scholarly journals Multiscale Permutation Lempel–Ziv Complexity Measure for Biomedical Signal Analysis: Interpretation and Application to Focal EEG Signals

Entropy ◽  
2021 ◽  
Vol 23 (7) ◽  
pp. 832
Author(s):  
Marta Borowska
Keyword(s):  
Mean Value ◽  
Complexity Measure ◽  
Support Vector ◽  
Svm Classifier ◽  
Eeg Signals ◽  
Biomedical Signal ◽  
The Mean ◽  
Electroencephalogram Eeg ◽  
Main Material

This paper analyses the complexity of electroencephalogram (EEG) signals in different temporal scales for the analysis and classification of focal and non-focal EEG signals. Futures from an original multiscale permutation Lempel–Ziv complexity measure (MPLZC) were obtained. MPLZC measure combines a multiscale structure, ordinal analysis, and permutation Lempel–Ziv complexity for quantifying the dynamic changes of an electroencephalogram (EEG). We also show the dependency of MPLZC on several straight-forward signal processing concepts, which appear in biomedical EEG activity via a set of synthetic signals. The main material of the study consists of EEG signals, which were obtained from the Bern-Barcelona EEG database. The signals were divided into two groups: focal EEG signals (n = 100) and non-focal EEG signals (n = 100); statistical analysis was performed by means of non-parametric Mann–Whitney test. The mean value of MPLZC results in the non-focal group are significantly higher than those in the focal group for scales above 1 (p < 0.05). The result indicates that the non-focal EEG signals are more complex. MPLZC feature sets are used for the least squares support vector machine (LS-SVM) classifier to classify into the focal and non-focal EEG signals. Our experimental results confirmed the usefulness of the MPLZC method for distinguishing focal and non-focal EEG signals with a classification accuracy of 86%.

CLASSIFICATION OF FOCAL AND NONFOCAL EEG SIGNALS USING FEATURES DERIVED FROM FOURIER-BASED RHYTHMS

2017 ◽  
Vol 17 (07) ◽  
pp. 1740002 ◽  
Author(s):  
PUSHPENDRA SINGH ◽  
RAM BILAS PACHORI
Keyword(s):  
Filter Bank ◽  
Support Vector ◽  
Svm Classifier ◽  
Automated Classification ◽  
Eeg Signals ◽  
Input Feature ◽  
The Mean ◽  
A New Technique ◽  
Electroencephalogram Eeg

We propose a new technique for the automated classification of focal and nonfocal electroencephalogram (EEG) signals using Fourier-based rhythms in this paper. The EEG rhythms, namely, delta, theta, alpha, beta and gamma, are obtained using the discrete Fourier transform (DFT)-based filter bank applied on EEG signals. The mean-frequency (MF) and root-mean-square (RMS) bandwidth features are derived using DFT-based computation on rhythms of EEG signals and their envelopes. These derived features, namely, MF and RMS bandwidths have been provided as an input feature set for the classification of focal and nonfocal EEG signals using a least-squares support vector machine (LS-SVM) classifier. We present experimental results obtained from the publicly available database in order to demonstrate the effectiveness of the proposed feature sets for the automated classification of the focal and nonfocal classes of EEG signals. The obtained classification accuracy in this dataset for the automated classification of focal and nonfocal 50 pairs and 750 pairs of EEG signals are 89.7% and 89.52%, respectively.

scholarly journals Wavelet based Extraction of Features from EEG Signals and Classification of Novel Emotion Recognition Using SVM and HMM Classifier and to Measure its Accuracy

2021 ◽  
Vol 36 (1) ◽  
pp. 727-732
Author(s):  
M. Mohanambal ◽  
Dr.P. Vishnu Vardhan
Keyword(s):  
Support Vector Machine ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Mean Value ◽  
Support Vector ◽  
Svm Classifier ◽  
Emotion Detection ◽  
Eeg Signals

Aim: The study aims to extract features from EEG signals and classify emotion using Support Vector Machine (SVM) and Hidden Markov Model (HMM) classifier. Materials and methods: The study was conducted using the Support Vector Machine (SVM) and Hidden Markov Model (HMM) programs to analyze and compare the recognition of emotions classified under EEG signals. The results were computed using the MATLAB algorithm. For each group, ten samples were used to compare the efficiency of SVM and HMM classifiers. Result: The study’s performance exhibits the HMM classifier’s accuracy over the SVM classifier and the emotion detection from EEG signals computed. The mean value of the HMM classifier is 52.2, and the SVM classifier is 22.4. The accuracy rate of 35% with the data features is found in HMM classifier. Conclusion: This study shows a higher accuracy level of 35% for the HMM classifier when compared with the SVM classifier. In the detection of emotions using the EEG signal. This result shows that the HMM classifier has a higher significant value of P=.001 < P=.005 than the SVM classifier.

