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In this paper, a sub-band correlation-based method is proposed for the automatic detection of epilepsy and seizure. The analysis is carried out by decomposing the electroencephalogram (EEG) signals, collected from a publicly available EEG database, into the dual tree complex wavelet transform(DT-CWT) domain. An Artificial Neural Network(ANN) is employed as a classifier where the maximum cross-correlation...
Electroencephalogram (EEG) is used to measure the bioelectric potential on the brain scalp. The recorded EEG signal can have different types of artifacts and the interpretation of a noisy EEG signal is difficult. In this research work, a simple method is proposed to minimize the artifacts present in the EEG signals recorded while perceiving a pure tone. The recorded EEG signal can contain artifacts,...
Developing efficient and usable brain-computer interfaces (BCIs) requires well-designed trade-off between accuracy and computational time. This paper presents a very fast and accurate method to classify asynchronous brain signals from a multi-class mental tasks dataset using time-domain features. Five different statistical time-domain features were extracted to characterize various properties of three...
This paper presents novel time-frequency (t-f) features based on t-f image descriptors for the automatic detection and classification of epileptic seizure activities in EEG data. Most previous methods were based only on signal-related features derived from the instantaneous frequency and the energies of EEG signals generated from different spectral sub-bands. The proposed features are extracted from...
In this paper, a method of seizure and non-seizure classification has been proposed based on the higher order statistics of the dominant Intrinsic mode function (IMF) resulting from the Empirical Mode Decomposition(EMD) of the EEG signals. Analyzing the temporal energy contents of different IMFs, it is found reasonable to determine the dominant IMF. In order to reduce the dimensionality, higher order...
This paper presents a new embedded architecture for home devices control system directed through motor imagery actions captured by EEG headset. The proposed system is validated by an offline approach which consists on using available public data-set. These recording are always accompanied with noise and useless information related to the equipment, eyes blinking and many others resources of artifacts...
Brain-computer interface (BCI) is currently developed as an alternative technology with a potential to restore lost motor function in patients with neurological injuries. In this paper, we describe an integrated system of a non-invasive electroencephalogram (EEG)-based BCI with a non-invasive functional electrical stimulation (FES). This system enables the direct brain control of upper limbs to achieve...
In this paper we introduced a new method to optimally select the time window for a single-trial classification problem in BCI system. As a hybrid-BCI, we combine EEG and NIRS signals to improve the performance of BCI system. Since there's a coupled relationship between EEG and NIRS, we try to define the activation state of subject's brain according to the changes of hemoglobin. We therefore defined...
Stress is a mental condition that can effects the brain electrical activity to be different from the normal state. This brain cognitive change can be measured using EEG. The objective of this paper is to classify stress subjects based on EEG signal using SVM. The data which are used to represent stress subjects were taken from the residents of Pusat Darul Wardah; a shelter centre for troubled women...
This paper presents an intelligent system for the classification of ischemic stroke severity. The application of Artificial Neural Network (ANN) is proposed in this study to classify ischemic stroke severity using EEG sub bands Relative Power Ratio (RPR). There were 100 subjects from National Stroke Association of Malaysia NASAM, Petaling Jaya, Selangor, Malaysia divided into Early Group (EG), Intermediate...
Power spectral density analysis (PSDA) and canonical correlation analysis (CCA) are two of the most widely used feature extraction methods for SSVEP based brain computer interfaces. However, these features may be contaminated by spontaneous EEG or noise. It is still a challenge to detect it with a high accuracy, especially at a short time window (TW) which is a tradeoff between accuracy and speed...
Brain-Computer Interfaces (BCIs) help paralyzed people who have lost some or all of their ability to communicate and control the outside environment from loss of voluntary muscle control. Most BCIs are based on the classification of multichannel electroencephalography (EEG) signals recorded from users as they respond to external stimuli or perform various mental activities. The classification process...
Motor imagery (MI) based electroencephalogram (EEG) signals are a widely used form of input in brain computer interface systems (BCIs). Although there are a number of ways to classify data, a question still persists as to which technique should be employed in the domain of MI based EEG signals. In this paper, an attempt is made to find the best classification algorithm and feature extraction technique...
Epilepsy is a common neurological disorder which is difficult to treat because of its unpredictable and recurrent nature. The electroencephalogram (EEG) is a valuable tool for detecting epileptic seizures. With the aim of reducing the input feature dimensionality, a single median based feature called interquartile range (IQR) was used in this paper for the classification of normal and seizure EEG...
In this work, the EEG signal is decomposed into its five subbands viz. delta (0.8–4Hz), theta (4–8Hz), alpha (8–15Hz), beta (15–30Hz), gamma (above 30Hz) using Triplet Half-band Filter Bank (THFB). Then, the autoregressive (AR) model is computed for each subband. Next, power spectral density (PSD) of the AR coefficients of each subbands is estimated for classfication of normal and epileptic EEG. It...
Brain Computer Interface(BCI) systems provide an additional way for people to interact with external environment without using peripheral nerves or muscles[1]. In a variety of BCI systems, a BCI system based on the steady-state visual evoked potentials (SSVEP) is one most common system known for application, because of its ease of use and good performance with little user training. In this study,...
This paper presents new time-frequency (T-F) features to improve the detection and classification of epileptic seizure activities in EEG signals. Most previous methods were based only on signal features derived from the instantaneous frequency and energies of EEG signals generated from different spectral sub-bands. The proposed features are based on T-F image descriptors, which are extracted from...
Brain-Computer Interfaces (BCIs) provide a way to communicate without movement and can offer significant clinical benefits therefore. Electrical brain activity recorded using electroencephalography (EEG) can be automatically interpreted by supervised learning classifiers according to the descriptive features of the signal. Compressive sensing paradigm commonly used for array antenna design and signal...
Non-invasive Brain-Computer Interface (BCI) has appeared as a new hope for a large population of disabled people, who were waiting for a new communication means that would translate some brain responses into actions. After several decades of research in fields such as neuroscience and machine learning, the performance remains too low due to the low signal to noise ratio of the EEG signal, and the...
We address a classification method for motor imagery tasks-based brain computer interface (BCI). The wavelet coefficients are used to extract the features from the motor imagery electroencephalographic (EEG) signals and the k-nearest neighbor classifier is applied to classify the pattern of left or right hand imagery movement and rest. The performance of the proposed method is evaluated using EEG...
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