Self-supervised learning for electroencephalography
Decades of research have shown machine learning superiority in discovering highly
nonlinear patterns embedded in electroencephalography (EEG) records compared with …
nonlinear patterns embedded in electroencephalography (EEG) records compared with …
[PDF][PDF] Epileptic seizure detection using deep learning through min max scaler normalization
B Deepa, K Ramesh - Int. J. Health Sci, 2022 - pdfs.semanticscholar.org
Epileptic seizure detection and prediction are significantly sought-after research currently
because robust algorithms are available. Machine learning and deep learning have allowed …
because robust algorithms are available. Machine learning and deep learning have allowed …
Time domain implementation of pediatric epileptic seizure detection system for enhancing the performance of detection and easy monitoring of pediatric patients
Objective The clinical phenomenon of epilepsy varies greatly among patients and this in
turn, has its effect on the quality of life they lead. Studies reveal a requisite for efficient …
turn, has its effect on the quality of life they lead. Studies reveal a requisite for efficient …
Patient-specific epileptic seizure prediction in long-term scalp EEG signal using multivariate statistical process control
An accurate epileptic seizure prediction algorithm can alleviate the problem and reduce
risks in the life of a patient suffering from epilepsy. The main motive of this work is to propose …
risks in the life of a patient suffering from epilepsy. The main motive of this work is to propose …
Wavelet scattering and scalogram visualization based human brain decoding using empirical wavelet transform
A prediction model to decode the human brain activities from brain signals is proposed in
this work. The existing works available in the Neuroscience field are higher in the …
this work. The existing works available in the Neuroscience field are higher in the …
Dynamic characterization of functional brain connectivity network for mental workload condition using an effective network identifier
Monitoring the mental workload (MWL) condition is essential for maintaining a more
productive working environment. The study proposed an Electroencephalogram (EEG) …
productive working environment. The study proposed an Electroencephalogram (EEG) …
Feature extraction and selection techniques for time series data classification: A comparative analysis
Recently, time-series data mining has attracted tremendous interest and initiated various
researches in real-time high dimensional data like, Stock market, Electrocardiogram …
researches in real-time high dimensional data like, Stock market, Electrocardiogram …
Motor imagery EEG signal classification using long short-term memory deep network and neighbourhood component analysis
A Nakra, M Duhan - International Journal of Information Technology, 2022 - Springer
Abstract Brain Computer Interface is a technique used to measure brain activity in terms of
electrical signals. The recorded Electroencephalograph (EEG) signal is highly sensitive to …
electrical signals. The recorded Electroencephalograph (EEG) signal is highly sensitive to …
Automatic detection of ictal activity in EEG using synchronization and chaos-based attributes
Automatic seizure onset detectors (SODs) have been proposed to alert epileptic patients
when a seizure is about to happen and in turn improve their quality of life. Yet, the detectors …
when a seizure is about to happen and in turn improve their quality of life. Yet, the detectors …
Emotion analysis and speech signal processing
O Raghib, E Sharma, T Ahmad… - 2017 IEEE International …, 2017 - ieeexplore.ieee.org
In recent times there have been notable advancements in the field of Automatic Speech
Recognition (ASR) in terms of the technology used as well as its applications. But still the …
Recognition (ASR) in terms of the technology used as well as its applications. But still the …