Time–frequency features for pattern recognition using high-resolution TFDs: A tutorial review
This paper presents a tutorial review of recent advances in the field of time–frequency (t, f)
signal processing with focus on exploiting (t, f) image feature information using pattern …
signal processing with focus on exploiting (t, f) image feature information using pattern …
Deep convolutional neural networks for mental load classification based on EEG data
Electroencephalograph (EEG), the representation of the brain's electrical activity, is a widely
used measure of brain activities such as working memory during cognitive tasks. Varying in …
used measure of brain activities such as working memory during cognitive tasks. Varying in …
Surface electromyography based muscle fatigue detection using high-resolution time-frequency methods and machine learning algorithms
PA Karthick, DM Ghosh, S Ramakrishnan - Computer methods and …, 2018 - Elsevier
Background and objective Surface electromyography (sEMG) based muscle fatigue
research is widely preferred in sports science and occupational/rehabilitation studies due to …
research is widely preferred in sports science and occupational/rehabilitation studies due to …
A prototype-based SPD matrix network for domain adaptation EEG emotion recognition
Emotion plays a vital role in human daily life, and EEG signals are widely used in emotion
recognition. Due to individual variability, training a generic emotion recognition model …
recognition. Due to individual variability, training a generic emotion recognition model …
EEG-based emotion recognition using quadratic time-frequency distribution
Accurate recognition and understating of human emotions is an essential skill that can
improve the collaboration between humans and machines. In this vein …
improve the collaboration between humans and machines. In this vein …
Supervisory protection and automated event diagnosis using PMU data
This paper presents a new framework for supervisory protection and situational awareness
to enhance grid operations and protection using modern wide-area monitoring systems. In …
to enhance grid operations and protection using modern wide-area monitoring systems. In …
Quantitative preterm EEG analysis: the need for caution in using modern data science techniques
JM O'Toole, GB Boylan - Frontiers in pediatrics, 2019 - frontiersin.org
Hemodynamic changes during neonatal transition increase the vulnerability of the preterm
brain to injury. Real-time monitoring of brain function during this period would help identify …
brain to injury. Real-time monitoring of brain function during this period would help identify …
Time–frequency representation using IEVDHM–HT with application to classification of epileptic EEG signals
RR Sharma, RB Pachori - IET Science, Measurement & …, 2018 - Wiley Online Library
Time–frequency representation (TFR) is useful for non‐stationary signal analysis as it
provides information about the time‐varying frequency components. This study proposes a …
provides information about the time‐varying frequency components. This study proposes a …
Automatic epileptic seizure detection in EEG signals using sparse common spatial pattern and adaptive short-time Fourier transform-based synchrosqueezing …
Epilepsy can now be diagnosed more accurately and quickly due to computer-aided seizure
detection utilizing Electroencephalography (EEG) recordings. In this work, a novel method …
detection utilizing Electroencephalography (EEG) recordings. In this work, a novel method …
Automatic signal abnormality detection using time-frequency features and machine learning: A newborn EEG seizure case study
B Boashash, S Ouelha - Knowledge-Based Systems, 2016 - Elsevier
Time-frequency (TF) based machine learning methodologies can improve the design of
classification systems for non-stationary signals. Using selected TF distributions (TFDs), TF …
classification systems for non-stationary signals. Using selected TF distributions (TFDs), TF …