A dataset recorded during development of an affective brain-computer music interface: calibration session

0. Sections
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1. Project
2. Dataset
3. Terms of Use
4. Contents
5. Method and Processing

1. PROJECT
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Title: Brain-Computer Music Interface for Monitoring and Inducing Affective States (BCMI-MIdAS)
Dates: 2012-2017
Funding organisation: Engineering and Physical Sciences Research Council (EPSRC)
Grant no.: EP/J003077/1 and EP/J002135/1.

2. DATASET
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EEG data from an affective Music Brain-Computer Interface: system calibration.

Description: This dataset accompanies the publication by Daly et al. (2018) and has been analysed in Daly et al. (2015) (please see Section 5 for full references). The purpose of the research activity in which the data were collected was to calibrate an affective brain-computer interface system to induce specific affective states by real-time online modification of synthetic music.

For this purpose, 20 healthy adult volunteers listened to music clips (40 s) targeting two affective states, as defined by valence and arousal (the first 20-s targeted state 1, while the remaining 20-s targeted state 2). Data were recorded over 1 session with 5 runs of 18 music trials each. The music clips were generated using a synthetic music generator.

The dataset contains the electroencephalogram (EEG), galvanic skin response (GSR) and electrocardiogram (ECG) data from 19 healthy adult participants while listening to the music clips, together with the reported affective state (valence and arousal values) and auxiliary variables.

This dataset is connected to 2 additional datasets:

1. EEG data from an affective Music Brain-Computer Interface: offline training to induce target emotional states. doi:
2. EEG data from an affective Music Brain-Computer Interface: online real-time control. doi:
Please note that the number of participants varies between datasets; however, participant codes are the same across all three datasets.

Publication Year: 2018

Creators: Nicoletta Nicolaou, Ian Daly

Contributors: Isil Poyraz Bilgin, James Weaver, Asad Malik, Alexis Kirke, Duncan Williams.

Principal Investigator: Slawomir Nasuto(EP/J003077/1).

Co-Investigator: Eduardo Miranda (EP/J002135/1).

Organisation: University of Reading

Rights-holders: University of Reading

Source: The synthetic generator used to generate the music clips was presented in Williams et al., “Affective Calibration of Musical Feature Sets in an Emotionally Intelligent Music Composition System”, ACM Trans. Appl. Percept. 14, 3, Article 17 (May 2017), 13 pages. DOI: https://doi.org/10.1145/3059005

3. TERMS OF USE
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Copyright University of Reading, 2018. This dataset is licensed by the rights-holder(s) under a Creative Commons Attribution 4.0 International Licence: https://creativecommons.org/licenses/by/4.0/.

4. CONTENTS
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The dataset comprises of data from 19 subjects. The sampling rate is 1 kHz and the music listening task corresponding to a music clip is 40 s long (clip duration). The 40-s music clip is generated in real-time by the music generator, based on the target emotional state (defined by LOW/NEUTRAL/HIGH valence and LOW/NEUTRAL/HIGH arousal).

5. METHOD and PROCESSING
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This information is available in the following publications:

[1] Daly, I., Nicolaou, N., Williams, D., Hwang, F., Kirke, A., Miranda, E., Nasuto, S.J., �Neural and physiological data from participants listening to affective music�, Scientific Data, 2018.
[2] Daly, I., Williams, D., Hwang, F., Kirke, A., Malik, A., Roesch, E., Weaver, J., Miranda, E. R., Nasuto, S. J., �Identifying music-induced emotions from EEG for use in brain-computer music interfacing�, in Proc. 4th Workshop on Affective Brain-Computer Interfaces at the 6th International Conference on Affective Computing and Intelligent Interaction (ACII2015). Xi�an, China, 21-25 September 2015.
If you use this dataset in your study please cite these references, as well as the following reference:
[3] Williams, D., Kirke, A., Miranda, E.R., Daly, I., Hwang, F., Weaver, J., Nasuto, S.J., �Affective Calibration of Musical Feature Sets in an Emotionally Intelligent Music Composition System�, ACM Trans. Appl. Percept. 14, 3, Article 17 (May 2017), 13 pages. DOI: https://doi.org/10.1145/3059005

Thank you for your interest in our work.