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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...
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...
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...
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...
Wu and coworkers introduced an active basis model (ABM) for detecting generic objects in static images. A grey-value local power spectrum was utilized to find a common template and deformable templates from a set of training images and to detect an object in unknown images by template matching. In this paper, we propose a color-based active basis model (color-based ABM for short) which includes color...
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