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This paper presents a novel method for classifying four different levels of cognitive workload. The workload levels are generated using visual stimuli degradation. Electroencephalogram (EEG) signals recorded from 16 subjects were used for workload classification. The proposed solution includes preprocessing of EEG signals and feature extraction based on statistical features. This is followed by variable...
In this paper, time-frequency domain operation exploiting wavelet analysis is performed on the gamma band (40–80 Hz) and theta band (4–8 Hz) oscillations of EEG signals in order to classify normal activity of healthy person, seizure-free interval (inter-ictal) and seizure (ictal) activity of seizure patients. Gaussian statistical model is utilized in wavelet coefficients to summarize information in...
Phase synchronies are often used to study relationships between different parts of the brain and to identify regions that interact in a coordinated manner for a certain task. In this paper, we propose a wavelet reconstruction and phase-locking-based feature extraction method to visualize and classify the direction-specific phase synchronies between Electroencephalogram (EEG) channel-pairs for hand...
Using electroencephalography for diagnosis of seizure attacks has been in a great attention as it records abnormal electrical activities of the brain. This paper proposes a novel technique for diagnosis of epileptic seizures based on non-linear entropy features extracted from maximal overlap discrete wavelet packet transform (MODWPT) of EEG signals. Discriminative features are selected by a t-test...
In this study, EEG data recorded during mental arithmetic operations and silent reading were analyzed by discrete wavelet transform and feature vectors were obtained. The obtained feature vectors are classified by Support Vector Machines (SVM). Results are given for 26 channels, all recorded channels, and for 10 most effective channels. Correlation based feature selection based algorithm is used for...
The aim of this study was to detect sleep stages of human by using EEG signals. In accordance with this purpose, discrete wavelet transforms (DWT) and empirical mode decomposition (EMD) were separately used for feature extraction. Subcomponents of EEG signals obtained by the two methods were assumed as feature vectors. Statistical parameters were used to reduce dimension of feature vectors. The same...
Epilepsy, a recurring disorder is symptomized by unprovoked seizures. Considered as one of the most common neurological disorders, Epilepsy affects people of all ages. Around 65 Million people across the world suffer from this disease. Manual diagnosis of EEG signals of long duration may be a source of error as well as a cumbersome task. Hence automation in Seizure Detection is essential for diagnosis...
Sleep Apnoea Syndromes (SAS) is a sleep disorder which caused breathing pauses during sleep at night. There is various method of analyzing sleep EEG signals can be found in the literature. In this paper both linear; Discrete Wavelet Transform (DWT) and non-linear; Approximate Entropy (ApEn) extraction methods were performed to differentiate features of each sleep stages between apnoea and healthy...
EEG signals, recording both normal and abnormal activities of neurons in the brain, are widely used in epilepsy detection. In this paper, an EEG signal classification method based on Slantlet Transform and sparse coding is proposed to greatly reduce number of false alarms and improve speed of detection. With Slantlet Transform, salient information of EEG signals is mapped into a sparse space. In order...
Emotions play a significant role in human communication and decision making. In order to bypass current limitations of human-robot interaction, more natural, trustworthy and nonverbal way of communication is needed. This requires robots to be able to explain and perceive person's emotions. Our work is based on the concept that each emotional state can be placed on a two-dimensional plane with arousal...
This paper proposes a novel relative wavelet bispectrum (RWB) approach for EEG signal feature extraction method to differentiate the signal between the alcoholic over the non-alcoholic subjects. Firstly, the EEG signal is calculated for its autocorrelation frequencies as the basic step in the bispectrum calculation. Then, the discrete wavelet transform (DWT) is applied substituting the FFT which usually...
Epilepsy is a chronic neurological disorder characterized by recurrent, sudden discharges of cerebral neurons, called seizures. Seizures are not always clearly defined and have extremely varied morphologies. Neurophysiologists are not always able to discriminate seizures, especially in long-term EEG datasets. Affecting 1% of the worlds population with 1/3 of the epileptic patients not corresponding...
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...
The method of time-frequency analysis are the ways that mainly are used to analyze electroencephalography (EEG) signals. In our previous studies, we proved that watching 2D and 3D of a movie causes different effects in EEG signals. In this paper, we used dataset of previous study and mean of five EEG frequency bands as features. To obtain these frequency bands, a frequency analysis and two time-frequency...
Using EEG signal as a new type of biometric in user authentication systems has been emerging as an interesting research topic. However, one of the major challenges is that a huge amount of EEG data that needs to be processed, transmitted and stored. The use of EEG compression is therefore becoming necessary. In this paper, we investigate the feasibility of using lossy compression to EEG data in EEG-based...
By using a brain-computer interface system (BCIs) humans can be enable direct communication with a computer or electronic device. In our previous works, we proved that gaze on the different rotation vanes causes a different effects on EEG signals. This paper proffers a novel BCI system based on this issue. Our BCI system proposes to identify four different rotating vane from EEG Signals that represents...
The importance of learning important features in an automatic manner is growing exponentially as the volume of data and number of systems using pattern recognition techniques continue to increase. In this paper, arousal recognition from multi channels EEG signals was conducted using human crafted statistical features and learned features from 32 different EEG source channels. We have obtained 98.99%...
In recent years, SRC has received many attentions for classification and identification tasks. This paper attempts to introduce a sparse representation based classification of EEG signal features and identification of associated activities or tasks. It uses wavelet and ICA processing of EEG signal for feature selection and dictionary training. Multiple dictionaries are trained and used for EEG signal...
Emotion plays an important role in human daily life and is a significant feature for interaction among people. Due to having adaptive role, it motivate human to respond stimuli in their environment quickly for improving their communication, learning and decision-making. With increasing role of brain computer interface (BCI) in interaction between users and computer, automatic emotion recognition has...
This work has focused on the possibilities of classifying vowels ‘a’, ‘e’, ‘i’, ‘o’, ‘u’ from EEG signals, that has been derived while imagining the vowels, with minimum input features. The EEG signals have been acquired from 5 subjects while imagining and uttering the vowels during a well defined experimental protocol, have been processed and segmented using established signal processing routines...
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