Early Detection of Hemodynamic Responses Using EEG: A Hybrid EEG-fNIRS Study

Abstract

Enhanced classification accuracy and a sufficient number of commands are highly demanding in brain computer interfaces (BCIs). For a successful BCI, early detection of brain commands in time is essential. In this paper, we propose a novel classifier using a modified vector phase diagram and the power of electroencephalography (EEG) signal for early prediction of hemodynamic responses. EEG and functional near-infrared spectroscopy (fNIRS) signals for a motor task (thumb tapping) were obtained concurrently. Upon the resting state threshold circle in the vector phase diagram that uses the maximum values of oxy- and deoxy-hemoglobin (ΔHbO and ΔHbR) during the resting state, we introduce a secondary (inner) threshold circle using the ΔHbO and ΔHbR magnitudes during the time window of 1 s where an EEG activity is noticeable. If the trajectory of ΔHbO and ΔHbR touches the resting state threshold circle after passing through the inner circle, this indicates that ΔHbO was increasing and ΔHbR was decreasing (i.e., the start of a hemodynamic response). It takes about 0.5 s for an fNIRS signal to cross the resting state threshold circle after crossing the EEG-based circle. Thus, an fNIRS-based BCI command can be generated in 1.5 s. We achieved an improved accuracy of 86.0% using the proposed method in comparison with the 63.8% accuracy obtained using linear discriminant analysis in a window of 0~1.5 s. Moreover, the active brain locations (identified using the proposed scheme) were spatially specific when a t-map was made after 10 s of stimulation. These results demonstrate the possibility of enhancing the classification accuracy for a brain-computer interface with a time window of 1.5 s using the proposed method.

Developmental Differences in Cortical Activation During Action Observation, Action Execution and Interpersonal Synchrony: An fNIRS Study

“…Given its advantage to tolerate motion artifacts, fNIRS has been used to study cortical activation across various movements of walking (Holtzer et al, 2019), playing a dance video game (Tachibana et al, 2011), as well as free arm movements during face to face interactions with others (Egetemeir et al, 2011). Moreover, with its greater temporal resolution compared to fMRI (Lloyd-Fox et al, 2010), fNIRS does a better job of detecting the onset and features of the hemodynamic response Khan et al, 2018). This ability to distinguish features of the hemodynamic response may help in identifying differences related to development as well as neuropathology.…”

Reduction of Onset Delay in Functional Near-Infrared Spectroscopy: Prediction of HbO/HbR Signals

“…Vector phase analysis is a polar coordinate plane method defined by HbO and HbR as orthogonal vector components. Two other vector components, cerebral oxygen exchange ( COE) and cerebral blood volume ( CBV), are obtained by rotating the vector coordinate system by 45 • counterclockwise using the following equations (Yoshino et al, 2013;Khan et al, 2018).…”

Application of functional near-infrared spectroscopy in the healthcare industry: A review

“…[332][333][334][335][336][337][338] The utilization of advanced signal processing and adaptive control algorithms in the future can be helpful to achieve earlier detection of the hemodynamic response not to mention the accuracy. 11,[339][340][341][342][343]…”

“…4,9 Various algorithms and techniques have been developed and explored using fNIRS to improve the brain-computer interfaces (BCIs) to help physically disabled persons. [10][11][12][13][14][15] This paper reviews the research works conducted to advance the understanding of the e®ects of various diseases on our brain using fNIRS. These studies mostly involve patients with degenerative brain or psychiatric disorders.…”