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Front Hum Neurosci. 2018 Nov 29;12:479. doi: 10.3389/fnhum.2018.00479. eCollection 2018.

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

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School of Mechanical Engineering, Pusan National University, Busan, South Korea.
School of Mechanical and Manufacturing Engineering, National University of Science and Technology, Islamabad, Pakistan.
Department of Cogno-Mechatronics Engineering, Pusan National University, Busan, South Korea.


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.


brain-computer interface (BCI); classifier; electroencephalography (EEG); functional near-infrared spectroscopy (fNIRS); hemodynamic response; hybrid EEG-fNIRS; vector phase diagram

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