Name
Abstract | ||
|---|---|---|
Recently, emotion classification from EEG data has attracted much attention with the rapid development of dry electrode techniques, machine learning algorithms, and various real-world applications of brain-computer interface for normal people. Until now, however, researchers had little understanding of the details of relationship between different emotional states and various EEG features. To improve the accuracy of EEG-based emotion classification and visualize the changes of emotional states with time, this paper systematically compares three kinds of existing EEG features for emotion classification, introduces an efficient feature smoothing method for removing the noise unrelated to emotion task, and proposes a simple approach to tracking the trajectory of emotion changes with manifold learning. To examine the effectiveness of these methods introduced in this paper, we design a movie induction experiment that spontaneously leads subjects to real emotional states and collect an EEG data set of six subjects. From experimental results on our EEG data set, we found that (a) power spectrum feature is superior to other two kinds of features; (b) a linear dynamic system based feature smoothing method can significantly improve emotion classification accuracy; and (c) the trajectory of emotion changes can be visualized by reducing subject-independent features with manifold learning. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1016/j.neucom.2013.06.046 | Neurocomputing |
Keywords | Field | DocType |
manifold learning,eeg feature,emotional state classification,emotion task,different emotional state,eeg-based emotion classification,emotion classification,eeg data,emotion classification accuracy,various eeg feature,emotion change,brain computer interface,support vector machine | Pattern recognition,Brain–computer interface,Support vector machine,Emotion classification,Smoothing,Artificial intelligence,Eeg data,Nonlinear dimensionality reduction,Electroencephalography,Mathematics,Machine learning,Trajectory | Journal |
Volume | ISSN | Citations |
129, | 0925-2312 | 93 |
PageRank | References | Authors |
3.48 | 14 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiao-Wei Wang | 1 | 596 | 59.78 |
| Dan Nie | 2 | 128 | 4.83 |
| Bao-Liang Lu | 3 | 2361 | 182.91 |