Citation map · Study

SZodixStudyAcademic Library

Shelves Open access

Study · an open academic library·Metadata from OpenAlex (CC0)·Shelves·OpenAlex metadata, de-duplicated

Emotion classification using EEG signals based on tunable‐ Q wavelet transform

16 references·1 citing·38 second hop

FromToDepth

this work references cited by second hop

References

1994Measuring emotion: The self-assessment manikin and the semantic differential9,531 cited
2011DEAP: A Database for Emotion Analysis ;Using Physiological Signals5,082 cited
2017DREAMER: A Database for Emotion Recognition Through EEG and ECG Signals From Wireless Low-cost Off-the-Shelf Devices1,130 cited
2010EEG-Based Emotion Recognition in Music Listening1,023 cited
2017Identifying Stable Patterns over Time for Emotion Recognition from EEG995 cited
2015Improving BCI-based emotion recognition by combining EEG feature selection and kernel classifiers506 cited
2009Emotion classification based on gamma-band EEG413 cited
2011Emotion Assessment From Physiological Signals for Adaptation of Game Difficulty402 cited
2010Real-Time EEG-Based Human Emotion Recognition and Visualization313 cited
2014Classifying Different Emotional States by Means of EEG-Based Functional Connectivity Patterns301 cited
2011EEG-Based Emotion Recognition Using Frequency Domain Features and Support Vector Machines273 cited
2008Emotion recognition using brain activity270 cited
2010Emotion Recognition from Brain Signals Using Hybrid Adaptive Filtering and Higher Order Crossings Analysis260 cited
2016Human emotion recognition and analysis in response to audio music using brain signals

Only edges whose both ends are imported exist here. Widen the year window, or show the whole map. Back to the work

223 cited

2017Optimal Feature Selection and Deep Learning Ensembles Method for Emotion Recognition From Human Brain EEG Sensors213 cited

2016An approach to EEG-based emotion recognition using combined feature extraction method154 cited

Cited by

2020Classification of emotions from EEG signals using time‐order representation based on the S‐transform and convolutional neural network55 cited