Classification of EEG signals recorded during right/left hand movement imagery using Fourier Transform based features

2011 ◽  
Author(s):  
Onder Aydemir ◽  
Temel Kayikcioglu
Keyword(s):  
Fourier Transform ◽  
Hand Movement ◽  
Eeg Signals ◽  
Left Hand ◽  
Movement Imagery

scholarly journals Motor Imagery EEG Classification Using Capsule Networks

Sensors ◽  
2019 ◽  
Vol 19 (13) ◽  
pp. 2854 ◽  
Author(s):  
Kwon-Woo Ha ◽  
Jin-Woo Jeong
Keyword(s):  
Motor Imagery ◽  
Learning Approaches ◽  
Eeg Signals ◽  
Left Hand ◽  
Computer Interfaces ◽  
Conventional Machine ◽  
Short Time ◽  
Electroencephalogram Eeg ◽  
Target Data

Various convolutional neural network (CNN)-based approaches have been recently proposed to improve the performance of motor imagery based-brain-computer interfaces (BCIs). However, the classification accuracy of CNNs is compromised when target data are distorted. Specifically for motor imagery electroencephalogram (EEG), the measured signals, even from the same person, are not consistent and can be significantly distorted. To overcome these limitations, we propose to apply a capsule network (CapsNet) for learning various properties of EEG signals, thereby achieving better and more robust performance than previous CNN methods. The proposed CapsNet-based framework classifies the two-class motor imagery, namely right-hand and left-hand movements. The motor imagery EEG signals are first transformed into 2D images using the short-time Fourier transform (STFT) algorithm and then used for training and testing the capsule network. The performance of the proposed framework was evaluated on the BCI competition IV 2b dataset. The proposed framework outperformed state-of-the-art CNN-based methods and various conventional machine learning approaches. The experimental results demonstrate the feasibility of the proposed approach for classification of motor imagery EEG signals.

EEG efficient classification of imagined right and left hand movement using RBF kernel SVM and the joint CWT_PCA

AI & Society ◽  
2017 ◽  
Vol 33 (4) ◽  
pp. 621-629 ◽  
Author(s):  
Rihab Bousseta ◽  
Salma Tayeb ◽  
Issam El Ouakouak ◽  
Mourad Gharbi ◽  
Fakhita Regragui ◽  
...  
Keyword(s):  
Hand Movement ◽  
Left Hand ◽  
Rbf Kernel ◽  
Right And Left Hand ◽  
Kernel Svm

scholarly journals Human Emotions Classification using EEG Signals in Virtual Instrumentation Platform

2019 ◽  
Vol 8 (4) ◽  
pp. 12104-12107
Keyword(s):  
Feature Extraction ◽  
Fourier Transform ◽  
Neurological Disorders ◽  
Virtual Instrumentation ◽  
Eeg Signals ◽  
Noise Elimination ◽  
Biological Signal ◽  
Source Of Information ◽  
Emotional Condition

This article presents the frequency band classification of emotions and detection of emotional condition of a person. This is done by the analysis of the biological signal, EEG (Electroencephalogram). The EEG is considered as the main biological signal, which signifies the electrical activity of the human brain and hence becomes main source of information for studying the neurological disorders. This analysis is done using one of the popular virtual instrumentation platform LabVIEW (Laboratory Virtual Instrumentation Engineering workbench) software. The EEG signals that are used for the analysis are taken from the open source database and are undergone different stages like preprocessing for noise elimination, classification and feature extraction. The feature extraction is done by performing Fast Fourier Transform (FFT) of the signal. This analysis helps us to identify the abnormality of the person (if any) from whom the signal is taken

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