Emotional state classification from EEG data using machine learning approach
By Xiao-Wei Wang, Dan Nie, Bao-Liang Lu. Neurocomputing, 2014
715 citations recorded by the source corpus.
Subfields: Cognitive Neuroscience, Experimental and Cognitive Psychology
View Paper: https://www.semanticscholar.org/paper/d443ff1955e9d544063693ce29016f94b98e6ab3
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Cognitive Neuroscience
- Wei-Long Zheng
- Bao-Liang Lu
- Wei Liu
- Jinpeng Li
- Huiguang He
- Jielin Qiu
- Zhaoxiang Zhang
- Andrzej S. Cichocki
In Experimental and Cognitive Psychology
- Wei-Long Zheng
- Bao-Liang Lu
- Wei Liu
- Jinpeng Li
- Huiguang He
- Jielin Qiu
- Zhaoxiang Zhang
- Andrzej S. Cichocki
Citations in the corpus
- Emotions Recognition Using EEG Signals: A Survey (2019, neutral): “3% [91] Offline Dep SVM Linear 87.”
- Identifying Stable Patterns over Time for Emotion Recognition from EEG (2016, background): “[16] Video 62 Power spectrum features, wavelet features, nonlinear dynamical features, 6 participants, SVM Positive and negative 87.”
- Investigating Critical Frequency Bands and Channels for EEG-Based Emotion Recognition with Deep Neural Networks (2015, background): “and accuracy rate in the literature [24], [40], [42].”
- Transfer Learning for EEG-Based Brain–Computer Interfaces: A Review of Progress Made Since 2016 (2020, neutral): “3) Feature extraction, for which time domain, frequency domain [20], time-frequency domain, Riemannian space [21] and/or functional brain connectivity [22] features could be used.”
- EEG Based Emotion Recognition: A Tutorial and Review (2022, methodology): “, discriminant analysis [101], support vector machine [70], K-nearest neighbor [78], Bayesian method [122], Random forest [123], perceptron [124], etc, as well as unsupervised learning methods such as manifold learning [74], clustering [125], and so on.”
- EEG-Based Emotion Recognition Using Regularized Graph Neural Networks (2019, background): “[6] compared power spectral density features (PSD), wavelet features and nonlinear dynamical features with a Support Vector Machine (SVM) classifier.”
- EmotionMeter: A Multimodal Framework for Recognizing Human Emotions (2019, background): “existing studies have shown the effectiveness and feasibility of EEG [14], [27], [31], [32].”
- Comparing Recognition Performance and Robustness of Multimodal Deep Learning Models for Multimodal Emotion Recognition (2021, methodology): “Various methods have been proposed for EEG-based emotion recognition [11]–[17], and one of the reasons is that EEG signals are more accurate and difficult to deliberately change by users.”
- Hierarchical Convolutional Neural Networks for EEG-Based Emotion Recognition (2017, background): “compared power spectrum, wavelet, and nonlinear dynamic feature to classify emotion states and indicated that power spectrum was superior to the other two features [23].”
- EEG-based emotion classification using deep belief networks (2014, background): “They proposed an approach to track the trajectory of emotion changes with manifold learning [6].”
- EEG-Based Emotion Recognition via Channel-Wise Attention and Self Attention (2023, background): “, Bayesian, support vector machines, decision trees, and deep learning classifiers [34].”