Current status, challenges, and possible solutions of EEG-based brain-computer interface: a comprehensive review
Brain-Computer Interface (BCI), in essence, aims at controlling different assistive devices
through the utilization of brain waves. It is worth noting that the application of BCI is not …
through the utilization of brain waves. It is worth noting that the application of BCI is not …
Automated epilepsy detection techniques from electroencephalogram signals: a review study
Epilepsy is a serious neurological condition which contemplates as top 5 reasons for
avoidable mortality from ages 5–29 in the worldwide. The avoidable deaths due to epilepsy …
avoidable mortality from ages 5–29 in the worldwide. The avoidable deaths due to epilepsy …
A computerized method for automatic detection of schizophrenia using EEG signals
Diagnosis of schizophrenia (SZ) is traditionally performed through patient's interviews by a
skilled psychiatrist. This process is time-consuming, burdensome, subject to error and bias …
skilled psychiatrist. This process is time-consuming, burdensome, subject to error and bias …
A comprehensive comparison of handcrafted features and convolutional autoencoders for epileptic seizures detection in EEG signals
A Shoeibi, N Ghassemi, R Alizadehsani… - Expert Systems with …, 2021 - Elsevier
Epilepsy, a brain disease generally associated with seizures, has tremendous effects on
people's quality of life. Diagnosis of epileptic seizures is commonly performed on …
people's quality of life. Diagnosis of epileptic seizures is commonly performed on …
Seizure prediction in scalp EEG using 3D convolutional neural networks with an image-based approach
Epileptic seizures occur as a result of a process that develops over time and space in
epileptic networks. In this study, we aim at developing a generalizable method for patient …
epileptic networks. In this study, we aim at developing a generalizable method for patient …
Exploring deep learning features for automatic classification of human emotion using EEG rhythms
Emotion recognition (ER) from Electroencephalogram (EEG) signals is a challenging task
due to the non-linearity and non-stationarity nature of EEG signals. Existing feature …
due to the non-linearity and non-stationarity nature of EEG signals. Existing feature …
Motor imagery EEG signals classification based on mode amplitude and frequency components using empirical wavelet transform
As one of the key techniques determining the overall system performances, efficient and
reliable algorithms for improving the classification accuracy of motor imagery (MI) based …
reliable algorithms for improving the classification accuracy of motor imagery (MI) based …
Depression recognition based on the reconstruction of phase space of EEG signals and geometrical features
Depression is a mental disorder that continues to make life difficult or impossible for a
depressed person and, if left untreated, can lead to dangerous activities such as self-harm …
depressed person and, if left untreated, can lead to dangerous activities such as self-harm …
A recent investigation on detection and classification of epileptic seizure techniques using EEG signal
The benefits of early detection and classification of epileptic seizures in analysis, monitoring
and diagnosis for the realization and actualization of computer-aided devices and recent …
and diagnosis for the realization and actualization of computer-aided devices and recent …
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 …