scholarly journals Sample Entropy on Multidistance Signal Level Difference for Epileptic EEG Classification

2018 ◽  
Vol 2018 ◽  
pp. 1-6 ◽  
Author(s):  
Achmad Rizal ◽  
Sugondo Hadiyoso
Keyword(s):  
High Performance ◽  
Cell Activity ◽  
Sample Entropy ◽  
Brain Cell ◽  
Support Vector ◽  
Eeg Signals ◽  
Eeg Classification ◽  
Level Difference ◽  
Eeg Data

Epilepsy is a disorder of the brain’s nerves as a result of excessive brain cell activity. It is generally characterized by the recurrent unprovoked seizures. This neurological abnormality can be detected and evaluated using Electroencephalogram (EEG) signal. Many algorithms have been applied to achieve high performance for the EEG classification of epileptic. However, the complexity and randomness of EEG signals become a challenge to researchers in applying the appropriate algorithms. In this research, sample entropy on Multidistance Signal Level Difference (MSLD) was applied to obtain the characteristic of EEG signals, especially towards the epilepsy patients. The test was performed on three classes of EEG data: EEG signals of epilepsy patient in ictal (seizure), interictal conditions (occurring between seizures) and normal EEG signals from healthy subjects with a closed eye condition. In this study, classification and verification were done using the Support Vector Machine (SVM) method. Through the 5-fold cross-validation, experimental results showed the highest accuracy of 97.7%.

Epilepsy EEG Classification Based on Convolution Support Vector Machine

2021 ◽  
Vol 11 (1) ◽  
pp. 25-32
Author(s):  
Qi Xin ◽  
Shaohai Hu ◽  
Shuaiqi Liu ◽  
Xiaole Ma ◽  
Hui Lv ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Focal Epilepsy ◽  
Principal Component ◽  
Support Vector ◽  
Eeg Signals ◽  
Eeg Classification ◽  
Eeg Data ◽  
Epilepsy Diagnosis ◽  
Electroencephalogram Eeg

Clinical Electroencephalogram (EEG) data is of great significance to realize automatable detection, recognition and diagnosis to reduce the valuable diagnosis time. To make a classification of epilepsy, we constructed convolution support vector machine (CSVM) by integrating the advantages of convolutional neural networks (CNN) and support vector machine (SVM). To distinguish the focal and non-focal epilepsy EEG signals, we firstly reduced the dimensionality of EEG signals by using principal component analysis (PCA). After that, we classified the epilepsy EEG signals by the CSVM. The accuracy, sensitivity and specificity of our method reach up to 99.56%, 99.72% and 99.52% respectively, which are competitive than the widely acceptable algorithms. The proposed automatic end to end epilepsy EEG signals classification algorithm provides a better reference for clinical epilepsy diagnosis.

scholarly journals A Study on Sensitive Bands of EEG Data under Different Mental Workloads

Algorithms ◽  
2019 ◽  
Vol 12 (7) ◽  
pp. 145
Author(s):  
Hongquan Qu ◽  
Zhanli Fan ◽  
Shuqin Cao ◽  
Liping Pang ◽  
Hao Wang ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Support Vector ◽  
Svm Classifier ◽  
Eeg Signals ◽  
Power Spectral ◽  
Eeg Data ◽  
Vector Machines ◽  
Full Band ◽  
Electroencephalogram Eeg

Electroencephalogram (EEG) signals contain a lot of human body performance information. With the development of the brain–computer interface (BCI) technology, many researchers have used the feature extraction and classification algorithms in various fields to study the feature extraction and classification of EEG signals. In this paper, the sensitive bands of EEG data under different mental workloads are studied. By selecting the characteristics of EEG signals, the bands with the highest sensitivity to mental loads are selected. In this paper, EEG signals are measured in different load flight experiments. First, the EEG signals are preprocessed by independent component analysis (ICA) to remove the interference of electrooculogram (EOG) signals, and then the power spectral density and energy are calculated for feature extraction. Finally, the feature importance is selected based on Gini impurity. The classification accuracy of the support vector machines (SVM) classifier is verified by comparing the characteristics of the full band with the characteristics of the β band. The results show that the characteristics of the β band are the most sensitive in EEG data under different mental workloads.

COMPARISON OF ICTAL AND INTERICTAL EEG SIGNALS USING FRACTAL FEATURES

2013 ◽  
Vol 23 (06) ◽  
pp. 1350028 ◽  
Author(s):  
YU WANG ◽  
WEIDONG ZHOU ◽  
QI YUAN ◽  
XUELI LI ◽  
QINGFANG MENG ◽  
...  
Keyword(s):  
Feature Analysis ◽  
Support Vector ◽  
Eeg Analysis ◽  
Eeg Signals ◽  
Eeg Classification ◽  
Significant Difference ◽  
Interictal Eeg ◽  
Nonlinear Features ◽  
The Difference ◽  
Multi Scales

The feature analysis of epileptic EEG is very significant in diagnosis of epilepsy. This paper introduces two nonlinear features derived from fractal geometry for epileptic EEG analysis. The features of blanket dimension and fractal intercept are extracted to characterize behavior of EEG activities, and then their discriminatory power for ictal and interictal EEGs are compared by means of statistical methods. It is found that there is significant difference of the blanket dimension and fractal intercept between interictal and ictal EEGs, and the difference of the fractal intercept feature between interictal and ictal EEGs is more noticeable than the blanket dimension feature. Furthermore, these two fractal features at multi-scales are combined with support vector machine (SVM) to achieve accuracies of 97.58% for ictal and interictal EEG classification and 97.13% for normal, ictal and interictal EEG classification.

