Results 241 to 250 of about 3,887,920 (279)

Time-domain exponential energy for epileptic EEG signal classification

Neuroscience Letters, 2019
Automatic classification and prediction of epileptic electroencephalogram (EEG) signal are of great concern to the research community due to its non-stationary and non-linear properties.
Fasil O.K, Rajesh R
exaly   +2 more sources

Automated EEG signal classification using chaotic local binary pattern

Expert systems with applications, 2021
Background Electroencephalography (EEG) signals are the electrical signals which depicts the brain's neuronal activities. The EEG signals inherently have nondeterministic patterns.
T. Tuncer, M. Kutlu Sengul, U. Acharya
semanticscholar   +2 more sources

Reputation-Based Federated Learning Defense to Mitigate Threats in EEG Signal Classification

International Conference on Computer and Automation Engineering, 2023
This paper presents a reputation-based threat mitigation framework that defends potential security threats in electroencephalogram (EEG) signal classification during model aggregation of Federated Learning.
Zhibo Zhang, Pengfei Li, Ahmed Y. Al Hammadi, Fusen Guo, Ernesto Damiani, C. Yeun   +5 more
semanticscholar   +1 more source

Ensemble Regularized Common Spatio-Spectral Pattern (ensemble RCSSP) model for motor imagery-based EEG signal classification

Comput. Biol. Medicine, 2021
The Brain-Computer interface system provides a communication path among the brain and computer, and recently, it is the subject of increasing attention. One of the most common paradigms of BCI systems is motor imagery.
Mohammad Norizadeh Cherloo, Homa Kashefi Amiri, M. Daliri   +2 more
semanticscholar   +1 more source

Comparison of linear, nonlinear, and feature selection methods for EEG signal classification

IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2003
Charles Anderson, Michael H Thaut, M H Thaut   +2 more
exaly   +2 more sources

Motor Imagery EEG Signal Classification based on Deep Transfer Learning

2021 IEEE 34th International Symposium on Computer-Based Medical Systems (CBMS), 2021
Deep transfer learning (DTL) has developed rapidly in the field of motor imagery (MI) on brain-computer interface (BCI) in recent years. DTL utilizes deep neural networks with strong generalization capabilities as the pre-training framework and ...
Ming-fei Wei, Rui Yang, Mengjie Huang
semanticscholar   +1 more source

EEG signal classification of imagined speech based on Riemannian distance of correntropy spectral density

Biomedical Signal Processing and Control, 2020
Several current brain–computer interface (BCI) systems are based on imagined speech. This means that these systems are controlled only by thinking about a speech without verbally expressing it. Imagined speech recognition using electroencephalogram (EEG)
Mohamad Amin Bakhshali, M. Khademi, Abbas Ebrahimi-Moghadam, S. Moghimi   +3 more
semanticscholar   +1 more source

EEG signal classification using universum support vector machine

Expert systems with applications, 2018
Support vector machine (SVM) has been used widely for classification of electroencephalogram (EEG) signals for the diagnosis of neurological disorders such as epilepsy and sleep disorders.
Bharat Richhariya, M. Tanveer
semanticscholar   +1 more source

EEG Signal Classification Using Convolutional Neural Networks on Combined Spatial and Temporal Dimensions for BCI Systems

Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2020
EEG signal classification is an important task to build an accurate Brain Computer Interface (BCI) system. Many machine learning and deep learning approaches have been used to classify EEG signals.
Ayman Anwar, A. Eldeib
semanticscholar   +1 more source