Bispectral analysis gives us more information than power spectral-based analysis

Hagihira, Satoshi; Takashina, Masaki; Mori, Tadashige; Mashimo, Takashi

doi:10.1093/annhyg/aeh554

Cited by 8 publications

(4 citation statements)

References 6 publications

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“…4 9 Nevertheless, electroencephalographic parameters evaluated during nitrous oxide and isoflurane anaesthesia are well correlated with BIS at surgical levels, 17 and BIS is known to give more information than power spectrum-based analysis. 18 In conclusion, administration of tramadol, together with ketorolac, to prevent postoperative pain does not further modify the values of BIS during anaesthesia with sevoflurane and remifentanil when the BIS is kept between 40 and 50.…”

Section: Discussionmentioning

confidence: 81%

Tramadol does not modify the Bispectral Index during anaesthesia with sevoflurane and remifentanil

Fodale

Praticò

Tescione

et al. 2005

British Journal of Anaesthesia

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Section: Discussionmentioning

confidence: 81%

Tramadol does not modify the Bispectral Index during anaesthesia with sevoflurane and remifentanil

Fodale

Praticò

Tescione

et al. 2005

British Journal of Anaesthesia

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“…Bispectrum analysis has been used as a signal processing technique for analyzing the EEG signal [14] and also used in medical research, such as in cases related to anesthesia [15,16], meditation [17], vigilance tests [18] and recently in emotion recognition [19,20], but the recognition rate result for the latter was somewhat lower than desired. Hosseini [19] used seven features from bispectrum analysis taken from each of five EEG channels to recognize whether the subject was stressed; Hosseini used a support vector machine (SVM) as the classifier and a genetic algorithm as the feature selection algorithm.…”

Section: Literature Reviewmentioning

confidence: 99%

Development of Filtered Bispectrum for EEG Signal Feature Extraction in Automatic Emotion Recognition Using Artificial Neural Networks

2017

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Abstract:The development of automatic emotion detection systems has recently gained significant attention due to the growing possibility of their implementation in several applications, including affective computing and various fields within biomedical engineering. Use of the electroencephalograph (EEG) signal is preferred over facial expression, as people cannot control the EEG signal generated by their brain; the EEG ensures a stronger reliability in the psychological signal. However, because of its uniqueness between individuals and its vulnerability to noise, use of EEG signals can be rather complicated. In this paper, we propose a methodology to conduct EEG-based emotion recognition by using a filtered bispectrum as the feature extraction subsystem and an artificial neural network (ANN) as the classifier. The bispectrum is theoretically superior to the power spectrum because it can identify phase coupling between the nonlinear process components of the EEG signal. In the feature extraction process, to extract the information contained in the bispectrum matrices, a 3D pyramid filter is used for sampling and quantifying the bispectrum value. Experiment results show that the mean percentage of the bispectrum value from 5 × 5 non-overlapped 3D pyramid filters produces the highest recognition rate. We found that reducing the number of EEG channels down to only eight in the frontal area of the brain does not significantly affect the recognition rate, and the number of data samples used in the training process is then increased to improve the recognition rate of the system. We have also utilized a probabilistic neural network (PNN) as another classifier and compared its recognition rate with that of the back-propagation neural network (BPNN), and the results show that the PNN produces a comparable recognition rate and lower computational costs. Our research shows that the extracted bispectrum values of an EEG signal using 3D filtering as a feature extraction method is suitable for use in an EEG-based emotion recognition system.

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“…thalamic reticular nuclei and thalamocortical loops [5]. Then, we compared EEG bicoherence obtained from Fp 1 -A 1 (hemi-frontal) lead with that obtained from Fp 1 -Fp 2 (bi-frontal) lead [6]. EEG bicoherence obtained from a uni-frontal lead showed two significant peaks in the low frequency region, but EEG bicoherence obtained from the bifrontal lead was quite low in all bi-frequency planes.…”

Section: What Do the Peaks Of Eeg Bicoherence Indicate?mentioning

confidence: 99%

What we can know from bispectral analysis of EEG?

Hagihira

Takashina

Mori

et al. 2005

International Congress Series

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Bispectral analysis gives us more information than power spectral-based analysis

Cited by 8 publications

References 6 publications

Tramadol does not modify the Bispectral Index during anaesthesia with sevoflurane and remifentanil

Tramadol does not modify the Bispectral Index during anaesthesia with sevoflurane and remifentanil

Development of Filtered Bispectrum for EEG Signal Feature Extraction in Automatic Emotion Recognition Using Artificial Neural Networks

What we can know from bispectral analysis of EEG?

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