The automatic recognition of human emotions is of great interest in the context of multimedia applications and brain-computer interfaces. While users’ emotions can be assessed based on questionnaires, the results may be biased because the answers could be influenced by social expectations. More objective measures of emotions can be obtained by studying the users' physiological responses. The present database has been constructed in particular to evaluate the usefulness of electroencephalography (EEG) for emotion recognition in the context of audio-visual stimuli, but it also contains simultaneous physiological recordings (electrocardiogram, respiration, blood oxygen level, pulse rate, galvanic skin response) in addition to the EEG data. To the best of our knowledge, this database is the first publicly available one containing high-resolution (HR) EEG recordings for studies of emotion.

In order to study the emotions elicited by audio-visual stimuli (film excerpts), we recruited 40 subjects to participate in an experiment. During the experiment, we presented 13 emotional videos (7 videos for positive emotions and 6 videos for negative emotions) and 13 neutral videos to the subjects. The subjects were comfortably installed on a chair at about 1m distance from the 21'' computer which was used for the presentation of the stimuli.

The experiment was implemented in E-Prime 2.0 (Psychology Software Tools, Pittsburgh, PA) according to the experimental protocol shown in Figure 1. Except for the test trial, both the neutral videos and the emotional videos were presented in an arbitrary order. The emotional stimuli were selected from the FilmStim database (A. Schaefer, F. Nils, X. Sanchez, and P. Philippot, “Assessing the effectiveness of a large database of emotion-eliciting films: a new tool for emotion researchers,” Cognition & Emotion, vol. 24, no. 7, pp. 1153–1172, 2010, http://nemo.psp.ucl.ac.be/FilmStim/) and were between 40 s and 6 min long. After each emotional video, the subjects evaluated the emotions felt during the video on a negative (-1) - neutral (0) - positive (1) discrete scale. HR-EEG recordings and physiological measurements (1-channel electrocardiogram, respiration, blood oxygen level, pulse rate) were acquired with a 257-channel EGI system (EGI, Electrical Geodesics Inc., Eugene, USA) at a sampling rate of 1000 Hz and galvanic skin response measurements were recorded simultaneously using a SenseWear sensor (BodyMedia Inc.) with a sampling rate of 31 Hz. The experiment took place in a shielded room in Pontchaillou hospital, Rennes, France, and was approved by the local ethical review board of Inserm.

The database includes the EEG and other physiological recordings of the 40 subjects collected during the viewing of neutral and emotional videos and for the black screen periods. The data are provided in Matlab file format. Further information on the subjects (age and gender), the individual ratings of the videos (self-assessment labels), and the experiment (order of presentation of the videos) is also included in the Matlab files, along with all the necessary details on preprocessing and data analysis required to reproduce the results presented in (H. Becker, J. Fleureau, P. Guillotel, F. Wendling, I. Merlet, and L. Albera, “Emotion recognition based on high-resolution EEG recordings and reconstructed brain sources”, submitted to IEEE Transactions on Affective Computing, 2016). Moreover, we provide a Matlab script to read this data. More details about the data and the contents of the Matlab files are 

Please cite the following paper in your publications making use of the HR-EEG4EMO database:


H. Becker, J. Fleureau, P. Guillotel, F. Wendling, I. Merlet, and L. Albera, “Emotion recognition based on high-resolution EEG recordings and reconstructed brain sources”, submitted to IEEE Transactions on Affective Computing, 2016

In the following, we describe the contents of all provided Matlab files and provide additional information on the data recorded for each subject.

The following table lists describe the 13 emotional videos that have been selected from the FilmStim database [1] (

 ). The descriptions of the scenes are reproduced from FilmStim.

The following table provides some information on the data provided for each subject. Note that the evaluation of the EEG quality is subjective. The suitability of the subject for inclusion in the EEG-based valence recognition analysis is rated on a scale from 1 (very good) to 6 (very bad) and is also subjective. The number of videos corresponds to the number of videos for which the subject reported to have felt positive or negative emotions. For the subjects for which marker signals for the beginning and ends of the videos were not present in the EEG, the synchronization was performed based on the timestamps of the EEG recordings and the E-Prime files.

For each subject, we provide the following data files:


Information relevant for the data analysis conducted in the associated journal paper:

In order to enable other researchers to reproduce the results described in [2] based on the reconstruction of brain activity on the cortical surface, we provide the following files that essentially characterize the employed head model:

Along with the data, we provide two Matlab scripts, one for reading the data and the second one for extracting the features described in [2].

 includes the following functionalities:

list information of analyzed data intervals

display the presentation order of neutral and emotional videos

visualize the GSR signals for each emotional video

interpolate bad EEG channels from the 4 nearest sensors

extract physiological recordings from data array

combine EEG and all physiological recordings (including the GSR) in one matrix

 permits to compute the following features for each EEG channel and for each emotional brain region (see [2] for more details):

powers in (4 – 8 Hz), (8 – 13 Hz), (13 – 30 Hz), low (30 – 45 Hz), and high (55 – 80 Hz) bands

connectivity in the whole frequency range

higher order crossings for, low, and high bands up to derivative of order 20

higher order crossings for the whole frequency range up to derivative of order 50

statistics (minimum, maximum, median, standard deviation, mean and maximum of the first two derivatives, skewness, kurtosis)

spectral crest factor in, low, and high bands

application of Independent Component Analysis (ICA) with Wavelet Denoising (WD) or not.

Schaefer, F. Nils, X. Sanchez, and P. Philippot, “Assessing the effectiveness of a large database of emotion-eliciting films: a new tool for emotion researchers,” Cognition & Emotion, vol. 24, no. 7, pp. 1153–1172, 2010

Becker, J. Fleureau, P. Guillotel, F. Wendling, I. Merlet, and L. Albera, “Emotion recognition based on high-resolution EEG recordings and reconstructed brain sources”, submitted to 

To download the database, we ask you to provide your name, email address and affiliation and to fill in and sign the 

EULA (End User License Agreement) form available here

, by which you agree to the terms of use described below. Then send an email to 

 asking for the database, with the EULA file attached and the above-required information. You will receive instructions on how to download the dataset via the provided email address.

We may store the data you supplied in order to contact you later about benchmark related matters. The data will not be used in any other way.

The user may only use the dataset for academic research. The user may not use the database for any commercial purposes. Commercial purposes include, but are not limited to:

proving the efficiency of commercial systems,

training or testing of commercial systems,

using screenshots of data from the database in advertisements,

The user may not distribute the dataset or portions thereof in any way, with the exception of using small portions of data for the exclusive purpose of clarifying academic publications or presentations. Note that publications will have to comply with the terms stated in article 4.

The user may only use the database after this End User License Agreement (EULA) has been signed and returned to the dataset administrators. The signed EULA should be returned in digital format by including it to the mail when requesting access to the dataset. Upon receipt of the EULA, a username and password to access the dataset will be issued. The user may not grant anyone access to the database by giving out their user name and password.

Publications include not only papers, but also presentations for conferences or educational purposes. All documents and papers that report on research that uses the 

Becker, J. Fleureau, P. Guillotel, F. Wendling, I. Merlet, and L. Albera, “Emotion recognition based on high-resolution EEG recordings and reconstructed brain sources”, submitted to 

The database comes without any warranty. University of Rennes 1, Inserm, LTSI and InterDigital R&D France cannot be held accountable for any damage (physical, financial or otherwise) caused by the use of the database.

HR-EEG4EMO Dataset

HR-EEG4EMO Dataset

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