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17 results for “electroencephalogram”
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Research ArticleOpen accessEvery word in the titleUsing 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 accessEvery word in the titleClassifying 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 accessEvery word in the titleSwarm 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 accessEvery word in the titleAnomaly 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
Research ArticleOpen accessEvery word in the titleSEGMENTATION OF ELECTROENCEPHALOGRAM SIGNALS DURING EPILEPTIC SEIZURES BY USING FUZZY C-MEANSMuhammad Abdy, Tahir AhmadElectroencephalogram (EEG) is a recording of electrical activity of the brain. It contains valuable information related to the different physiological states of the brain. A quantitative EEG analysis has been developed over the years that introduce objective measure, reflecting not only the characteristics of the brain activity itself but also giving clues concerning the underlying associated neural dynamics. In this … …American Journal of Applied Sciences, Vol. 11, Issue 10 · 11 Nov 2014 · 6,230 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleComparative 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 accessEvery word in the titleDiscrete 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 accessEvery word in the titleBispectral Analysis of Scalp Electroencephalograms: Quadratic Phasecoupling Phenomenon in Detecting Brain TumorV. Salai Selvam, S. ShenbagadeviSince the power spectral analysis of a non-Gaussian process generated by a nonlinear mechanism e.g., EEG, does not provide much information on the underlying nonlinear dynamics due to the lack of phase information, the higher-order statistics such as the bispectra are used to better understand the underlying nonlinear dynamics e.g., the quadratic phase coupling phenomena. The quadratic phase couplings have … …American Journal of Applied Sciences, Vol. 10, Issue 3 · 27 Mar 2013 · 6,537 accessesView PDFCite
Research ArticleOpen accessIn the keywordsEpileptic Seizure Detection from EEG Signal Using Progressive Channel Selection and Deep LearningSuraiya Akter Mumu, Shupta Das, M. A. H. Akhand et al.Epileptic Seizures (ES), marked by abrupt abnormal electrical discharges in the brain, represent the primary symptom of epilepsy, making timely and accurate detection critical because of their unpredictable recurrence and potentially severe consequences. The Electroencephalogram (EEG), a widely used, non-invasive, and economical method for monitoring brain activity, plays a key role in automated ES detection through Machine Learning (ML) and … …Journal of Computer Science, Vol. 22, Issue 6 · 8 Jul 2026 · 1,393 accessesView PDFCite
Research ArticleOpen accessIn the keywordsEpileptic Seizure Detection Using Integrated Decomposed Features from EEG SignalShupta Das, Suraiya Akter Mumu, M. A. H. Akhand, Md Abdus Samad KamalThe root cause of the seizure is a sudden abnormal excessive electrical discharge in the brain and it is an Epileptic Seizure (ES) when such abnormal electrical activity arises particularly for epilepsy. Recognizing ES is crucial for effective treatment as it often repeats and can lead to serious outcomes. Since epilepsy is a neurological issue, detecting ES by analyzing brain … …Journal of Computer Science, Vol. 20, Issue 10 · 8 Aug 2024 · 4,845 accessesView PDFCite