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In this study, we have generated four active commands using hybrid electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) for quadcopter control in online environment. Mental arithmetic, left hand clench imagery, and left and right eye-movements are used to navigate the quadcopter. Mental arithmetic task (decoded by fNIRS from the prefrontal cortex) is used to move the quadcopter...
This paper presents a multiple optodes arrangement method in functional near-infrared spectroscopy (fNIRS) for measuring the absolute concentrations of oxy-hemoglobin and deoxy-hemoglobin. To remove physiological noises, short/long-separation detectors are used in the spatially resolved spectroscopy approach. The proposed method is applied to measure the brain activity of five healthy male subjects...
In this paper, we investigate the feasibility of identifying the functional near-infrared spectroscopy (fNIRS) signal occurred from a single trial arithmetic task, in which the rest state hemodynamic response (HR), the occurrence of an initial dip, and the regular hemodynamic response are involved. fNIRS signals are measured from five healthy subjects for mental arithmetic tasks from the prefrontal...
In this research, we have investigated the detection of drowsiness activity in dorsolateral-prefrontal cortex in three different time windows (0∼3 sec, 0∼4 sec and 0∼5 sec) using functional near-infrared spectroscopy (fNIRS). Five drowsy subjects participated in a simulated driving task while their brain activity is monitored using fNIRS. The recorded brain activity is segmented into three windows...
In this paper, we have investigated the feasibility of detecting drowsiness using hemodynamic brain signals for a passive brain-computer interface (BCI). Functional near-infrared spectroscopy (fNIRS) is used to measure the right dorsolateral-prefrontal brain region in order to investigate the hemodynamic changes corresponding to drowsy and alert states. The data is recorded using five drowsy subjects...
In this paper, we have demonstrated the ability of functional near-infrared spectroscopy (fNIRS) system to detect spontaneous lie in an interactive game paradigm. Brain signals from prefrontal cortex area of four healthy male subjects were collected using wireless fNIRS system. Oxy and deoxy-hemoglobin (HbO and HbR) signals were used to define the features and then data was classified using linear...
In this paper, we have combined electroencephalography (EEG) and functional near-infrared spectroscopy (fNRIS) to make a hybrid EEG-NIRS based system for brain-computer interface (BCI). The EEG electrodes were placed on the motor cortex region and the NIRS optodes were set on the prefrontal region. The data of four subjects was acquired using mental arithmetic tasks and motor imageries of the left-...
Various neuroimaging modalities have appeared to acquire brain signals for developing a brain-computer interface (BCI). In this article, we review studies on different modalities including both invasive and non-invasive techniques for the implementation of BCIs, for brain signals detection, decoding, feature extraction, and classification. We discuss their advantages, disadvantages, and implementation...
The purpose in this paper is to reduced noise of hemodynamic response including physiological noises and motion artifacts. We measured neuronal activation coupled hemodynamic response using functional near infrared spectroscopy (fNIRS) from five subjects during arithmetical task in the prefrontal cortex. Blind separation into independent components (ICs) as good technique for signal processing is...
In the experiment, four different inter-stimulus intervals (ISIs) are utilized: 325 ms, 350 ms, 375 ms, and 400 ms. The applicability of an adaptive nonlinear principle component analysis method for extracting the P300 waves included in the EEG signals without down-sampling and averaging of the original signals was demonstrated. Back-propagation neural networks were used as the P300 classifier. After...
Deception involves complex neural processes and correlates in the brain. Different techniques have been used to study and understand brain mechanisms during deception. Moreover, efforts have been made to develop schemes that can detect and differentiate the deception and truth telling. In this paper, a functional near-infrared spectroscopy (fNIRS) based online brain deception decoding framework is...
In this paper, a new adaptive neural network classifier of six different mental tasks from EEG-based P300 signals is proposed. To overcome the classifier of overtraining caused by noisy and non-stationary data, the EEG signals are filtered and extracted using autoregressive model before passed to the adaptive neural network classifier. To test the improvement in the EEG classification performance...
The nonstationary nature of the brain signals provides a rather unstable input resulting in uncertainty and complexity in the control. Intelligent processing algorithms adapted to the task are a prerequisite for reliable BCI applications. This work presents a novel intelligent processing strategy for the realization of an effective BCI which has the capability to improved classification accuracy and...
EEG recordings provide an important means of brain-computer communication, but their classification accuracy is limited by unforeseeable signal variations due to artifacts or recognizer-subject feedback. A number of techniques recently have been developed to address the related problem of recognizer robustness to uncontrollable signal variation. In this paper, we propose a classification method entailing...
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