Emotion Recognition From EEG Using Higher Order Crossings

By Panagiotis C. Petrantonakis, Leontios J. Hadjileontiadis. IEEE Transactions on Information Technology in Biomedicine, 2010

679 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Experimental and Cognitive Psychology

View Paper: https://www.semanticscholar.org/paper/c8d665766b6e7661dbdef3fe80ccd5be7579aafb

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In Cognitive Neuroscience

In Experimental and Cognitive Psychology

Citations in the corpus

An Efficient LSTM Network for Emotion Recognition From Multichannel EEG Signals (2020, background): “dimension feature [33] and higher order crossing feature [34],”
Spatial–Temporal Recurrent Neural Network for Emotion Recognition (2017, methodology): “For instances, for EEG-based emotion recognition, descriptors, such as high order crossings [6]”
EEG Emotion Recognition Using Dynamical Graph Convolutional Neural Networks (2020, background): “, Hjorth feature [23], fractal dimension feature [24] and higher order crossing feature [25], mainly capture the temporal information of EEG signals.”
Feature Extraction and Selection for Emotion Recognition from EEG (2014, neutral): “Life Robot., vol. 1, no. 1, pp. 15-19, Mar. 1997.”
Current Status, Challenges, and Possible Solutions of EEG-Based Brain-Computer Interface: A Comprehensive Review (2020, background): “Petrantonakis and Hadjileontiadis (2010) presented a user-independent emotion recognition strategy that achieved an 83.33% recognition rate for six emotion categories.”
Emotions Recognition Using EEG Signals: A Survey (2019, methodology): “59% pleasure [52] Offline Ind SVM Polynomial 83.”
A Review on the Computational Methods for Emotional State Estimation from the Human EEG (2013, methodology): “A vector of the simple HOCs is constructed to contain the features related to emotion.”
Human emotion recognition from EEG-based brain–computer interface using machine learning: a comprehensive review (2022, methodology): “…potential (ERP), histogram analysis method, Hjorth features/parameters: activity, mobility and complexity [87], higher-order crossing (HOC) [88, 89], principal component analysis (PCA) [90], independent component analysis (ICA), and Higuchi’s fractal dimensions (FD) as a measurement of…”
Emotional state classification from EEG data using machine learning approach (2014, neutral): “[45] demonstrated a userindependent emotion recognition system.”
Affect Detection: An Interdisciplinary Review of Models, Methods, and Their Applications (2010, methodology): “Although not often used in the AC literature, EEG-based techniques are being increasingly used [62], [63].”