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21 results for “EEG Signals”
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Research ArticleOpen accessPhrase in the titleEEG Signals During Epileptic Seizure as a Semigroup of Upper Triangular MatricesFaisal Abdurabu Mubarak Binjadhnan, Tahir AhmadProblem statement: EEG signals during epileptic seizure can be presented as an algebraic structure, namely semigroup of upper triangular matrices. Approach: EEG signals during seizure were recorded and composed into set of matrices. They were transformed into upper triangular matrices using QR-Schur decomposition and finally as a semigroup of upper triangular matrices. Results: EEG signals during epileptic seizure were transformed … …American Journal of Applied Sciences, Vol. 7, Issue 4 · 30 Apr 2010 · 7,019 accessesView PDFCite
Research ArticleOpen accessIn the keywordsSEGMENTATION 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 accessIn the keywordsApplication of Multi Task Analysis Model Based on EEG Features in the Study of the Correlation between Depression and Sleep Disorders in College StudentsFeng MaWith the improvement of multi-modal technology, research on depression recognition combining multiple biological signal features has been increasing. Electroencephalogram signals reflect brain activity, and by analyzing their characteristics, the accuracy and stability of depression recognition can be effectively improved. Therefore, this study proposes a correlation analysis between depression and sleep disorders in college students based on electroencephalogram feature analysis, innovatively … …American Journal of Biochemistry and Biotechnology, Vol. 21, Issue 4 · 2 Feb 2026 · 1,994 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 abstractEpileptic 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
Research ArticleOpen accessIn the abstractDiscrete to Continuous Algorithm for Optimal Channel Selection to Detect Alcoholism through EEG RecordingsHayat Sedrati, Wajih Rhalem et al.Alcoholism is a serious public health issue, and early diagnosis of this brain disease can be performed by analyzing Electroencephalogram (EEG) signals. However, the high dimensionality of EEG datasets requires significant computational time and resources for the automatic processing of EEG signals. This study proposes a novel method to reduce EEG dataset dimensionality using a Discrete to Continuous algorithm (DtC) … …Journal of Computer Science, Vol. 19, Issue 1 · 17 Jan 2023 · 5,904 accessesView PDFCite
Research ArticleOpen accessIn the abstractAutism Detection from 2D Transformed EEG Signal using Convolutional Neural NetworkZahrul Jannat Peya, M. A. H. Akhand, Jannatul Ferdous Srabonee, Nazmul SiddiqueAutism Spectrum Disorder (ASD) is a neurodevelopmental disorder relating to speech complications, nonverbal and social communication, and repetitive behaviors. There is no remedy for ASD but early diagnosis, mediation, and supportive care can aid the development of language, conduct, and communication skills. As the cause of ASD is a neurodevelopmental disorder, its diagnosis based on brain function analyzing different brain … …Journal of Computer Science, Vol. 18, Issue 8 · 31 Jul 2022 · 7,921 accessesView PDFCite
Review ArticleOpen accessIn the abstractEEG-based Processing and Classification Methodologies for Autism Spectrum Disorder: A ReviewGunavaran Brihadiswaran, Dilantha Haputhanthri, Sahan Gunathilaka et al.Autism Spectrum Disorder is a lifelong neurodevelopmental condition which affects social interaction, communication and behaviour of an individual. The symptoms are diverse with different levels of severity. Recent studies have revealed that early intervention is highly effective for improving the condition. However, current ASD diagnostic criteria are subjective which makes early diagnosis challenging, due to the unavailability of well-defined medical … …Journal of Computer Science, Vol. 15, Issue 8 · 23 Aug 2019 · 10,401 accessesView PDFCite
Research ArticleOpen accessIn the abstractDynamical System of Flat ElectroencephalographyTan Lit Ken, Tahir bin Ahmad, Mohd Sham bin Mohamad, Su Kong NgienFlat EEG is a way of viewing electroencephalography signals on the real plane. The wealth of information contained within makes it a great platform to study epileptic seizure. In this study, the time embedment nature of Flat EEG is exploited to establish a dynamic Flat EEG model. Basically, a geometrical representation which displays the important information visually for Flat EEG … …American Journal of Applied Sciences, Vol. 12, Issue 7 · 21 Aug 2015 · 6,600 accessesView PDFCite
Research ArticleOpen accessIn the abstractEVENT-RELATED POTENTIALS EXTRACTION OF WORKING MEMORY USING WAVELET ALGORITHMSitiZubaidahMohd Tumari, Rubita Sudirman, Abdul Hamid AhmadThis study was designed to classify and determine the Event-Related Potentials (ERPs) signal pattern of normal children on visual response. Thirty-eight children aged between 10 to 12 years old were subjected to a two-phase computer-based assessment while their working memory activity was recorded using a Neurofax-EEG 9200 machine. For children, it is anticipated that some information can be lost when … …Journal of Computer Science, Vol. 10, Issue 2 · 13 Nov 2013 · 6,801 accessesView PDFCite