Emotion Recognition from EEG Using Rhythm Synchronization Patterns with Joint Time-Frequency-Space Correlation

2017 ◽  
pp. 159-168 ◽  
Author(s):  
Hongzhi Kuai ◽  
Hongxia Xu ◽  
Jianzhuo Yan
Keyword(s):  
Emotion Recognition ◽  
Space Correlation ◽  
Frequency Space ◽  
Time Frequency

Orthogonal Time Frequency Space (OTFS) With Dual-Mode Index Modulation

2021 ◽  
pp. 1-1
Author(s):  
Hang Zhao ◽  
Dongxuan He ◽  
Ziqi Kang ◽  
Hua Wang
Keyword(s):  
Dual Mode ◽  
Frequency Space ◽  
Time Frequency ◽  
Mode Index ◽  
Index Modulation

scholarly journals Transmitter and Receiver Window Designs for Orthogonal Time Frequency Space Modulation

2021 ◽  
pp. 1-1
Author(s):  
Zhiqiang Wei ◽  
Weijie Yuan ◽  
Shuangyang Li ◽  
Jinhong Yuan ◽  
Derrick Wing Kwan Ng
Keyword(s):  
Frequency Space ◽  
Time Frequency ◽  
Space Modulation

Narrow band time–frequency space matched passive detector for underwater signal

2021 ◽  
Vol 183 ◽  
pp. 108287
Author(s):  
Yao Haiyang ◽  
Zhang Zhichen ◽  
Wang Haiyan ◽  
Wang Yong
Keyword(s):  
Narrow Band ◽  
Frequency Space ◽  
Time Frequency ◽  
Passive Detector

Mathematical view of a blind source separation on a time frequency space

2007 ◽  
Vol 187 (1) ◽  
pp. 153-162 ◽  
Author(s):  
Keiko Fujita ◽  
Yoshitsugu Takei ◽  
Akira Morimoto ◽  
Ryuichi Ashino
Keyword(s):  
Blind Source Separation ◽  
Source Separation ◽  
Frequency Space ◽  
Time Frequency

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.

Orthogonal Time-Frequency Space with Improved Index Modulation

2021 ◽  
Author(s):  
Huarong Ren ◽  
Weikai Xu ◽  
Lin Wang
Keyword(s):  
Frequency Space ◽  
Time Frequency ◽  
Index Modulation

scholarly journals Non-orthogonal Discrete Multi-tone With Low Decoding Complexity Utilizing Symmetric Compression in Time–Frequency Space

2018 ◽  
Vol 10 (6) ◽  
pp. 1-7 ◽  
Author(s):  
Pengcheng Wei ◽  
Qi Sui ◽  
Zibin Li ◽  
Fan Li ◽  
Xingwen Yi ◽  
...  
Keyword(s):  
Decoding Complexity ◽  
Frequency Space ◽  
Time Frequency
Sign in / Sign up

Export Citation Format

Share Document