scholarly journals Parametric and Nonparametric EEG Analysis for the Evaluation of EEG Activity in Young Children with Controlled Epilepsy

2008 ◽  
Vol 2008 ◽  
pp. 1-15 ◽  
Author(s):  
Vangelis Sakkalis ◽  
Tracey Cassar ◽  
Michalis Zervakis ◽  
Kenneth P. Camilleri ◽  
Simon G. Fabri ◽  
...  
Keyword(s):  
Arma Model ◽  
Spectral Features ◽  
Control Task ◽  
Eeg Signals ◽  
Linear Discriminant ◽  
The Past ◽  
Representation Techniques ◽  
Important Evidence ◽  
Linear Discriminant Classifier

There is an important evidence of differences in the EEG frequency spectrum of control subjects as compared to epileptic subjects. In particular, the study of children presents difficulties due to the early stages of brain development and the various forms of epilepsy indications. In this study, we consider children that developed epileptic crises in the past but without any other clinical, psychological, or visible neurophysiological findings. The aim of the paper is to develop reliable techniques for testing if such controlled epilepsy induces related spectral differences in the EEG. Spectral features extracted by using nonparametric, signal representation techniques (Fourier and wavelet transform) and a parametric, signal modeling technique (ARMA) are compared and their effect on the classification of the two groups is analyzed. The subjects performed two different tasks: a control (rest) task and a relatively difficult math task. The results show that spectral features extracted by modeling the EEG signals recorded from individual channels by an ARMA model give a higher discrimination between the two subject groups for the control task, where classification scores of up to 100% were obtained with a linear discriminant classifier.

FEATURES EXTRACTION METHOD FOR BRAIN-MACHINE COMMUNICATION BASED ON THE EMPIRICAL MODE DECOMPOSITION

2013 ◽  
Vol 25 (06) ◽  
pp. 1350058 ◽  
Author(s):  
Pablo F. Diez ◽  
Vicente A. Mut ◽  
Eric Laciar ◽  
Abel Torres ◽  
Enrique M. Avila Perona
Keyword(s):  
Empirical Mode Decomposition ◽  
Communication System ◽  
Brain Activity ◽  
Features Extraction ◽  
Eeg Signals ◽  
Linear Discriminant ◽  
Mode Decomposition ◽  
Machine Communication ◽  
Mental Tasks

A brain-machine interface (BMI) is a communication system that translates human brain activity into commands, and then these commands are conveyed to a machine or a computer. It is proposes a technique for features extraction from electroencephalographic (EEG) signals and afterward, their classification on different mental tasks. The empirical mode decomposition (EMD) is a method capable of processing non-stationary and nonlinear signals, as the EEG. The EMD was applied on EEG signals of seven subjects performing five mental tasks. Six features were computed, namely, root mean square (RMS), variance, Shannon entropy, Lempel–Ziv complexity value, and central and maximum frequencies. In order to reduce the dimensionality of the feature vector, the Wilks' lambda (WL) parameter was used for the selection of the most important variables. The classification of mental tasks was performed using linear discriminant analysis (LDA) and neural networks (NN). Using this method, the average classification over all subjects in database is 91 ± 5% and 87 ± 5% using LDA and NN, respectively. Bit rate was ranging from 0.24 bits/trial up to 0.84 bits/trial. The proposed method allows achieving higher performances in the classification of mental tasks than other traditional methods using the same database. This represents an improvement in the brain-machine communication system.

scholarly journals Deep Learning-Based Classification of Fine Hand Movements from Low Frequency EEG

2021 ◽  
Vol 13 (5) ◽  
pp. 103
Author(s):  
Giulia Bressan ◽  
Giulia Cisotto ◽  
Gernot R. Müller-Putz ◽  
Selina Christin Wriessnegger
Keyword(s):  
Low Frequency ◽  
Classification Performance ◽  
Hand Movements ◽  
Eeg Signals ◽  
Low Frequencies ◽  
Linear Discriminant ◽  
Comparable Performance ◽  
Research Challenge ◽  
Electroencephalogram Eeg

The classification of different fine hand movements from electroencephalogram (EEG) signals represents a relevant research challenge, e.g., in BCI applications for motor rehabilitation. Here, we analyzed two different datasets where fine hand movements (touch, grasp, palmar, and lateral grasp) were performed in a self-paced modality. We trained and tested a newly proposed CNN, and we compared its classification performance with two well-established machine learning models, namely, shrinkage-linear discriminant analysis (LDA) and Random Forest (RF). Compared to previous literature, we included neuroscientific evidence, and we trained our Convolutional Neural Network (CNN) model on the so-called movement-related cortical potentials (MRCPs). They are EEG amplitude modulations at low frequencies, i.e., (0.3,3) Hz that have been proved to encode several properties of the movements, e.g., type of grasp, force level, and speed. We showed that CNN achieved good performance in both datasets (accuracy of 0.70±0.11 and 0.64±0.10, for the two datasets, respectively), and they were similar or superior to the baseline models (accuracy of 0.68±0.10 and 0.62±0.07 with sLDA; accuracy of 0.70±0.15 and 0.61±0.07 with RF, with comparable performance in precision and recall). In addition, compared to the baseline, our CNN requires a faster pre-processing procedure, paving the way for its possible use in online BCI applications.

scholarly journals Comparison of Features for Movement Prediction from Single-Trial Movement-Related Cortical Potentials in Healthy Subjects and Stroke Patients

