scholarly journals Topography-Time-Frequency Atomic Decomposition for Event-Related M/EEG Signals

2007 ◽  
Author(s):  
Christian-G. Benar ◽  
Theodore Papadopoulo ◽  
Maureen Clerc
Keyword(s):  
Atomic Decomposition ◽  
Eeg Signals ◽  
Time Frequency

A Modified S-Transform for EEG Signals Analysis

2015 ◽  
Vol 12 (03) ◽  
pp. 1550021 ◽  
Author(s):  
M. A. Al-Manie ◽  
W. J. Wang
Keyword(s):  
Image Processing ◽  
Fourier Transform ◽  
Energy Concentration ◽  
Biomedical Data ◽  
Short Time Fourier Transform ◽  
Phase Information ◽  
Eeg Signals ◽  
Time Frequency ◽  
S Transform ◽  
Short Time

Due to the advantages offered by the S-transform (ST) distribution, it has been recently successfully implemented for various applications such as seismic and image processing. The desirable properties of the ST include a globally referenced phase as the case with the short time Fourier transform (STFT) while offering a higher spectral resolution as the wavelet transform (WT). However, this estimator suffers from some inherent disadvantages seen as poor energy concentration with higher frequencies. In order to improve the performance of the distribution, a modification to the existing technique is proposed. Additional parameters are proposed to control the window's width which can greatly enhance the signal representation in the time–frequency plane. The new estimator's performance is evaluated using synthetic signals as well as biomedical data. The required features of the ST which include invertability and phase information are still preserved.

Emotion recognition using time–frequency ridges of EEG signals based on multivariate synchrosqueezing transform

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Ahmet Mert ◽  
Hasan Huseyin Celik
Keyword(s):  
Emotion Recognition ◽  
Feature Vector ◽  
Dimensional Space ◽  
Computational Cost ◽  
Feature Space ◽  
Maximum Energy ◽  
Machine Learning Algorithms ◽  
Eeg Signals ◽  
Time Frequency ◽  
Synchrosqueezing Transform

Abstract The feasibility of using time–frequency (TF) ridges estimation is investigated on multi-channel electroencephalogram (EEG) signals for emotional recognition. Without decreasing accuracy rate of the valence/arousal recognition, the informative component extraction with low computational cost will be examined using multivariate ridge estimation. The advanced TF representation technique called multivariate synchrosqueezing transform (MSST) is used to obtain well-localized components of multi-channel EEG signals. Maximum-energy components in the 2D TF distribution are determined using TF-ridges estimation to extract instantaneous frequency and instantaneous amplitude, respectively. The statistical values of the estimated ridges are used as a feature vector to the inputs of machine learning algorithms. Thus, component information in multi-channel EEG signals can be captured and compressed into low dimensional space for emotion recognition. Mean and variance values of the five maximum-energy ridges in the MSST based TF distribution are adopted as feature vector. Properties of five TF-ridges in frequency and energy plane (e.g., mean frequency, frequency deviation, mean energy, and energy deviation over time) are computed to obtain 20-dimensional feature space. The proposed method is performed on the DEAP emotional EEG recordings for benchmarking, and the recognition rates are yielded up to 71.55, and 70.02% for high/low arousal, and high/low valence, respectively.

scholarly journals Low-frequency oscillations in human tibial somatosensory evoked potentials

2006 ◽  
Vol 64 (2b) ◽  
pp. 402-406 ◽  
Author(s):  
Carlos Julio Tierra-Criollo ◽  
Antonio Fernando Catelli Infantosi
Keyword(s):  
Evoked Potential ◽  
Low Frequency ◽  
Electric Activity ◽  
Relative Power ◽  
Eeg Signals ◽  
Time Frequency ◽  
Low Frequency Oscillations ◽  
Current Pulses ◽  
Stimulus Artifact ◽  
Minimum Intensity

Oscillatory cerebral electric activity has been related to sensorial and perceptual-cognitive functions. The aim of this work is to investigate low frequency oscillations (<300 Hz), particularly within the gamma band (30-110 Hz), during tibial stimulation. Twenty-one volunteers were subjected to 5 Hz stimulation by current pulses of 0.2 ms duration and the minimum intensity to provoke involuntary twitch. EEG signals without (spontaneously) and during stimulation were recorded at primary somatosensory area. A time-frequency analysis indicated the effect of the stimulus artifact in the somatosensory evoked potential (SEP) frequencies up to 5 ms after the stimulus. The oscillations up to 100 Hz presented the highest relative power contribution (approximately 99%) for the SEP and showed difference (p<0.01) from the frequencies of the spontaneously EEG average. Moreover, the range 30-58 Hz was identified as the band with the highest contribution for the tibial SEP morphology (p<0.0001).

Time–frequency distributions in the classification of epilepsy from EEG signals

2012 ◽  
Vol 39 (13) ◽  
pp. 11413-11422 ◽  
Author(s):  
Marcus Musselman ◽  
Dragan Djurdjanovic
Keyword(s):  
Eeg Signals ◽  
Frequency Distributions ◽  
Time Frequency
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