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 (Δ<i>HbO</i> and Δ<i>HbR</i>) during the resting state, we introduce a secondary (inner) threshold circle using the Δ<i>HbO</i> and Δ<i>HbR</i> magnitudes during the time window of 1 s where an EEG activity is noticeable. If the trajectory of Δ<i>HbO</i> and Δ<i>HbR</i> touches the resting state threshold circle after passing through the inner circle, this indicates that Δ<i>HbO</i> was increasing and Δ<i>HbR</i> 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 <i>t-</i>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.