Figure 8. Sample Emotion Classification Result-Classification of Human Emotion from Deap EEG Signal Using Hybrid Improved Neural Networks with Cuckoo Search
| Main Authors: | M. Sreeshakthy, J. Preethi |
|---|---|
| Format: | info Image |
| Bahasa: | eng |
| Terbitan: |
, 2015
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| Subjects: | |
| Online Access: |
https://www.edusoft.ro/brain/index.php/brain/issue/view/34 |
Daftar Isi:
- For all the 32 participants the EEG signal has to be sampled and process their emotions. The emotions are depends on the music and video clips. In this paper the video clips are changed from one person to other person. Here hybrid feed forward neural networks with radial basis function; probabilistic neural network classifier is used to classify the emotions from EEG. PNN is very fast and insensitive neural network which provides the optimized classification result. Compare to the multi layer perception neural network it provide accurate result. It classifies the emotion into two different groups like arousal and valence. Figure 8 shows that the model implemented result of emotion classification and person identification.