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.

Classification of Diffusion Tensor Metrics for the Diagnosis of a Myelopathic Cord Using Machine Learning

2018 ◽  
Vol 28 (02) ◽  
pp. 1750036 ◽  
Author(s):  
Shuqiang Wang ◽  
Yong Hu ◽  
Yanyan Shen ◽  
Hanxiong Li
Keyword(s):  
Machine Learning ◽  
Surgical Planning ◽  
Diffusion Tensor ◽  
Mean Value ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Svm Classifier ◽  
Control Groups ◽  
Diffusion Tensor Imaging Dti

In this study, we propose an automated framework that combines diffusion tensor imaging (DTI) metrics with machine learning algorithms to accurately classify control groups and groups with cervical spondylotic myelopathy (CSM) in the spinal cord. The comparison between selected voxel-based classification and mean value-based classification were performed. A support vector machine (SVM) classifier using a selected voxel-based dataset produced an accuracy of 95.73%, sensitivity of 93.41% and specificity of 98.64%. The efficacy of each index of diffusion for classification was also evaluated. Using the proposed approach, myelopathic areas in CSM are detected to provide an accurate reference to assist spine surgeons in surgical planning in complicated cases.

scholarly journals Irrigation Mapping Using Sentinel-1 Time Series at Field Scale

2018 ◽  
Vol 10 (9) ◽  
pp. 1495 ◽  
Author(s):  
Qi Gao ◽  
Mehrez Zribi ◽  
Maria Escorihuela ◽  
Nicolas Baghdadi ◽  
Pere Segui
Keyword(s):  
Time Series ◽  
Weather Conditions ◽  
Mean Value ◽  
Support Vector ◽  
Irrigated Crops ◽  
The Mean ◽  
Sar Data ◽  
Ground Truthing ◽  
Agricultural Site

The recently launched Sentinel-1 satellite with a Synthetic Aperture Radar (SAR) sensor onboard offers a powerful tool for irrigation monitoring under various weather conditions, with high spatial and temporal resolution. This research discusses the potential of different metrics calculated from the Sentinel-1 time series for mapping irrigated fields. A methodology for irrigation mapping using SAR data is proposed. The study is performed using VV (vertical–vertical) and VH (vertical–horizontal) polarizations over an agricultural site in Urgell, Catalunya (Spain). With field segmentation information from SIGPAC (the Geographic Information System for Agricultural Parcels), the backscatter intensities are averaged within each field. From the Sentinel-1 time series for each field, the statistics and metrics, including the mean value, the variance of the signal, the correlation length, and the fractal dimension, are analyzed. With the Support Vector Machine (SVM), the classification of irrigated crops, irrigated trees, and non-irrigated fields is performed with the metrics vector. The results derived from the SVM are validated with ground truthing from SIGPAC over the whole study area, with a good overall accuracy of 81.08%. Random Forest (RF) machine classification is also tested in this study, which gives an accuracy of around 82.2% when setting the tree depth at three. The methodology is based only on SAR data, which makes it applicable to all areas, even with frequent cloud cover, but this method may be less robust when irrigation is less dominated to soil moisture change.

Automated Classification of Focal and Non-Focal EEG Signals Based on Bivariate Empirical Mode Decomposition

2018 ◽  
pp. 13-33 ◽  
Author(s):  
Rajeev Sharma ◽  
Ram Bilas Pachori
Keyword(s):  
Empirical Mode Decomposition ◽  
Support Vector ◽  
Automated Classification ◽  
Intrinsic Mode Functions ◽  
Eeg Signals ◽  
Mode Decomposition ◽  
Electroencephalogram Eeg ◽  
Aided Diagnosis ◽  
Bivariate Empirical Mode Decomposition

The chapter presents a new approach of computer aided diagnosis of focal electroencephalogram (EEG) signals by applying bivariate empirical mode decomposition (BEMD). Firstly, the focal and non-focal EEG signals are decomposed using the BEMD, which results in intrinsic mode functions (IMFs) corresponding to each signal. Secondly, bivariate bandwidths namely, amplitude bandwidth, precession bandwidth, and deformation bandwidth are computed for each obtained IMF. Interquartile range (IQR) values of bivariate bandwidths of IMFs are employed as the features for classification. In order to perform classification least squares support vector machine (LS-SVM) is used. The results of the experiment suggest that the computed bivariate bandwidths are significantly useful to discriminate focal EEG signals. The resultant classification accuracy obtained using proposed methodology, applied on the Bern-Barcelona EEG database, is 84.01%. The obtained results are encouraging and the proposed methodology can be helpful for identification of epileptogenic focus.

