Neuromarketing has become an academic and commercial area of interest, as the
advancements in neural recording techniques and interpreting algorithms have made it an …

Neuromarketing is the application of neuroscience to the understanding of consumer
preferences toward products and services. As such, it studies the neural activity associated …

Even with an unprecedented breakthrough of deep learning in electroencephalography
(EEG), collecting adequate labelled samples is a critical problem due to laborious and time …

Semisupervised learning holds significant academic and practical importance in the realm of
EEG-based emotion recognition. Currently, a multitude of research endeavors are dedicated …

This paper proposes emotion detection using Electroencephalography (EEG) signal based
on Linear Formulation of Differential Entropy (LF-D f E) feature extractor and BiLSTM …

Emotion plays an important role in human interaction. People can explain their emotions in
terms of word, voice intonation, facial expression, and body language. However, brain …

Deep learning has been used popularly and successfully in state of art researches to
classify different types of images. However, so far, the applications of deep learning methods …

Recognizing emotional state of human using brain signal is an active research domain with
several open challenges. In this research, we propose a signal spectrogram image based …

The Relevance Feedback (RF) process relies on accurate and real-time relevance
estimation of feedback documents to improve retrieval performance. Since collecting explicit …

Neurofeedback, utilizing an electroencephalogram (EEG) and/or a functional near-infrared
spectroscopy (fNIRS) device, is a real-time measurement of brain activity directed toward …