of Motor Activity from Functional Near-Infrared Spectroscopy Signals Using Machine Learning

“…The noise handling new feature generation using wavelet, Hilbert, and Hjorth parameters are implemented in this research to identify the best accuracy in detecting motor tasks such as spatial navigation (SN) and mental drawing (MD). Wavelets are used to handle motion artifact correction [37], phase information of fNIRS signals extracted using Hilbert transform to detect the activity [38]. Also, the classical statistical time domain and frequency domain features are also used.…”

Multimodal Assessment of Cognitive Workload Using Neural, Subjective and Behavioural Measures in Smart Factory Settings

“…Heart rate, which is expected to vary with the occurrence of stress [18], can be extracted from fNIRS data [19]. EEG is an electrophysiological measure that analyses the neural activity of the brain using electrodes located at the head surface [20]. These variables can come in handy for monitoring the vigilance and attention of a person [16] while performing the required experiment.…”