Classifying Electroencephalogram (EEG) Signals Using BAT-SVM Classifier for Detecting Epilepsy

2021 ◽  
Vol 12 (3) ◽  
pp. 96-115
Author(s):  
Manal Tantawi ◽  
Aya Naser ◽  
Howida Shedeed ◽  
Mohammed Fahmy Tolba
Keyword(s):  
Support Vector Machines ◽  
Epileptic Seizures ◽  
Bat Algorithm ◽  
Line Length ◽  
Support Vector ◽  
Svm Classifier ◽  
Eeg Signals ◽  
Vector Machines ◽  
Electroencephalogram Eeg ◽  
Source Of Information

Electroencephalogram (EEG) signals are a valuable source of information for detecting epileptic seizures. However, monitoring EEG for long periods of time is very exhausting and time consuming. Thus, detecting epilepsy in EEG signals automatically is highly appreciated. In this study, three classes, namely normal, interictal (out of seizure time), and ictal (during seizure), are considered. Moreover, a comparative study is provided for the efficient features in literature resulting in a suggested combination of only three discriminative features, namely R'enyi entropy, line length, and energy. These features are calculated from each of the EEG sub-bands. Finally, support vector machines (SVM) classifier optimized using BAT algorithm (BAT-SVM) is introduced by this study for discriminating between the three classes. Experiments were conducted using Andrzejak database. The accomplished experiments and comparisons in this study emphasize the superiority of the proposed BAT-SVM along with the suggested feature set in achieving the best results.

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 Developing a Motor Imagery-Based Real-Time Asynchronous Hybrid BCI Controller for a Lower-Limb Exoskeleton

Sensors ◽  
2020 ◽  
Vol 20 (24) ◽  
pp. 7309
Author(s):  
Junhyuk Choi ◽  
Keun Tae Kim ◽  
Ji Hyeok Jeong ◽  
Laehyun Kim ◽  
Song Joo Lee ◽  
...  
Keyword(s):  
Real Time ◽  
Motor Imagery ◽  
Lower Limb ◽  
Neurological Disorders ◽  
Support Vector ◽  
Svm Classifier ◽  
Eeg Signals ◽  
Common Spatial Pattern ◽  
Lower Limb Exoskeleton ◽  
Electroencephalogram Eeg

This study aimed to develop an intuitive gait-related motor imagery (MI)-based hybrid brain-computer interface (BCI) controller for a lower-limb exoskeleton and investigate the feasibility of the controller under a practical scenario including stand-up, gait-forward, and sit-down. A filter bank common spatial pattern (FBCSP) and mutual information-based best individual feature (MIBIF) selection were used in the study to decode MI electroencephalogram (EEG) signals and extract a feature matrix as an input to the support vector machine (SVM) classifier. A successive eye-blink switch was sequentially combined with the EEG decoder in operating the lower-limb exoskeleton. Ten subjects demonstrated more than 80% accuracy in both offline (training) and online. All subjects successfully completed a gait task by wearing the lower-limb exoskeleton through the developed real-time BCI controller. The BCI controller achieved a time ratio of 1.45 compared with a manual smartwatch controller. The developed system can potentially be benefit people with neurological disorders who may have difficulties operating manual control.

scholarly journals Classification of Relaxation and Concentration Mental States with EEG

Information ◽  
2021 ◽  
Vol 12 (5) ◽  
pp. 187
Author(s):  
Shingchern D. You
Keyword(s):  
Mental States ◽  
Support Vector ◽  
Svm Classifier ◽  
Eeg Signals ◽  
Consumer Market ◽  
Time Frequency ◽  
Average Accuracy ◽  
Spectral Components ◽  
Machine Interface

In this paper, we study the use of EEG (Electroencephalography) to classify between concentrated and relaxed mental states. In the literature, most EEG recording systems are expensive, medical-graded devices. The expensive devices limit the availability in a consumer market. The EEG signals are obtained from a toy-grade EEG device with one channel of output data. The experiments are conducted in two runs, with 7 and 10 subjects, respectively. Each subject is asked to silently recite a five-digit number backwards given by the tester. The recorded EEG signals are converted to time-frequency representations by the software accompanying the device. A simple average is used to aggregate multiple spectral components into EEG bands, such as α, β, and γ bands. The chosen classifiers are SVM (support vector machine) and multi-layer feedforward network trained individually for each subject. Experimental results show that features, with α+β+γ bands and bandwidth 4 Hz, the average accuracy over all subjects in both runs can reach more than 80% and some subjects up to 90+% with the SVM classifier. The results suggest that a brain machine interface could be implemented based on the mental states of the user even with the use of a cheap EEG device.

Sign in / Sign up

Export Citation Format

Share Document