Synchrosqueezing transform based feature extraction from EEG signals for emotional state prediction

Özel, Pınar; Akan, Aydın; Yılmaz, Bülent

doi:10.1016/j.bspc.2019.04.023

Cited by 54 publications

(19 citation statements)

References 32 publications

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“…Zheng et al [20] used EEG spectral power as a feature in the discovery and emotional identification of EEG channels by coarse canonical correlation analysis. Ozel et al [21] implemented multivariate synchrosqueezing transform to extract EEG features for emotional state recognition. For one of the emotional states, they achieved 93% classification accuracy.…”

Section: Introductionmentioning

confidence: 99%

Recognizing Emotions Evoked by Music Using CNN-LSTM Networks on EEG Signals

et al. 2020

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Emotion is considered to be critical for the actual interpretation of actions and relationships. Recognizing emotions from EEG signals is also becoming an important computer-aided method for diagnosing emotional disorders in neurology and psychiatry. Another advantage of this approach is recognizing emotions without clinical and medical examination, which plays a major role in completing the Brain-Computer Interface (BCI) structure. Emotions recognition ability, without traditional utilization strategies such as self-assessment tests, is of paramount importance. EEG signals are considered the most reliable technique for emotions recognition because of the non-invasive nature. Manual analysis of EEG signals is impossible for emotions recognition, so an automatic method of EEG signals should be provided for emotions recognition. One problem with automatic emotions recognition is the extraction and selection of discriminative features that generally lead to high computational complexity. This paper was design to prepare a new approach to automatic two-stage classification (negative and positive) and three-stage classification (negative, positive, and neutral) of emotions from EEG signals. In the proposed method, directly apply the raw EEG signal to the convolutional neural network and long short-term memory network (CNN-LSTM), without involving feature extraction/selection. In prior literature, this is a challenging method. The suggested deep neural network architecture includes 10-convolutional layers with 3-LSTM layers followed by 2-fully connected layers. The LSTM network in a fusion of the CNN network has been used to increase stability and reduce oscillation. In the present research, we also recorded the EEG signals of 14 subjects with music stimulation for the process. The simulation results of the proposed algorithm for two-stage classification (negative and positive) and three-stage classification (negative, neutral and positive) of emotion for 12 active channels showed 97.42% and 96.78% accuracy and Kappa coefficient of 0.94 and 0.93 respectively. We also compared our proposed LSTM-CNN network (end-toend) with other hand-crafted methods based on MLP and DBM classifiers and achieved promising results in comparison with similar approaches. According to the high accuracy of the proposed method, it can be used to develop the human-computer interface system.

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Section: Introductionmentioning

confidence: 99%

Recognizing Emotions Evoked by Music Using CNN-LSTM Networks on EEG Signals

et al. 2020

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“…A small value of s provides highfrequency resolution. [22][23][24] SST generates a twodimensional matrix in TF representation. Further, SVD is used to reduce the dimensions of the TF matrix for better characterisation of the nonstationary signal.…”

Section: Synchrosqueezed Wavelet Transformmentioning

confidence: 99%

“…23 This transform finds application in different areas of research such as emotional state prediction from electroencephalography signals, chatter detection in sound signals, diagnosis of sleep apnoea and muscle fatigue assessment. [24][25][26][27] However, this method has not been reported for analysing dynamic contractions using sEMG signals.…”

Section: Introductionmentioning

confidence: 99%

Dynamic contraction and fatigue analysis in biceps brachii muscles using synchrosqueezed wavelet transform and singular value features

Hari

Venugopal

Swaminathan

2021

Proc Inst Mech Eng H

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In this study, the dynamic contractions and the associated fatigue condition in biceps brachii muscle are analysed using Synchrosqueezed Wavelet Transform (SST) and singular value features of surface Electromyography (sEMG) signals. For this, the recorded signals are decomposed into time-frequency matrix using SST. Two analytic functions namely Morlet and Bump wavelets are utilised for the analysis. Singular Value Decomposition method is applied to this time-frequency matrix to derive the features such as Maximum Singular Value (MSV), Singular Value Entropy (SVEn) and Singular Value Energy (SVEr). The results show that both these wavelets are able to characterise nonstationary variations in sEMG signals during dynamic fatiguing contractions. Increase in values of MSV and SVEr with the progression of fatigue denotes the presence of nonstationarity in the sEMG signals. The lower values of SVEn with the progression of fatigue indicate the randomness in the signal. Thus, it appears that the proposed approach could be used to characterise dynamic muscle contractions under varied neuromuscular conditions.

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“…Various TF methods has also been used for fatigue analysis such as continuous wavelet transform (CWT), B distribution and extended modified B distribution, S transform methods [6]. Recently, synchrosqueezed CWT (SST) has been used for TF representation and analysis of non‐stationary and oscillatory signals such as vibration signals, Electroencephalograph (EEG) signals and seismic signals [7–9]. It is also reported that SST provides high resolution of frequency components, and it is better than CWT.…”

Section: Introductionmentioning

confidence: 99%

Muscle fatigue analysis in biceps brachii surface electromyography signals using synchrosqueezed Morlet wavelet and singular value decomposition

Hari

Venugopal

Swaminathan

2020

Electronics Letters

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Muscle fatigue during isometric contraction of biceps brachii is analysed using synchrosqueezed continuous wavelet transform with Morlet wavelet and singular value decomposition (SVD) features. The recorded surface electromyography signals are decomposed to time frequency matrix using Morlet wavelet and the characteristics are extracted using singular value features such as maximum singular value and zero crossing frequency. The percentage difference in feature values for each segment with the progression of fatigue is calculated. Results show that the recorded signals are complex, non‐stationary, multicomponent, and random in nature. Maximum singular value represents the non‐stationarity of a signal, with an increasing trend towards the fatigue condition. Zero crossing frequency represents the complexity or randomness in the signals and it decreases with the progression of fatigue. It is found that both the features are statistically significant with p < 0.01. It appears that the synchrosqueezed continuous wavelet transform and singular value features are able to analyse fatigue in surface electromyography signals.

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Synchrosqueezing transform based feature extraction from EEG signals for emotional state prediction

Cited by 54 publications

References 32 publications

Recognizing Emotions Evoked by Music Using CNN-LSTM Networks on EEG Signals

Recognizing Emotions Evoked by Music Using CNN-LSTM Networks on EEG Signals

Dynamic contraction and fatigue analysis in biceps brachii muscles using synchrosqueezed wavelet transform and singular value features

Muscle fatigue analysis in biceps brachii surface electromyography signals using synchrosqueezed Morlet wavelet and singular value decomposition

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