Sign inSubmit
Tips: “quotes” for a phrase · author:Lee · a DOI goes straight to its article

40 results for “EEG”
Set a search alert
Research ArticleOpen accessIn the keywordsComparative 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 keywordsImproved Application of Paraconsistent Artificial Neural Networks in Diagnosis of Alzheimer's DiseaseHelder F.S. Lopes, Jair M. Abe, Paulo A.M. Kanda et al.Problem statement: The visual analysis of Electroencephalogram (EEG) activity has shown useful as a complementary tool in Alzheimer Disease (AD diagnosis) when the diagnosis remains uncertain, in addition to be used in some clinical protocols. However, this analysis is subject to the inherent equipment imprecision, biological artifact, electrical records, and subjective physician interpretation of the visual analysis variation. The Artificial … …Neuroscience International, Vol. 2, Issue 1 · 6 Oct 2011 · 12,061 accessesView PDFCite
Research ArticleOpen accessIn the keywordsClassifying 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 keywordsTopological Conjugacy Between Seizure and Flat ElectroencephalographyTahir Ahmad, Tan Lit KenProblem statement: Seizure and Flat EEG which are modeled as two distinct dynamical systems share the same dynamics in the topological viewpoint. Approach: Motions of seizure and Flat EEG of the dynamical systems were written as set points. A function that maps between these two sets is then built. By using topological conjugacy, they are shown to share the same … …American Journal of Applied Sciences, Vol. 7, Issue 11 · 8 Nov 2010 · 6,760 accessesView PDFCite
Research ArticleOpen accessIn the keywordsDiscrete 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 keywordsAnomaly 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 accessIn the keywordsFunctional Neuroimaging in Dementia and other Amnesic Disorders: A Radiological ReviewLeonardo Caixeta, Renata Teles Vieira, Sergio Machado et al.Problem statement: The structural and functional neuroimaging have much to contribute to the cognitive neuroscience. Approach: We describe a radiological review of the major dementia syndromes and Mild Cognitive Impairment (MCI), included Alzheimer's disease, Lewy body dementia, Vascular dementia, Front temporal Lobar degeneration, Dementia associated with parkinsonism and other amnesic syndromes. Results: The different syndromes and diseases presenting with dementia … …Neuroscience International, Vol. 2, Issue 1 · 23 Nov 2011 · 7,495 accessesView PDFCite
Research ArticleOpen accessIn the keywordsFlows of Continuous-Time Dynamical Systems with No Periodic Orbit as an Equivalence Class under Topological Conjugacy RelationTahir Ahmad, Tan Lit KenProblem statement: Flows of continuous-time dynamical systems with the same number of equilibrium points and trajectories, and which has no periodic orbit form an equivalence class under the topological conjugacy relation. Approach: Arbitrarily, two trajectories resulting from two distinct flows of this type of dynamical systems were written as a set of points (orbit). A homeomorphism which maps between these … …Journal of Mathematics and Statistics, Vol. 7, Issue 3 · 18 Jul 2011 · 7,254 accessesView PDFCite
Research ArticleOpen accessIn the abstractNovel Depression Classification Framework Using Optimal Feature Integration with Hybrid Convolution (1D/3D) Based Adaptive Residual DenseNetB. Manjulatha, Suresh PabbojuThis study introduces a novel depression classification method by incorporating multimodal data to reduce this limitation and improve the accuracy of depression diagnosis. In the beginning, the multimodal data such as speech signal, Electroencephalographic (EEG), and text data is obtained from the benchmark datasets. These acquired text data are subjected to the text pre-processing phase, where the stemming, character removal, … …Journal of Computer Science, Vol. 21, Issue 3 · 25 Feb 2025 · 5,860 accessesView PDFCite
Research ArticleOpen accessIn the abstractA Non-Intrusive Experiment to Examine the Visual Attention Data on Exposure to a Brand Using Eye TrackingSurya Bahadur Thapa, Prasun Gahlot, Komal Suryavanshi et al.The objective of this study is to understand the influence of articulation on sports sponsorship posters of a sporting event. An eye tracking experiment was conducted to examine the visual attention data on exposure to the sponsor’s brand while viewing an advertisement poster. The respondents were asked to fill out a questionnaire based on what they could recall while viewing … …Journal of Computer Science, Vol. 19, Issue 3 · 28 Feb 2023 · 5,908 accessesView PDFCite