Comparison of Fractal Dimension and Wavelet Transform Methods in Classification of Stress State from EEG Signals

In this paper, we propose a system for the purpose of classifying Electroencephalography (EEG) signals associated with imagined movement of right hand and relaxation state using machine learning algorithm namely Random Forest Algorithm. The EEG dataset used in this research was created by the University of Tubingen, Germany. EEG signals associated with the imagined movement of right hand and relaxation state were processed using wavelet transform analysis with Daubechies orthogonal wavelet as the mother wavelet. After the wavelet transform analysis, eight features were extracted. Subsequently, a feature selection method based on Random Forest Algorithm was employed giving us the best features out of the eight proposed features. The feature selection stage was followed by classification stage in which eight different models combining the different features based on their importance were constructed. The optimum classification performance of 85.41% was achieved with the Random Forest classifier. This research shows that this system of classification of motor movements can be used in a Brain Computer Interface system (BCI) to mentally control a robotic device or an exoskeleton.

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Comparison of STFT and wavelet transform methods in determining epileptic seizure activity in EEG signals for real-time application

Computers in Biology and Medicine ◽

10.1016/j.compbiomed.2004.05.001 ◽

2005 ◽

Vol 35 (7) ◽

pp. 603-616 ◽

Cited By ~ 118

Author(s):

M.Kemal Kıymık ◽

İnan Güler ◽

Alper Dizibüyük ◽

Mehmet Akın

Keyword(s):

Wavelet Transform ◽

Real Time ◽

Epileptic Seizure ◽

Seizure Activity ◽

Eeg Signals ◽

Transform Methods ◽

Real Time Application

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Adaptive Neuro-fuzzy Inference System for Classification of EEG Signals Using Fractal Dimension

2009 Third UKSim European Symposium on Computer Modeling and Simulation ◽

10.1109/ems.2009.65 ◽

2009 ◽

Cited By ~ 5

Author(s):

Maryam Vatankhah ◽

Mehdi Yaghubi

Keyword(s):

Fractal Dimension ◽

Fuzzy Inference System ◽

Fuzzy Inference ◽

Eeg Signals ◽

Inference System ◽

Neuro Fuzzy

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Classification of Motor Functions from Electroencephalogram (EEG) Signals Based on an Integrated Method Comprised of Common Spatial Pattern and Wavelet Transform Framework

Sensors ◽

10.3390/s19224878 ◽

2019 ◽

Vol 19 (22) ◽

pp. 4878 ◽

Cited By ~ 1

Author(s):

Norashikin Yahya ◽

Huwaida Musa ◽

Zhong Yi Ong ◽

Irraivan Elamvazuthi

Keyword(s):

Wavelet Transform ◽

Continuous Wavelet ◽

Eeg Signal ◽

Parietal Region ◽

Eeg Signals ◽

Common Spatial Pattern ◽

Band Pass ◽

Rgb Images ◽

Electroencephalogram Eeg

In this work, an algorithm for the classification of six motor functions from an electroencephalogram (EEG) signal that combines a common spatial pattern (CSP) filter and a continuous wavelet transform (CWT), is investigated. The EEG data comprise six grasp-and-lift events, which are used to investigate the potential of using EEG as input signals with brain computer interface devices for controlling prosthetic devices for upper limb movement. Selected EEG channels are the ones located over the motor cortex, C3, Cz and C4, as well as at the parietal region, P3, Pz and P4. In general, the proposed algorithm includes three main stages, band pass filtering, CSP filtering, and wavelet transform and training on GoogLeNet for feature extraction, feature learning and classification. The band pass filtering is performed to select the EEG signal in the band of 7 Hz to 30 Hz while eliminating artifacts related to eye blink, heartbeat and muscle movement. The CSP filtering is applied on two-class EEG signals that will result in maximizing the power difference between the two-class dataset. Since CSP is mathematically developed for two-class events, the extension to the multiclass paradigm is achieved by using the approach of one class versus all other classes. Subsequently, continuous wavelet transform is used to convert the band pass and CSP filtered signals from selected electrodes to scalograms which are then converted to images in grayscale format. The three scalograms from the motor cortex regions and the parietal region are then combined to form two sets of RGB images. Next, these RGB images become the input to GoogLeNet for classification of the motor EEG signals. The performance of the proposed classification algorithm is evaluated in terms of precision, sensitivity, specificity, accuracy with average values of 94.8%, 93.5%, 94.7%, 94.1%, respectively, and average area under the receiver operating characteristic (ROC) curve equal to 0.985. These results indicate a good performance of the proposed algorithm in classifying grasp-and-lift events from EEG signals.

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