Stimuli and Experiment
SubjectsFifteen Chinese subjects (7 males and 8 females; MEAN: 23.27, STD: 2.37) participated in the experiments. To protect personal privacy, we hide their names and indicate each subject with a number from 1 to 15.
Dataset SummaryThe SEED consists of two parts:
In the "Preprocessed_EEG" folder, there are files that contain downsampled, preprocessed and segmented versions of the EEG data in MATLAB (.mat file).
The data was downsampled to 200 Hz.
A bandpass frequency filter from 0 - 75 Hz was applied.
We extracted the EEG segments that correspond to the duration of each movie.
There were a total of 45 files with the extention .mat (MATLAB files), one per experiment.
Each subject performed the experiment three times with an interval of approximately one week.
Each subject file contains 16 arrays.
Fifteen arrays contain segmented preprocessed EEG data of 15 trials in one experiment (eeg_1~eeg_15, channel×data).
Array name labels contain the label of the corresponding emotional labels
(-1 for negative, 0 for neutral and +1 for positive).
The detailed order of the channels is included in the dataset.
The EEG cap according to the international 10 - 20 system for 62 channels is shown below:
- In the "Extracted_Features" folder, there are files that contain extracted differential entropy (DE) features of the EEG signals, which was first proposed in . These data are well suited to those who want to quickly test a classification method without preprocessing the raw EEG data. The file format is the same as the Data_preprocessed. We also computed differential asymmetry (DASM) and rational asymmetry (RASM) features as the differences and ratios between the DE features of 27 pairs of hemispheric asymmetry electrodes. All of the features were further smoothed with a conventional moving average and linear dynamic systems (LDS) approaches. For more details about feature extraction and feature smoothing, please refer to  and .
If you feel that the dataset is helpful for your study, please add the following references to your publications.
1. Wei-Long Zheng, and Bao-Liang Lu, Investigating Critical Frequency Bands and Channels for EEG-based Emotion Recognition with Deep Neural Networks, accepted by IEEE Transactions on Autonomous Mental Development (IEEE TAMD) 7(3): 162-175, 2015. [link] [BibTex]
2. Ruo-Nan Duan, Jia-Yi Zhu and Bao-Liang Lu, Differential Entropy Feature for EEG-based Emotion Classification, Proc. of the 6th International IEEE EMBS Conference on Neural Engineering (NER). 2013: 81-84. [link] [BibTex]