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@article{170156,
        author = {Habi Patrick and Dr. Shailja Shukla},
        title = {Influence of EEG Constraints for the Detection of Dementia Diseases: An Organized Review},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {6},
        pages = {3511-3514},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=170156},
        abstract = {The paper aims to reconnoiter the application in the field of Convolutional Neural Networks (CNNs) in detecting dementia from Electroencephalogram (EEG) images. Dementia is a progressive neurological disorder, categorized by the progressive deterioration of cognitive function, can be stimulating to detect the disorder at initial phases. It explores the area to work of Convolutional Neural Networks (CNNs) in the detection of dementia by analyzing Electroencephalogram (EEG) images. EEG is a non-invasive way or technique that records electrical activity in the brain and show probability in detecting neurodegenerative diseases. The usage of deep learning work for approaches, predominantly CNNs, offers potential in powering, automating and enhancing the research to detect accurateness of dementia disease from EEG signals.},
        keywords = {},
        month = {December},
        }