Human emotion modeling based on salient global features of EEG signal

Ahmed, Tazrin; Islam, Mohammad Mahfuzul; Ahmad, Mohiuddin

doi:10.1109/icaee.2013.6750341

Cited by 8 publications

(6 citation statements)

References 9 publications

(17 reference statements)

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“…Combination of the classifiers increase the accuracy and stability. Techniques in [1] [15] [20] have extracted features such as statistical, Fast Fourier Transform (FFT) and Discrete Wavelet Transform (DWT) for emotion recognition. In this, the highest accuracy obtained was by DWT features 60.15% as compare to statistical features and FFT features.…”

Section: Higher Order Spectra (Hos)mentioning

confidence: 99%

Emotion Recognition Based on Various Physiological Signals - A Review

Londhe¹,

Borse²

2018

IJCT

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Emotion recognition is one of the biggest challenges in human-human and human-computer interaction. There are various approaches to recognize emotions like facial expression, audio signals, body poses, and gestures etc. Physiological signals play vital role in emotion recognition as they are not controllable and are of immediate response type. In this paper, we discuss the research done on emotion recognition using skin conductance, skin temperature, electrocardiogram (ECG), electromyography (EMG), and electroencephalogram (EEG) signals. Altogether, the same methodology has been adopted for emotion recognition techniques based upon various physiological signals. After survey, it is understood that none of these methods are fully efficient standalone but the efficiency can be improved by using combination of physiological signals. The study of this paper provides an insight on the current state of research and challenges faced during emotion recognition using physiological signals, so that research can be advanced for better recognition.

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Section: Higher Order Spectra (Hos)mentioning

confidence: 99%

Emotion Recognition Based on Various Physiological Signals - A Review

Londhe¹,

Borse²

2018

IJCT

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“…The adaptive neuro fuzzy inference system is also proposed for classifying and analyzing the emotions based on the features selected. In [11].The efficacy of extracted features for classifying five types of emotional states relax, mental task, memory related task, pleasant, and fear. For this purpose support vector machine classifier was employed to classify the five emotional states by using salient global features.…”

Section: Related Workmentioning

confidence: 99%

Novel Algorithm for Feature Extraction and Classification of EEG signals

G¹,

P²,

S³

2015

IJERT

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This paper aims in developing an algorithm for feature extraction by using Discrete wavelet transform (DWT).Feature extraction from Electroencephalogram (EEG) signal for emotion recognition provides an adequate information. In this paper DWT is used to extract significant features representing emotion in Brain Computer Interface (BCI) in EEG signals. The EEG signals are acquired in real time using Neurosky Mind wave sensor and processed in real time using wavelets for feature extraction. For a given EEG signal brain waves are identified from DWT Spectrum. These brain waves quantify emotions. The proposed algorithm based on DWT, is modeled in Matlab and it is validated using 10 different EEG samples. Features such as energy are found to identify the intensity level of different bands of EEG signal .The best results were obtained by using Bior 5.5 wavelet for signal decomposition and to obtain the accurate frequency bands.

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“…4 shows that if using modified grayscale image, the mouth's corner and contour would be clearer than that of original one. To further remove the mouth contour, the enhanced grayscale image with only mouth-corners can be derived by (3) ( , ) ( , ) ( , 1) Img x y IGray x y IGray x y = + −…”

Section: B Mcfs Extractionmentioning

confidence: 99%

Multi-level smile intensity measuring based on mouth-corner features for happiness detection

Chang

Lin

Kuan

et al. 2014

2014 International Conference on Orange Technologies

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This work presents a low-complexity algorithm for multi-level smile intensity measurement based on mouth-corner features (MCFs). The proposed MCFs-based algorithm uses the mouth region images and accumulates the value of enhanced grayscale pixels along the horizontal axis. To further analyze the mouth shape, the local maximum information of the accumulated values is extracted to identify the height and width sizes of an opening mouth. Finally, the normalized threshold method is adopted to measure the smile intensity. The experimental results have shown that the proposed approach can achieve an average accuracy rate of 80% for the smiling measuring with four different levels of intensity. Moreover, the proposed algorithm can measure the smile intensity in a multi-face environment. Such results have demonstrated the efficiency and the feasibility of proposed algorithm.

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Human emotion modeling based on salient global features of EEG signal

Cited by 8 publications

References 9 publications

Emotion Recognition Based on Various Physiological Signals - A Review

Emotion Recognition Based on Various Physiological Signals - A Review

Novel Algorithm for Feature Extraction and Classification of EEG signals

Multi-level smile intensity measuring based on mouth-corner features for happiness detection

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