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21 results for “EEG Signals”
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Research ArticleOpen accessIn the abstractDETERMINATION OF EPILEPTIC DISORDER WITH DISCRETE WAVELET TRANSFORMS AND NEURAL NETWORK CLASSIFIERS. T. Sadish Kumar, N. KasthuriNowadays Epileptic disorder is a most challenge aspects in brain activation. Electroencephalograph (EEG) is one of the popular procedures to understand the human brain condition. The activation of brain will be changed due to the symptoms of neurological disorder. We have been proposed a procedure to find epilepsy disorder, using discrete wavelet transform and neural network classifier. The EEG classification … …Journal of Computer Science, Vol. 10, Issue 1 · 12 Nov 2013 · 6,657 accessesView PDFCite
Research ArticleOpen accessIn the abstractComparative Evaluation of Adaptive Filter and Neuro-Fuzzy Filter in Artifacts Removal From Electroencephalogram SignalPaulchamy Balaiah, IlavennilaProblem statement: This study presents an effective method for removing mixed artifacts (EOG-Electro-ocular gram, ECG-Electrocardiogram, EMG-Electromyogram) from the EEG-Electroencephalogram records. The noise sources increases the difficulty in analyzing the EEG and obtaining clinical information. EEG signals are multidimensional, non-stationary (i.e., statistical properties are not invariant in time), time domain biological signals, which are not reproducible. It is supposed to contain … …American Journal of Applied Sciences, Vol. 9, Issue 10 · 15 Aug 2012 · 7,087 accessesView PDFCite
Research ArticleOpen accessIn the abstractA Qualitative Analysis of Independent Component Analysis Based Algorithms for the Removal of Artifacts from Electroencephalography SignalsBalaiah Paulchamy, Ilavennila, Jayabalan JayaProblem statement: Independent Component Analysis (ICA) based algorithms applied in the context to remove the artifacts from the EEG signals are evaluated with appropriate metrics and it compares and contrasts the performance of the different methods for such applications. The primary goal is to gain some insight into relative performance of the various methods. Approach: CA is a statistical and … …Journal of Computer Science, Vol. 8, Issue 3 · 6 Jan 2012 · 6,983 accessesView PDFCite
Research ArticleOpen accessIn the abstractUsing Invariant Translation to Denoise Electroencephalogram SignalsJanett Walters-Williams, Yan LiProblem statement: Because of the distance between the skull and the brain and their different resistivity's, Electroencephalogram (EEG) recordings on a machine is usually mixed with the activities generated within the area called noise. EEG signals have been used to diagnose major brain diseases such as Epilepsy, narcolepsy and dementia. The presence of these noises however can result in misdiagnosis, … …American Journal of Applied Sciences, Vol. 8, Issue 11 · 5 Oct 2011 · 7,508 accessesView PDFCite
Research ArticleOpen accessIn the abstractIdentification of Electrooculography Signals Frequency Energy Distribution Using Wavelet AlgorithmW.M. Bukhari, W. Daud, Rubita Sudirman, Camallil OmarProblem statement: The time frequency analysis of non-stationary signals has been the considerable research effort in recent years. Wavelet transform is one of the favored tool for the analyzing the biomedical signals. Approach: We describe the identification of Electro-Oculograph (EOG) signals of eye movement potentials by using wavelet algorithm which gives a lot of information than FFT. The capability of … …Journal of Computer Science, Vol. 7, Issue 11 · 18 Aug 2011 · 8,551 accessesView PDFCite
Research ArticleOpen accessIn the abstractClassifying Single Trail Electroencephalogram Using Gaussian Smoothened Fast Hartley Transform for Brain Computer Interface during Motor ImageryV. B. Deepa, P. Thangaraj, S. ChitraProblem statement: Brain-Computer Interface (BCI) is a emerging research area which translates the brain signals for any motor related actions into computer understandable signals by capturing the signal, processing the signal and classifying the motor imagery. This area of work finds various applications in neuroprosthetics. Mental activity leads to changes of electrophysiological signals like the Electroencephalogram (EEG) or Electrocorticogram (ECoG). … …Journal of Computer Science, Vol. 7, Issue 5 · 7 May 2011 · 6,658 accessesView PDFCite
Research ArticleOpen accessIn the abstractAdvances in Electromyogram Signal Classification to Improve the Quality of Life for the Disabled and Aged PeopleMd. R. Ahsan, Muhammad I. Ibrahimy, Othman O. KhalifaProblem statement: The social demands for the Quality Of Life (QOL) are increasing with the exponentially expanding silver generation. To improve the QOL of the disabled and elderly people, robotic researchers and biomedical engineers have been trying to combine their techniques into the rehabilitation systems. Various biomedical signals (biosignals) acquired from a specialized tissue, organ, or cell system like the … …Journal of Computer Science, Vol. 6, Issue 7 · 31 Jul 2010 · 8,813 accessesView PDFCite
Research ArticleOpen accessIn the abstractDiscrete Wavelet Transform Based Classification of Human Emotions Using Electroencephalogram SignalsMohamed RizonProblem statement: The aim of this study was to report the human emotion assessment using Electroencephalogram (EEG). Approach: An audio-visual induction based protocol was designed for inducing five different emotions (happy, surprise, fear, disgust and neutral) on 20 subjects in the age group of 19~39 years. EEG signals are recorded from 64 channels placed over entire scalp according to International … …American Journal of Applied Sciences, Vol. 7, Issue 7 · 31 Jul 2010 · 7,549 accessesView PDFCite
Research ArticleOpen accessIn the abstractSwarm Negative Selection Algorithm for Electroencephalogram Signals ClassificationNasser Omer Sahel Ba-Karait, Siti Mariyam Shamsuddin, Rubita SudirmanProblem statement: The process of epilepsy diagnosis from EEG signals by a human scorer is a very time consuming and costly task considering the large number of epileptic patients admitted to the hospitals and the large amount of data needs to be scored. Therefore, there is a strong need to automate this process. Such automated systems must rely on robust … …Journal of Computer Science, Vol. 5, Issue 12 · 31 Dec 2009 · 7,205 accessesView PDFCite
Research ArticleOpen accessIn the abstractAnomaly Detection in Electroencephalogram Signals Using Unconstrained Minimum Average Correlation Energy FilterAini Hussain, Rosniwati Ghafar, Salina Abdul Samad, Nooritawati Md TahirProblem statement: Electroencepharogram (EEG) is an extremely complex signal with very low signal to noise ratio and these attributed to difficulty in analyzing the signal. Hence for detecting abnormal segment, a distinctive method is required to train the technologist to distinguish the anomalous in EEG data. The objective of this study was to create a framework to analyze EEG signals … …Journal of Computer Science, Vol. 5, Issue 7 · 31 Jul 2009 · 7,326 accessesView PDFCite