Online EEG Classification of Covert Speech for Brain–Computer Interfacing

2017 ◽  
Vol 27 (08) ◽  
pp. 1750033 ◽  
Author(s):  
Alborz Rezazadeh Sereshkeh ◽  
Robert Trott ◽  
Aurélien Bricout ◽  
Tom Chau
Keyword(s):  
Chance Level ◽  
Support Vector ◽  
Eeg Classification ◽  
Time Frequency ◽  
Computer Interfaces ◽  
Online Classification ◽  
Average Accuracy ◽  
Gamma Power ◽  
Brain Computer Interfacing

Brain–computer interfaces (BCIs) for communication can be nonintuitive, often requiring the performance of hand motor imagery or some other conversation-irrelevant task. In this paper, electroencephalography (EEG) was used to develop two intuitive online BCIs based solely on covert speech. The goal of the first BCI was to differentiate between 10[Formula: see text]s of mental repetitions of the word “no” and an equivalent duration of unconstrained rest. The second BCI was designed to discern between 10[Formula: see text]s each of covert repetition of the words “yes” and “no”. Twelve participants used these two BCIs to answer yes or no questions. Each participant completed four sessions, comprising two offline training sessions and two online sessions, one for testing each of the BCIs. With a support vector machine and a combination of spectral and time-frequency features, an average accuracy of [Formula: see text] was reached across participants in the online classification of no versus rest, with 10 out of 12 participants surpassing the chance level (60.0% for [Formula: see text]). The online classification of yes versus no yielded an average accuracy of [Formula: see text], with eight participants exceeding the chance level. Task-specific changes in EEG beta and gamma power in language-related brain areas tended to provide discriminatory information. To our knowledge, this is the first report of online EEG classification of covert speech. Our findings support further study of covert speech as a BCI activation task, potentially leading to the development of more intuitive BCIs for communication.

Classification Algorithms for EEG-Based Brain-Computer Interface

2019 ◽  
pp. 52-73
Author(s):  
Sravanth Kumar Ramakuri ◽  
Chinmay Chakraboirty ◽  
Anudeep Peddi ◽  
Bharat Gupta
Keyword(s):  
Brain Activity ◽  
Biomedical Signal Processing ◽  
Computer Interface ◽  
Eeg Signals ◽  
Large Sets ◽  
Eeg Data ◽  
Important Measurement ◽  
The Brain

In recent years, a vast research is concentrated towards the development of electroencephalography (EEG)-based human-computer interface in order to enhance the quality of life for medical as well as nonmedical applications. The EEG is an important measurement of brain activity and has great potential in helping in the diagnosis and treatment of mental and brain neuro-degenerative diseases and abnormalities. In this chapter, the authors discuss the classification of EEG signals as a key issue in biomedical research for identification and evaluation of the brain activity. Identification of various types of EEG signals is a complicated problem, requiring the analysis of large sets of EEG data. Representative features from a large dataset play an important role in classifying EEG signals in the field of biomedical signal processing. So, to reduce the above problem, this research uses three methods to classify through feature extraction and classification schemes.

Epileptic seizure detection on EEG signals using machine learning techniques and advanced preprocessing methods

2020 ◽  
Vol 65 (1) ◽  
pp. 33-50 ◽  
Author(s):  
Chahira Mahjoub ◽  
Régine Le Bouquin Jeannès ◽  
Tarek Lajnef ◽  
Abdennaceur Kachouri
Keyword(s):  
Epileptic Seizure ◽  
High Performance ◽  
Visual Analysis ◽  
Binary Classification ◽  
Seizure Detection ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Intrinsic Mode Functions ◽  
Eeg Signals ◽  
Epileptic Seizure Detection

AbstractElectroencephalography (EEG) is a common tool used for the detection of epileptic seizures. However, the visual analysis of long-term EEG recordings is characterized by its subjectivity, time-consuming procedure and its erroneous detection. Various epileptic seizure detection algorithms have been proposed to deal with such issues. In this study, a novel automatic seizure-detection approach is proposed. Three different strategies are suggested to the user whereby he/she could choose the appropriate one for a given classification problem. Indeed, the feature extraction step, including both linear and nonlinear measures, is performed either directly from the EEG signals, or from the derived sub-bands of tunable-Q wavelet transform (TQWT), or even from the intrinsic mode functions (IMFs) of multivariate empirical mode decomposition (MEMD). The classification procedure is executed using a support vector machine (SVM). The performance of the proposed method is evaluated through a publicly available database from which six binary classification cases are formulated to discriminate between healthy, seizure and non-seizure EEG signals. Our results show high performance in terms of accuracy (ACC), sensitivity (SEN) and specificity (SPE) compared to the state-of-the-art approaches. Thus, the proposed approach for automatic seizure detection can be considered as a valuable alternative to existing methods, able to alleviate the overload of visual analysis and accelerate the seizure detection.

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