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 …
Medical big data: neurological diseases diagnosis through medical data analysis
Diagnosis of neurological diseases is a growing concern and one of the most difficult
challenges for modern medicine. According to the World Health Organisation's recent report …
challenges for modern medicine. According to the World Health Organisation's recent report …
A spectrogram image based intelligent technique for automatic detection of autism spectrum disorder from EEG
Autism spectrum disorder (ASD) is a developmental disability characterized by persistent
impairments in social interaction, speech and nonverbal communication, and restricted or …
impairments in social interaction, speech and nonverbal communication, and restricted or …
EEG data augmentation using Wasserstein GAN
G Bouallegue, R Djemal - 2020 20th International Conference …, 2020 - ieeexplore.ieee.org
Electroencephalogram (EEG) presents a challenge during the classification task using
machine learning and deep learning techniques due to the lack or to the low size of …
machine learning and deep learning techniques due to the lack or to the low size of …
Electroencephalography (EEG) signal processing for epilepsy and autism spectrum disorder diagnosis
Quantification of abnormality in brain signals may reveal brain conditions and pathologies.
In this study, we investigate different electroencephalography (EEG) feature extraction and …
In this study, we investigate different electroencephalography (EEG) feature extraction and …
EEG signal analysis for diagnosing neurological disorders using discrete wavelet transform and intelligent techniques
Analysis of electroencephalogram (EEG) signals is essential because it is an efficient
method to diagnose neurological brain disorders. In this work, a single system is developed …
method to diagnose neurological brain disorders. In this work, a single system is developed …
Accurate detection of autism using Douglas-Peucker algorithm, sparse coding based feature map** and convolutional neural network techniques with EEG signals
Abstract Autism Spectrum Disorders (ASD) is a collection of complicated neurological
disorders that first show in early childhood. Electroencephalogram (EEG) signals are widely …
disorders that first show in early childhood. Electroencephalogram (EEG) signals are widely …
Automatic and efficient framework for identifying multiple neurological disorders from EEG signals
The burden of neurological disorders is huge on global health and recognized as major
causes of death and disability worldwide. There are more than 600 neurological diseases …
causes of death and disability worldwide. There are more than 600 neurological diseases …
EEG‐based computer aided diagnosis of autism spectrum disorder using wavelet, entropy, and ANN
Autism spectrum disorder (ASD) is a type of neurodevelopmental disorder with core
impairments in the social relationships, communication, imagination, or flexibility of thought …
impairments in the social relationships, communication, imagination, or flexibility of thought …
A robust method for early diagnosis of autism spectrum disorder from EEG signals based on feature selection and DBSCAN method
Electroencephalogram (EEG) is one of the most important signals for diagnosis of Autism
Spectrum Disorder (ASD). There are different challenges such as feature selection and the …
Spectrum Disorder (ASD). There are different challenges such as feature selection and the …