A comparative analysis of signal processing and classification methods for different applications based on EEG signals
A Khosla, P Khandnor, T Chand - Biocybernetics and Biomedical …, 2020 - Elsevier
Electroencephalogram (EEG) measures the neuronal activities in the form of electric
currents that are generated due to the synchronized activity by a group of specialized …
currents that are generated due to the synchronized activity by a group of specialized …
Detection of epileptic seizures on EEG signals using ANFIS classifier, autoencoders and fuzzy entropies
A Shoeibi, N Ghassemi, M Khodatars… - … Signal Processing and …, 2022 - Elsevier
Epileptic seizures are one of the most crucial neurological disorders, and their early
diagnosis will help the clinicians to provide accurate treatment for the patients. The …
diagnosis will help the clinicians to provide accurate treatment for the patients. The …
A comprehensive review on machine learning in brain tumor classification: taxonomy, challenges, and future trends
Abstract In recent years, Machine Learning (ML), a key component of artificial intelligence
(AI), has become increasingly popular in data analysis and processing. ML is now widely …
(AI), has become increasingly popular in data analysis and processing. ML is now widely …
Classification of alcoholic EEG signals using wavelet scattering transform-based features
Following the research question and the relevant dataset, feature extraction is the most
important component of machine learning and data science pipelines. The wavelet …
important component of machine learning and data science pipelines. The wavelet …
Alcoholic EEG signals recognition based on phase space dynamic and geometrical features
Alcoholism is a severe disorder that leads to brain problems and associated cognitive,
emotional and behavioral impairments. This disorder is typically diagnosed by a …
emotional and behavioral impairments. This disorder is typically diagnosed by a …
Classification of normal and depressed EEG signals based on centered correntropy of rhythms in empirical wavelet transform domain
A widespread brain disorder of present days is depression which influences 264 million of
the world's population. Depression may cause diverse undesirable consequences, including …
the world's population. Depression may cause diverse undesirable consequences, including …
Epileptic seizures detection in EEG signals using fusion handcrafted and deep learning features
Epilepsy is a brain disorder disease that affects people's quality of life.
Electroencephalography (EEG) signals are used to diagnose epileptic seizures. This paper …
Electroencephalography (EEG) signals are used to diagnose epileptic seizures. This paper …
Automated detection of focal EEG signals using features extracted from flexible analytic wavelet transform
Epilepsy is a neurological disease which is difficult to diagnose accurately. An authentic
detection of focal epilepsy will help the clinicians to provide proper treatment for the patients …
detection of focal epilepsy will help the clinicians to provide proper treatment for the patients …
Empirical wavelet transform based automated alcoholism detecting using EEG signal features
A Anuragi, DS Sisodia - Biomedical Signal Processing and Control, 2020 - Elsevier
Electroencephalogram (EEG) signals are well used to characterize the brain states and
actions. In this paper, a novel empirical wavelet transform (EWT) based machine learning …
actions. In this paper, a novel empirical wavelet transform (EWT) based machine learning …
EEG based alcoholism detection by oscillatory modes decomposition second order difference plots and machine learning
The excessive drinking of alcohol can disrupt the neural system. This can be observed by
properly analysing the Electroencephalogram (EEG) signals. However, the EEG is a signal …
properly analysing the Electroencephalogram (EEG) signals. However, the EEG is a signal …