Functional near-infrared spectroscopy (fNIRS) is a relatively new imaging modality in the
functional neuroimaging research arena. The fNIRS modality non-invasively investigates the …

To achieve a highly efficient brain-computer interface (BCI) system regarding emotion
recognition from electroencephalogram (EEG) signal, the most crucial issues are feature …

This paper proposes a method to find the best conditions for applying Savitzky–Golay (SG)
filter to remove physiological noises from the functional near-infrared spectroscopy (fNIRS) …

Human cognitive load level assessment is a challenging issue in the field of functional brain
imaging. This work aims to study different cognitive load levels statistically from brain …

The extraction methodology of the significant features from the signals is one of the most
important pre-requisite steps for EEG signal classification. Common spatial pattern (CSP) is …

The trends in movement-related functional activity measurement for brain-computer interface
(BCI) are mostly associated with the central lobe of the brain. This consideration may be a …

This work reports the potentiality of the motor imagery movement classification from
prefrontal hemodynamics for the brain–computer interface (BCI) applications. Although …

In this paper, we investigated the hemodynamic response of human brain activity during
answering lie with respect to true. For laboratory investigation, several mock lying protocols …

EEG signal in the time domain with high sampled rate faces difficulties for their noise
sensitive properties that lead to erroneous feature extraction. Since the feature extraction is …

Motor imagery events classification is very important for functional near-infrared
spectroscopy (fNIRS) based brain-computer interface research. Moreover, the detection of …