Classification of human emotion from EEG using discrete wavelet transform

Murugappan, M.; Ramachandran, Nagarajan; Yaacob, Sazali

doi:10.4236/jbise.2010.34054

Cited by 367 publications

(170 citation statements)

References 15 publications

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“…The joint highly time-frequency resolution, which cannot be achieved by either Fast Fourier Transform (FFT) or by Short Time Fourier Transform (STFT), obtained by wavelet transform yields a more reliable candidate for the extraction of potential features in EEG [15]. In the proposed approach, we employ the Mallat algorithm [16] to obtain the multi-resolution wavelet decomposition of EEG.…”

Section: 2wavelet-cspmentioning

confidence: 99%

Motor Imagery EEG Classification using Wavelet Common Spatial Boosting Pattern

2017

The 5th International Conference on Advanced Computer Science Applications and Technologies (ACSAT 2017)

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Abstract. Feature Extraction is one of the most important steps in brain-computer interface (BCI) systems. In particular, the common spatial patterns (CSP) is one of the most successful solutions which has been widely used in MI-BCIs. However, studies have reported that the performance of CSP heavily depends on its channels configuration. To the best of our current knowledge, it is not available to obtain the active channels related to brain activities of stroke patients in advance. Hence, we usually set a relatively broad channels or try to select a subject-specific channels when applying CSP to stroke patients. In this paper, we present a novel approach which employs wavelet transform and boosting algorithm to improve accuracy and robustness of the conventional CSP. In our proposed approach, the channel configurations are initially divided into multiple preconditions. Then, the informative features of the predefined channels are obtained using the Wavelet Common Spatial Pattern (W-CSP) algorithm that provided high-temporal-spectral resolution. Eventually, we train weak classifiers on the obtained features and combine these weak classifiers to a weighted combinational model using boosting strategy. Extensive experiments have been performed on datasets from the famous BCI competition III and IV. The results demonstrate its superior performance.

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Section: 2wavelet-cspmentioning

confidence: 99%

Motor Imagery EEG Classification using Wavelet Common Spatial Boosting Pattern

2017

The 5th International Conference on Advanced Computer Science Applications and Technologies (ACSAT 2017)

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“…Nevertheless, through the feature selection process, some researchers have found that other EEG channels were better suited for emotion recognition [20,21]. In the frequency domain, particular emotions are believed to affect specific frequency bands, such as alpha [22,23], gamma [24,25] and theta [26], and some researchers have incorporated all frequencies, delta, alpha, beta, theta and gamma [19,27], into their EEG research. In this work, we used all frequencies of brain signals and omitted none.…”

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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“…Silberstein [21] have focused on external effects on human emotions using EEG measurements for determining the level of attention of a subject to a visual stimulus. Murugappan et al [17] classified dynamic emotional content into five discrete emotions (disgust, happy, surprise, fear and neutral) based on Electroencephalography (EEG) signal. Recently, EEG-based emotion recognition using deep learning network with principal component improved classification accuracy compared to SVM and naïve Bayes classifier [12].…”

Section: 2mentioning

confidence: 99%

Multi-level assessment model for wellness service based on human mental stress level

Jung

Yoon

2016

Multimed Tools Appl

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In this paper, we measure human physiological changes from different body parts to quantify human mental stress level by using multimodal bio-sensors. By integrating these physiological responses, we generate bio-index and rule for the prediction of mental status, such as tension, normal, and relax. We also develop an inspection service middleware for analyzing health parameters such as electroencephalography (EEG), electrocardiography (ECG), oxygen saturation (SpO2), blood pressure (BP), and respiration rate (RR). In this service middleware, we use the multi-level assessment model for mental stress level that consists of three steps as follows; classification, reasoning, and decision making. The classification of datasets from bio-sensors is enabled by fuzzy logic and SVM algorithm. The reasoning uses the decision-tree model and random forest algorithm to classify the mental stress level from the health parameters. Finally, we propose a prediction model to make a decision for the wellness contents by using Expectation Maximization (EM).

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Classification of human emotion from EEG using discrete wavelet transform

Cited by 367 publications

References 15 publications

Motor Imagery EEG Classification using Wavelet Common Spatial Boosting Pattern

Motor Imagery EEG Classification using Wavelet Common Spatial Boosting Pattern

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

Multi-level assessment model for wellness service based on human mental stress level

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