Emotion Recognition based on EEG using LSTM Recurrent Neural Network

By Salma Alhagry, Aly Aly Fahmy, Reda A. El-Khoribi. 2017

559 citations recorded by the source corpus.

Emotion is the most important component in daily interaction between people. Nowadays, it is important to make the computers understand user’s emotion who interacts with it in human-computer interaction (HCI) systems. Electroencephalogram (EEG) signals are the main source of emotion in our body. Recently, emotion recognition based on EEG signals have attracted many researchers and many methods were reported. Different types of features were extracted from EEG signals then different types of classifiers were applied to these features. In this paper, a deep learning method is proposed to recognize emotion from raw EEG signals. Long-Short Term Memory (LSTM) is used to learn features from EEG signals then the dense layer classifies these features into low/high arousal, valence, and liking. DEAP dataset is used to verify this method which gives an average accuracy of 85.65%, 85.45%, and 87.99% with arousal, valence, and liking classes, respectively. The proposed method introduced high average accuracy in comparison with the traditional techniques.

Subfields: Cognitive Neuroscience, Experimental and Cognitive Psychology, Human-Computer Interaction

View Paper: https://www.semanticscholar.org/paper/6a4d4bf50c412635335af1b8444d427d9c3e8c86

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

1. Rencheng Song
2. Feng Wan
3. Lin Shu
4. Alexandre Gramfort
5. Dan Liao
6. Zhenqi Li
7. Wei Tao
8. Ziyi Li

In Experimental and Cognitive Psychology

1. Rencheng Song
2. Feng Wan
3. Alexandre Gramfort
4. Wei Tao
5. Xun Chen
6. Chang Li
7. Yu Liu
8. Tiago H. Falk

In Human-Computer Interaction

1. Xiangmin Xu

Citations in the corpus

• EEG-Based Emotion Recognition via Channel-Wise Attention and Self Attention (2023, methodology): “For example, Alhagry et al. proposed an end-to-end deep learning neural network to recognize emotion from raw EEG signals, which used an LSTM-RNN to learn features from EEG signals and used the dense layer for classification [15].”
• A Review of Emotion Recognition Using Physiological Signals (2018, background): “In the work of [125], an end-to-end structure was proposed, in which the raw EEG signals in 5s-long segments were sent to the LSTM networks, in which autonomously learned features and an average accuracy of 85.”
• Deep learning-based electroencephalography analysis: a systematic review (2019, methodology): “able 6: Most often used datasets by domain. Datasets that were only used by one study are grouped under "Other" for each category. Main domain Dataset # articles References Affective DEAP 9 [100, 6, 17, 102, 200, 103, 42, 79, 96] SEED 3 [220, 103, 228] BCI BCI Competition 13 [43, 87, 146, ”