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Brain activations underlying control of breathing are not completely known. Furthermore, the coupling between neural and respiratory dynamics is usually estimated through linear correlation measures, thus totally disregarding possible underlying nonlinear interactions. To overcome these limitations, in this preliminary study we propose a nonlinear coupling analysis of simultaneous recordings of electroencephalographic...
State-of-the-art hearing prostheses are equipped with acoustic noise reduction algorithms to improve speech intelligibility. Currently, one of the major challenges is to perform acoustic noise reduction in so-called cocktail party scenarios with multiple speakers, in particular because it is difficult-if not impossible-for the algorithm to determine which are the target speaker(s) that should be enhanced,...
It's proposed that weights of links play critical role in complex system. In this study, we adopted nine network characteristics to verify their performance in the brain of temporal lobe epilepsy (TLE). Weighted networks were derived from phase locking values on multichannel intracranial electroencephalography (EEG) recordings when the patient is undergoing seizure attack. It's illustrated that network...
Nowadays, wavelet transform (WT) is widely used in the realm of signal denoising, has proven a high effectiveness in terms of time and quality concerning denoising methods. Despite there are several achievements denoising through wavelet thresholding methods, these do not disclose an optimal configuration. In this paper, we proposed a comparative performance analysis of several thresholding methods...
Attention is the primary cognitive process to induce a response to a stimulus. Maintaining the attentive state continuously for a prolonged period of time is known as sustained attention which is vital for performing any task. The present study aims at evaluating the activation of different brain regions while performing an attention requiring task. A standard attention task called the Visual Continuous...
It is assumed that the network of cortical brain area associated with task difficulty may dynamically allocate resources for difficult decisions. As a result, it could play an indispensable role during the process of decision making. Recently, the lateralized readiness potential (LRP) is reinterpreted as a new way to study the evidence accumulation process during decision making. Therefore, we use...
In this paper, we present a control method that combines Brain-computer interface based on Steady-State Visual Evoked Potentials and traditional force/angle sensors to control a lower-limb rehabilitation exoskeleton. Through visual stimulation experiments, the subjects will produce raw electroencephalogram signals including commands information. After acquiring such information, the control system...
This study conducted a preliminary investigation on the correlations between learners' EEG-based workload and their self-reported cognitive load in a multimedia learning context. An experiment including two learning tasks was conducted with 15 graduate students. The NeuroSky brainwave headset was used to collect participants' electroencephalography (EEG) data and using the theta/alpha ratio as brain...
The canonical correlation analysis (CCA), double-partial least-squares (DPLS) methods and least absolute shrinkage and selection operator (LASSO) have been proven effectively in detecting the steady-state visual evoked potential (SSVEP) in SSVEP-based brain-computer interface systems. However, the accuracy of SSVEP classification can be affected by phase shifts of the electroencephalography data,...
This paper proposes a novel evolutionary approach to the optimal selection of electrodes as well as relevant EEG features for effective classification of cognitive tasks. The problem has been formulated in the framework of a single objective optimization problem with an aim to simultaneously satisfy three criteria. The first criterion deals with maximization of the correlation between the features...
In this paper, we fuse EEG and forehead EOG to detect drivers' fatigue level by using discriminative graph regularized extreme learning machine (GELM). Twenty-one healthy subjects including twelve men and nine women participate in our driving simulation experiments. Two fusion strategies are adopted: feature level fusion (FLF) and decision level fusion (DLF). PERCLOS (the percentage of eye closure)...
A measurement method for the evaluation of the image complexity based on SIFT&K-means algorithm, namely the estimation of the mismatch between the target and the interesting points has been introduced in our previous research. Based on this method, we have made some improvements to calculate the image complexity of images with different memory targets. The improved algorithm SIFT&AIM&K-means...
Blinking has two functions: to moisturize eyes and as a defensive response to the environment and responses caused by the by mental processes. In this paper, we investigate statistical characteristics of blinks and blink rate variability of 11 subjects. The subjects are presented with a reading/memorization session preceded and followed by resting sessions. EEG signals were recorded during these sessions...
Electroencephalogram (EEG) is a tool to record the electrical activity of brain. It often records artifacts which are the electrical activities originating from sites other than brain. The presence of artifacts is undesirable as it increases the probability of misinterpretation that may result in adverse clinical consequences. In this paper, we present a novel method of motion artifact removal from...
The medical therapeutic method presented in this paper is an extension of the biofeedback technique that uses supplementary stimuli and a pattern recorder of the bio signals correlated to the patient's subjective state of wellness. The method puts the patient in the automated therapeutic system acting as the regulator of his (her) own physiological parameters and provides a set of patterns for further...
Brain Computer Interface (BCI) could be used as an effective tool for active engagement of patients in motor rehabilitation by enabling them to initiate the movement by sending the command to BCI directly via their brain. In this paper, we have developed a BCI using novel EEG analysis to control a Virtual Reality avatar and a Soft Robotics rehabilitation device. This BCI is able identify and predict...
In this paper, we propose a technique for improving the feature extraction and classification stages in EEG-based Brain-Computer Interface (BCI) Systems. The problem can be formulated as Linear Matrix Inequalities (LMIs) and, therefore, be solved through robust computational tools. The idea is to represent the EEG signals using a sinusoidal signal basis in a given frequency range, and introducing...
Brain computer interface (BCI) can establish communication between human brain and a computer which is independent from normal neuromuscular pathways. This allows giving instructions to the computer without the use of standard communication channels, such as a mouse or keyboard. This paper describes the development of a steady-state visual evoked potential (SSVEP) based BCI system. EEG amplifier with...
In this paper, analysis of correlation based functional connectivity network of electroencephalogram (EEG) signals has been done. Functional connectivity network explores the important facts about activation and synchronization among different areas of brain. Multi-channel EEG signals generated through audio-visual stimulation is used in this study. Correlated electrode pairs show the simultaneous...
Classification of data is used in data analysis to group various instances in appropriate classes to enhance readability of data and study its characteristics easily. The main aim of every classification problem is the enhancement of classification accuracy. Ranked feature ordering helps in improving the classification accuracy by removing the least dominant features. Classification model uses only...
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