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....
High accuracy for the classification of electroencephalogram (EEG) signal is an important basis for a brain-computer interface (BCI) system. In this paper, we proposed a novel approach to enhance the classification performance in identifying EEG signals, which classify EEG by combining multi-scale ...
This paper explores the potential of a Brain-Computer Interface (BCI) system for recognizing Telugu vocal and sub-vocal vowels, intending to improve communication for Telugu-speaking patients with neurodegenerative disorders. We propose classifying Electroencephalogram (EEG) signals gener-ated when Telugu ...
Classification of EEG signals using normal inverse Gaussian parameters in the dual-tree complex wavelet transform domain for seizure detection. Signal Image Video Process 2016; 10: 259-266.Das, A. B., Bhuiyan, M. I. H., & Alam, S. M. S. (2016). Classifi- cation of EEG signals ...
feature selection method based on one-way anova, along with high performance machine learning classifiers for the classification of eeg signals in normal and epileptic eeg signals. in addition, the authors also present new methods of feature extraction, including singular spect...
This section reviews related work in automatic systems for epileptic EEG signal classification. These automatic systems are made up of two main modules. Firstly, a feature extractor preprocesses the EEG signals and obtains the main signal characteristics, and secondly, these characteristics are used as...
along with high performance machine learning classifiers for the classification of EEG signals in normal and epileptic EEG signals. In addition, the authors also present new methods of feature extraction, including Singular Spectrum-Empirical Wavelet Transform (SSEWT) for improved classification of seizure...
networkclassificationofEEGsignalsbyusingARmodelwith MLEpreprocessing[J].NeuralNetwork200518985- 997. @@[3] ChisciLMavinoAPerferiGetal. Real-TimeEpileptic SeizurePrediction Using AR Models and Support Vector Machines[J].IEEETransonBiomedEng2010571124- 1132. @@[4] UbeyliED. Leastsquaressupportvector...
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 [...
(ECoG) signals, convey the information related to mental activities. Hence the essential problem of identifying mental tasks is to classify the recorded brain electrical signals into different classes, each of which corresponds to a mental task. A well-known EEG database was experimented with in...