No abstract

Human dependence on computers is increasing day by day; thus, human interaction with computers must be more dynamic and contextual rather than static or generalized. The development of such devices requires knowledge of the emotional state of the user interacting with it; for this purpose, an emotion recognition system is required. Physiological signals, specifically, electrocardiogram (ECG) and electroencephalogram (EEG), were studied here for the purpose of emotion recognition. This paper proposes novel entropy-based features in the Fourier–Bessel domain instead of the Fourier domain, where frequency resolution is twice that of the latter. Further, to represent such non-stationary signals, the Fourier–Bessel series expansion (FBSE) is used, which has non-stationary basis functions, making it more suitable than the Fourier representation. EEG and ECG signals are decomposed into narrow-band modes using FBSE-based empirical wavelet transform (FBSE-EWT). The proposed entropies of each mode are computed to form the feature vector, which are further used to develop machine learning models. The proposed emotion detection algorithm is evaluated using publicly available DREAMER dataset. K-nearest neighbors (KNN) classifier provides accuracies of 97.84%, 97.91%, and 97.86% for arousal, valence, and dominance classes, respectively. Finally, this paper concludes that the obtained entropy features are suitable for emotion recognition from given physiological signals.

No abstract

No abstract

Electroencephalogram (EEG) signals are the recording of brain electrical activity, commonly used for emotion recognition. Different EEG rhythms carry different neural dynamics. EEG rhythms are separated using tunable Q-factor wavelet transform (TQWT). Several features like mean, standard deviation, information potential are extracted from the TQWT-based EEG rhythms. Machine learning classifiers are used to differentiate various emotional states automatically. The authors have validated the proposed model using a publicly available database. Obtained classification accuracy of 92.9% proves the candidature of the proposed method for emotion identification.

<p>Article propose a framework for automatic diagnosis of breast cancer using ultrasound images</p>

<p>Article propose a framework for automatic diagnosis of breast cancer using ultrasound images</p>