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Analysis of the Electroencephalogram (EEG) can assist in developing a deeper understanding of neural development and maturation. Neurologists observed that neonatal EEG patterns change as a function of post conceptional age. Some waveforms such as delta brushes and traće alternant, the discontinuity of background activity, and the percentages of inter hemispheric synchrony are helpful features in...
Electroencephalogram (EEG) provides an objective analysis of almost every function of the human brain and hence can be used as an effective tool to assess such functions and human audition is no exception. Auditory Temporal Resolution (ATR), defined as the ability of the human brain to perceive the sound and differentiate between various categories of sounds, can assess conditions such as Dyslexia...
The Quran is a script consisting of 114 Surah. The Quran is known to have positive effects on human, aids the stress healing process. Each Surah is provided for a different understanding and meaning of its own. This paper investigates the subject's reaction towards listening to two different Surahs. The electroencephalogram (EEG) machine was used to observe and record the subject brain activity. By...
The study was intended to analyze the effect of three types of music, namely Indian classical music, Bhajan (spiritual anthem) music and rock music, on the electroencephalogram (EEG) activity for two different age groups, viz. young subjects (23–27 years) and elder subjects (40–55 years). We recorded EEG in 10 healthy elder subjects and 10 healthy young subjects at eyes closed condition during without...
Recent advances in signal processing for the detection of Steady-State Visual Evoked Potentials (SSVEPs) have moved away from traditionally calibrationless methods, such as canonical correlation analysis, and towards algorithms that require substantial training data. In general, this has improved detection rates, but SSVEP-based brain-computer interfaces (BCIs) now suffer from the requirement of costly...
In recent years, several studies have shown that there is a correlation between electroencephalographic (EEG) signals and hand-reaching kinematic parameters after applying linear decoders. These studies have been generally conducted using actual upper limb movements, but so far there has been little discussion about the possibility of applying these decoders to motor imagery tasks. Moreover, the use...
For brain function study, it is very important to assess the state of different attention conditions. In this paper, we study a cross-correlation between different attention electroencephalograph (EEG) signals by detrended cross-correlation analysis (DCCA). We use the method to study attention a wave EEG. And, we found that is possible to discriminate the cross-correlation between the meditation state...
In this paper, we propose a method to analyze epileptic electroencephalogram based on time series that is transformed from improved k-nearest neighbor network. The study of complex networks has become a hot research of electroencephalogram signal. Electroencephalogram time series generated by the network keeps node information of network, so researching the time series from the network can also achieve...
Brain activities are inherently determined by a person's unique pattern of neural pathways and are closely associated with his/her genetic personality traits. Brain activity recorded by electroencephalogram (EEG), has recently been regarded as potential candidate in future generation biometric systems. In this paper, a biometric identification system is proposed, combining subject-specific alpha peak...
Canonical correlation analysis (CCA) has been successfully used for extracting frequency components of steady-state visual evoked potential (SSVEP) in electroencephalography (EEG). Recently, a few efforts on CCA-based SSVEP methods have been made to demonstrate the benefits for brain computer interface (BCI). Most of these methods are limited to linear CCA. In this paper consider a deep extension...
Error-related potentials (ErrP) have been increasingly studied in psychophysical experiments as well as for brain-machine interfacing. In the latter case, the generalisation capabilities of ErrP decoders is a crucial element to avoid frequent recalibration processes, thus increasing their usability. Previous studies have suggested that ErrP signals are rather stable across recording sessions. Also,...
In this study we investigated the effect of mastery confidence manipulation on BCI performance and P300 amplitude. We used a 6×6 P300 speller matrix and participants spelled words containing five letters each. Using a cover story, thirty-six participants were misinformed that they would use classification algorithms of diverse difficulty in three experimental blocks (easy, medium, hard condition)...
Electroencephalogram (EEG) is a method that records electrical activities of the brain. Reliable interpretation of its measurements rely on accurate correspondence of the scalp electrodes and the underlying cortical surface. To standardize comparative studies international 10/20, 10/10 and 10/5 systems for the placement of electrodes have been proposed to specify the locations of scalp EEG sensors...
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...
Auditory-evoked noninvasive electroencephalography (EEG) based brain-computer interfaces (BCIs) could be useful for improved hearing aids in the future. This manuscript investigates the role of frequency and spatial features of audio signal in EEG activities in an auditory BCI system with the purpose of detecting the attended auditory source in a cocktail party setting. A cross correlation based feature...
In order to verify whether there is a correlation between human behavior and EEG signal, proposes a method based on exploratory analysis to study the relationship between human behavior and EEG. Through the acquisition of mice EEG and respiratory signal under sleep state, using exploratory analysis modeling to construct a transmission system of the linear time invariant multi-resolution model, then...
Correlation size together with Lyapunov exponents estimated from both electroencephalography (EEG) and electromyography (EMG) signals, are the crucial variables in the classification of mental tasks using an artificial neural network (ANN) classifier for patients suffering from neurological disorders/diseases. The above parameters vary according to the status of the patient, for example: depending...
Accurate and fast detection of event related potential (ERP) components is an unresolved issue in neuroscience and critical health care. Mismatch negativity (MMN) is a component of the ERP to an odd stimulus in a sequence of identical stimuli which has good correlation with coma awakening. All of the previous studies for MMN detection are based on visual inspection of the averaged ERPs (over a long...
Components of auditory event-related potentials (ERPs) may represent various aspects of the cortical processing of pitch. However, evidence hints an earlier representation of pitch perception in auditory ERPs of cortical origin. In this study, we examined whether earlier waves in cortical auditory evoked potentials (CAEPs) might reflect pitch-relevant features of both listeners and stimuli. CAEPs...
If an early predictor of motor functional outcome after stroke were available, stroke patients would receive more appropriate treatments for motor recovery. We performed a correlation analysis of the electroencephalography (EEG) signal patterns of nine subacute stroke patients (recorded 22.9 ± 7.1 days after onset) and their motor recovery rates (measured 100.2 ± 8.9 days after onset). The beta band...
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