@inproceedings{ac9e27a68e924c228e392065ce54b470,
title = "Classification of basic human emotions from electroencephalography data",
abstract = "This paper explores the combination of known signal processing techniques to analyze electroencephalography (EEG) data for the classification of a set of basic human emotions. An Emotiv EPOC headset with 16 electrodes was used to measure EEG data from a population of 24 subjects who were presented an audiovisual stimuli designed to evoke 4 emotions (rage, fear, fun and neutral). Raw data was preprocessed to eliminate noise, interference and physiologic artifacts. Discrete Wavelet Transform (DWT) was used to extract its main characteristics and define relevant features. Classification was performed using different algorithms and results compared. The best results were obtained when using meta-learning techniques with classification errors at 5 \%. Final conclusions and future work are discussed.",
keywords = "Discrete wavelet transform, Electroencephalography, Human emotion classification",
author = "Ximena Fer{\'n}andez and Rosana Garc{\'i}a and Enrique Ferreira and Juan Men{\'e}ndez",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2015.; 20th Iberoamerican Congress on on Pattern Recognition, CIARP 2015 ; Conference date: 09-11-2015 Through 12-11-2015",
year = "2015",
doi = "10.1007/978-3-319-25751-8\_14",
language = "Ingl{\'e}s",
isbn = "9783319257501",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "108--115",
editor = "Alvaro Pardo and Josef Kittler",
booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
}