Fourier-Bessel representation for signal processing: A review
Several applications, analysis and visualization of signal demand representation of time-
domain signal in different domains like frequency-domain representation based on Fourier …
domain signal in different domains like frequency-domain representation based on Fourier …
EEG-based cross-subject emotion recognition using Fourier-Bessel series expansion based empirical wavelet transform and NCA feature selection method
Automated emotion recognition using brain electroencephalogram (EEG) signals is
predominantly used for the accurate assessment of human actions as compared to facial …
predominantly used for the accurate assessment of human actions as compared to facial …
[图书][B] Time-frequency analysis techniques and their applications
RB Pachori - 2023 - taylorfrancis.com
Most of the real-life signals are non-stationary in nature. The examples of such signals
include biomedical signals, communication signals, speech, earthquake signals, vibration …
include biomedical signals, communication signals, speech, earthquake signals, vibration …
Classification of epileptic seizures in EEG signals based on phase space representation of intrinsic mode functions
R Sharma, RB Pachori - Expert Systems with Applications, 2015 - Elsevier
Epileptic seizure is the most common disorder of human brain, which is generally detected
from electroencephalogram (EEG) signals. In this paper, we have proposed the new …
from electroencephalogram (EEG) signals. In this paper, we have proposed the new …
EMD-based temporal and spectral features for the classification of EEG signals using supervised learning
This paper presents a novel method for feature extraction from electroencephalogram (EEG)
signals using empirical mode decomposition (EMD). Its use is motivated by the fact that the …
signals using empirical mode decomposition (EMD). Its use is motivated by the fact that the …
Classification of seizure and nonseizure EEG signals using empirical mode decomposition
V Bajaj, RB Pachori - IEEE Transactions on Information …, 2011 - ieeexplore.ieee.org
In this paper, we present a new method for classification of electroencephalogram (EEG)
signals using empirical mode decomposition (EMD) method. The intrinsic mode functions …
signals using empirical mode decomposition (EMD) method. The intrinsic mode functions …
Application of entropy measures on intrinsic mode functions for the automated identification of focal electroencephalogram signals
The brain is a complex structure made up of interconnected neurons, and its electrical
activities can be evaluated using electroencephalogram (EEG) signals. The characteristics …
activities can be evaluated using electroencephalogram (EEG) signals. The characteristics …
A novel approach for automated detection of focal EEG signals using empirical wavelet transform
A Bhattacharyya, M Sharma, RB Pachori… - Neural Computing and …, 2018 - Springer
The determination of epileptogenic area is a prime task in presurgical evaluation. The
seizure activity can be prevented by operating the affected areas by clinical surgery. In this …
seizure activity can be prevented by operating the affected areas by clinical surgery. In this …
An effective dual self-attention residual network for seizure prediction
X Yang, J Zhao, Q Sun, J Lu… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
As one of the most challenging data analysis tasks in chronic brain diseases, epileptic
seizure prediction has attracted extensive attention from many researchers. Seizure …
seizure prediction has attracted extensive attention from many researchers. Seizure …
Epileptic seizure identification using entropy of FBSE based EEG rhythms
V Gupta, RB Pachori - Biomedical Signal Processing and Control, 2019 - Elsevier
This paper has proposed a new method for classification of epileptic seizures based on
weighted multiscale Renyi permutation entropy (WMRPE) and rhythms obtained with Fourier …
weighted multiscale Renyi permutation entropy (WMRPE) and rhythms obtained with Fourier …