D. Upadhyay, "Classification of EEG Signals under Different Mental Tasks Using Wavelet Transform and Neural Network with One Step Secant Algorithm," International Journal of Scientific Engineering and Technology, vol. 2, no. 4, pp. 256- 259, 2013....
The analysis of electroencephalogram (EEG) signal is a low-cost and effective technique to examine electrical activity of the brain and diagnose brain diseases in the Brain Computer Interface (BCI) applications. Classification of EEG signals is an important task in BCI applications. This paper invest...
Classification of EEG signals using neural network and logistic regression Epileptic seizures are manifestations of epilepsy. Careful analyses of the electroencephalograph (EEG) records can provide valuable insight and improved un... A Subasi,Ergun Erelebi - 《Computer Methods & Programs in Biomedicine...
摘要: Classification of electroencephalogram (EEG) signals is an important task in the brain computer interface system. This paper presents two combination strategies of feature extraction on EEG signals. I关键词: EEG signal Wavelet packet decomposition Autoregressive model Approximate entropy Support ...
In the field of neuroscience, a significant challenge lies in extracting essential features from biological signals like Electroencephalography (EEG). Utilized as a non-invasive method, EEG records brain activities through metal electrodes on the scalp. The analysis of EEG data finds applications in ...
The recording of brain activity using EEG signals and its subsequent characterisation, especially for the study of consciousness, has therefore become a trending topic, as this technology solves several of the problems associated with fMRI and has been shown to be able to produce reliable results [...
Testing the Effectiveness of CNN and GNN and Exploring the Influence of Different Channels on Decoding Covert Speech from EEG Signals: CNN and GNN on Decod... In this paper, the effectiveness of two deep learning models was tested and the significance of 62 different electroencephalogram (EEG) ...
Experimental results showed that the proposed method improved the classification performance substantially and got a much less size of optimal feature subset with compared to the other methods. 展开 关键词: EEG signals wavelet packet decomposition approximation entropy feature selection particle swarm ...
Feature extraction and classification of EEG is core issues on brain computer interface.The energy entropy of different motor imagery EEG signals is used to extract features.Finally,classification of Motor Imagery EEG is performed by a method based on the statistical theory.The results show that clas...
However, the classification frameworks for EEG brain signals have been used infrequently due to the lack of a complete theoretical framework. Therefore, we present here an analysis of two different classification methods which are SVM and KNN. Four different types of emotional stimulus were presented...