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A variety of methods exists for electroencephalographic (EEG) signals classification. In this paper, we briefly review selected methods developed for such a purpose. First, a short description of the EEG signal characteristics is provided. Then, a comparison between the selected EEG signal classification methods, based on the overview of research studies on this topic, is presented. Examples of methods...
The main objective of this paper is the time-frequency analysis of the EEG signal captured in a cognitive task (i.e. object recognition) performed by human subjects. We investigate whether the power spectral density of the gamma frequency range can be used to classify the outcome of the object recognition task (i.e. seen, unseen, uncertain). The EEG signals were acquired and analyzed from 128 electrodes...
The electroencephalogram (EEG) signals are commonly used for diagnosis of epilepsy. In this paper, we present a new methodology for EEG-based automated diagnosis of epilepsy. Our method involves detection of key points at multiple scales in EEG signals using a pyramid of difference of Gaussian filtered signals. Local binary patterns (LBPs) are computed at these key points and the histogram of these...
Brain Computer Interface is a reliable communication interface between human brain and external world. It translates human brain electrical activity to useful command by extracting meaningful features from Electroencephalogram signals. In present work, feature extraction techniques and classification methods are proposed for implementation of Brain Computer Interface system. Proposed methodology is...
Epileptic seizure is the abnormal synchronous neuronal activity that occurs in human brain. The early detection of epileptic seizure helps in improving patient's mental health. In this work, an Electroencephalogram based methodology of automated epileptic seizure detection using Flexible Analytical Wavelet Transform is presented. In the proposed methodology, Electroencephalogram signals are decomposed...
This study investigates the discrimination between calm, exciting positive and exciting negative emotional states using EEG signals. Towards this direction, a publicly available dataset from eNTERFACE Workshop 2006 was used having as stimuli emotionally evocative images. At first, EEG features were extracted based on literature review. Then, a computational framework is proposed using machine learning...
In this paper, the single-channel EEG based classification systems using simple extracted features are investigated. Each classification system contains the following stages: data acquisition, signal decomposition, feature extraction, and classification. In addition to using the filter bank and empirical mode decomposition (EMD) methods for signal decomposition, a sparse discrete wavelet packet transform...
This paper presents a comparison of Electroencephalogram (EEG) signals classification for Brain Computer-Interfaces (BCI). At present, it is a challenging task to extract the meaningful EEG signal patterns from a large volume of poor quality data and simultaneously with the presence of artifacts noises. Selection of the effective classification technique of the EEG signals at classification stage...
Brain computer interface is used for human and machine learning analysis. This paper represents the EEG datasets that are built with different cognitive task such as left, right, back and front imaginary movement with eye open. We have used different feature extraction method to classify these EEG signal using Support Vector Machine (SVM), k-Nearest Neighbor (k-NN) and Artificial Neural Network (ANN)...
A biometric person authentication system using brain waves or Electroencephalogram (EEG) signals recorded using a minimum number of channels ranging from 2 to 6 is presented. The task for EEG recording consists of simple motor imagery movements that the subject has to imagine. The system uses an effective time-frequency based feature extraction method using the short-time Fourier transform (STFT)...
Extreme Learning Machine (ELM) is a fast and efficient classifier with single hidden layer feed-forward neural networks. In this paper, the ELM is employed to classify the EEG signals in BCI system, the BCI competition datasets are used to test, the mutual information and classify accuracy are considered as evaluation criteria. Compare with the LDA and SVM, the ELM method could obtain more mutual...
In recent years, the detection of drowsiness based on Electroencephalogram (EEG) signal has been paid great attentions. Most of the popular algorithms used for Brain Computer Interface (BCI) applications are, the Support Vector Machine (SVM) and the Artificial Neuronal Network (ANN)). The challenge is to developed a drowsiness detection system that is at once adapt to an embedded implementation and...
The introduction of game-based learning (GBL) into the pedagogical processes and curriculum design can increase student engagement in the learning process. There are a range of game based learning approaches available, but, so far, limited adoption of serious games has been recorded. The digital habits of learners should be studied carefully, to better understand the way current technology-savvy students...
Performance of any brain computer interface system depends upon features of electroencephalogram signals. Electroencephalogram signals undergo for unpredictable changes when vigilance state of human brain alters widely. This may cause adverse changes in extracted features and affect classification performance of brain computer interface system. To avoid miss-classification, brain computer interface...
Emotion recognition is an important task for computer to understand the human status in brain computer interface (BCI) systems. It is difficult to perceive the emotion of some disabled people through their facial expression, such as functional autism patient. EEG signal provides us a non-invasive way to recognize the emotion of these disable people through EEG headset electrodes placed on their scalp...
Epilepsy is a global problem, and with seizures eluding even the smartest of diagnosis, a requirement for automatic detection of the same using electroencephalogram (EEG) would have a huge impact in diagnosis of the disorder. Contemporary researchers went ahead and devised a multitude of methods for automatic epilepsy detection, becoming a reason why one should find the best method out, based on accuracy,...
Measurement of cognitive load using brain signalsis an important area of research in human behavior and psychology. Recently, there have been attempts to use low cost, commercially available Electroencephalogram (EEG) devices for the analysis of the cognitive load. Due to the reduced number of leads, these low resolution devices pose major challenges in signal processing as well as in feature extraction...
In this paper, a statistical method for automatic detection of seizure and epilepsy in the dual-tree complex wavelet transform(DT-CWT) domain is proposed. Variances calculated from the EEG signals and their DT-CWT sub-bands are utilized as features in the classifiers such as artificial neural network(ANN) and support vector machine(SVM). Studies are conducted using EEG signals from a publicly available...
Electroencephalogram (EEG) is one of the potential physiological signals used for detecting epileptic seizure. Discriminant features, representing different brain conditions, are often extracted for diagnosis purposes. On-line detection necessitates that these features are to be computed efficiently. In this work, an evidence theory-based approach for epileptic detection, using such features, and...
This paper intends to be a literature review in the field of emotions detection using spectral analysis of neurological signals. It also shows the great boom in Brain Computer Interfaces (BCI) applications. Explains the research methodology used for this type of projects, and finally it highlights the results of several studies that have been done in this area.
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