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Ernest Nlandu Kamavuako ◽  
Mads Jochumsen ◽  
Imran Khan Niazi ◽  
Kim Dremstrup
Keyword(s):  
Template Matching ◽  
Spectral Features ◽  
Single Trial ◽  
Eeg Signals ◽  
Stroke Patients ◽  
Movement Execution ◽  
Linear Discriminant ◽  
Temporal Features ◽  
Electroencephalogram Eeg ◽  
Movement Intention

Detection of movement intention from the movement-related cortical potential (MRCP) derived from the electroencephalogram (EEG) signals has shown to be important in combination with assistive devices for effective neurofeedback in rehabilitation. In this study, we compare time and frequency domain features to detect movement intention from EEG signals prior to movement execution. Data were recoded from 24 able-bodied subjects, 12 performing real movements, and 12 performing imaginary movements. Furthermore, six stroke patients with lower limb paresis were included. Temporal and spectral features were investigated in combination with linear discriminant analysis and compared with template matching. The results showed that spectral features were best suited for differentiating between movement intention and noise across different tasks. The ensemble average across tasks when using spectral features was (error = 3.4 ± 0.8%, sensitivity = 97.2 ± 0.9%, and specificity = 97 ± 1%) significantly better (P<0.01) than temporal features (error = 15 ± 1.4%, sensitivity: 85 ± 1.3%, and specificity: 84 ± 2%). The proposed approach also (error = 3.4 ± 0.8%) outperformed template matching (error = 26.9 ± 2.3%) significantly (P>0.001). Results imply that frequency information is important for detecting movement intention, which is promising for the application of this approach to provide patient-driven real-time neurofeedback.

TIME-FREQUENCY PROCESSING METHOD OF EPILEPTIC EEG SIGNALS

2015 ◽  
Vol 27 (02) ◽  
pp. 1550015 ◽  
Author(s):  
Assya Bousbia-Salah ◽  
Malika Talha-Kedir
Keyword(s):  
Support Vector Machine ◽  
Principal Component ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Statistical Features ◽  
Eeg Signals ◽  
Linear Discriminant ◽  
Time Frequency ◽  
Eeg Recordings

Wavelet transform decomposition of electroencephalogram (EEG) signals has been widely used for the analysis and detection of epileptic seizure of patients. However, the classification of EEG signals is still challenging because of high nonstationarity and high dimensionality. The aim of this work is an automatic classification of the EEG recordings by using statistical features extraction and support vector machine. From a real database, two sets of EEG signals are used: EEG recorded from a healthy person and from an epileptic person during epileptic seizures. Three important statistical features are computed at different sub-bands discrete wavelet and wavelet packet decomposition of EEG recordings. In this study, to select the best wavelet for our application, five wavelet basis functions are considered for processing EEG signals. After reducing the dimension of the obtained data by linear discriminant analysis and principal component analysis (PCA), feature vectors are used to model and to train the efficient support vector machine classifier. In order to show the efficiency of this approach, the statistical classification performances are evaluated, and a rate of 100% for the best classification accuracy is obtained and is compared with those obtained in other studies for the same dataset. However, this method is not meant to replace the clinician but can assist him for his diagnosis and reinforce his decision.

EMD-Based Temporal and Spectral Features for the Classification of EEG Signals Using Supervised Learning

2016 ◽  
Vol 24 (1) ◽  
pp. 28-35 ◽  
Author(s):  
Farhan Riaz ◽  
Ali Hassan ◽  
Saad Rehman ◽  
Imran Khan Niazi ◽  
Kim Dremstrup
Keyword(s):  
Supervised Learning ◽  
Spectral Features ◽  
Eeg Signals

scholarly journals The Promise of Deep Learning for BCIs: Classification of Motor Imagery EEG using Convolutional Neural Network

2021 ◽  
Author(s):  
Navneet Tibrewal ◽  
Nikki Leeuwis ◽  
Maryam Alimardani
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Motor Imagery ◽  
Binary Classification ◽  
Activity Patterns ◽  
Mental Process ◽  
Eeg Signals ◽  
Linear Discriminant

Motor Imagery (MI) is a mental process by which an individual rehearses body movements without actually performing physical actions. Motor Imagery Brain-Computer Interfaces (MI-BCIs) are AI-driven systems that capture brain activity patterns associated with this mental process and convert them into commands for external devices. Traditionally, MI-BCIs operate on Machine Learning (ML) algorithms, which require extensive signal processing and feature engineering to extract changes in sensorimotor rhythms (SMR). However, in recent years, Deep Learning (DL) models have gained popularity for EEG classification as they provide a solution for automatic extraction of spatio-temporal features in the signals. In this study, EEG signals from 54 subjects who performed a MI task of left- or right-hand grasp was employed to compare the performance of two MI-BCI classifiers; a ML approach vs. a DL approach. In the ML approach, Common Spatial Patterns (CSP) was used for feature extraction and then Linear Discriminant Analysis (LDA) model was employed for binary classification of the MI task. In the DL approach, a Convolutional Neural Network (CNN) model was constructed on the raw EEG signals. The mean classification accuracies achieved by the CNN and CSP+LDA models were 69.42% and 52.56%, respectively. Further analysis showed that the DL approach improved the classification accuracy for all subjects within the range of 2.37 to 28.28% and that the improvement was significantly stronger for low performers. Our findings show promise for employment of DL models in future MI-BCI systems, particularly for BCI inefficient users who are unable to produce desired sensorimotor patterns for conventional ML approaches.

Classification of EEG Signals using Genetic Programming

2017 ◽  
Author(s):  
Marco Antonio Meggiolaro ◽  
Felipe Rebelo Lopes
Keyword(s):  
Genetic Programming ◽  
Eeg Signals
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