[PDF][PDF] A Comprehensive Review for Emotion Detection Based on EEG Signals: Challenges, Applications, and Open Issues.
A Abdulrahman, M Baykara - Traitement du Signal, 2021 - researchgate.net
Accepted: 25 July 2021 Emotion classification based on physiological signals has become a
hot topic in the past decade. Many studies have attempted to classify emotions using various …
hot topic in the past decade. Many studies have attempted to classify emotions using various …
An intelligent neuromarketing system for predicting consumers' future choice from electroencephalography signals
Abstract Neuromarketing utilizes Brain-Computer Interface (BCI) technologies to provide
insight into consumers responses on marketing stimuli. In order to achieve insight …
insight into consumers responses on marketing stimuli. In order to achieve insight …
BCI-based consumers' choice prediction from EEG signals: an intelligent neuromarketing framework
Neuromarketing relies on Brain Computer Interface (BCI) technology to gain insight into how
customers react to marketing stimuli. Marketers spend about $750 billion annually on …
customers react to marketing stimuli. Marketers spend about $750 billion annually on …
EEG based emotion classification using “correlation based subset selection”
DD Chakladar, S Chakraborty - Biologically inspired cognitive architectures, 2018 - Elsevier
Emotion detection is one of the popular research topics in “Brain–Computer Interfacing”
where researchers are trying to find the various emotional states of people. EEG signal is …
where researchers are trying to find the various emotional states of people. EEG signal is …
A novel approach for emotion recognition based on EEG signal using deep learning
Emotion can be defined as a voluntary or involuntary reaction to external factors. People
express their emotions through actions, such as words, sounds, facial expressions, and …
express their emotions through actions, such as words, sounds, facial expressions, and …
A hybrid fuzzy cognitive map/support vector machine approach for EEG-based emotion classification using compressed sensing
Due to the high dimensional, non-stationary and non-linear properties of
electroencephalogram (EEG), a significant portion of research on EEG analysis remains …
electroencephalogram (EEG), a significant portion of research on EEG analysis remains …
Intelligent Transportation Activity Recognition Using Deep Belief Network
Comprehending and analyzing a diverse range of transportation modalities within urban
environments is paramount for efficient traffic management and the development of smart …
environments is paramount for efficient traffic management and the development of smart …
EEG based emotion monitoring using wavelet and learning vector quantization
EC Djamal, P Lodaya - 2017 4th international conference on …, 2017 - ieeexplore.ieee.org
Emotional identification is necessary for example in Brain Computer Interface (BCI)
application and when emotional therapy and medical rehabilitation take place. Some …
application and when emotional therapy and medical rehabilitation take place. Some …
Affective recognition from EEG signals: an integrated data-mining approach
F Mendoza-Palechor, ML Menezes… - Journal of Ambient …, 2019 - Springer
Emotions play an important role in human communication, interaction, and decision making
processes. Therefore, considerable efforts have been made towards the automatic …
processes. Therefore, considerable efforts have been made towards the automatic …
1D convolutional neural networks versus automatic classifiers for known LPI radar signals under white gaussian noise
A Yildirim, S Kiranyaz - IEEE Access, 2020 - ieeexplore.ieee.org
In this study we analyze the signal classification performances of various classifiers for
deterministic signals under the additive White Gaussian Noise (WGN) in a wide range of …
deterministic signals under the additive White Gaussian Noise (WGN) in a wide range of …