Emotions Recognition Using EEG Signals: A Survey

TLDR

Emotions influence daily life and decision‑making, and recent research has turned to neurophysiological measures—particularly EEG, which offers a simple, inexpensive, portable means—to identify emotional states. This survey reviews neurophysiological studies of EEG‑based emotion recognition from 2009 to 2016, aiming to provide a structured starting point for researchers entering the field. The authors analyze key components of the recognition pipeline—subject selection, feature extraction, and classifier choice—and compare studies across these dimensions. They conclude with a set of best‑practice recommendations to ensure reproducible, replicable, well‑validated, and high‑quality EEG emotion‑recognition research.

Abstract

Emotions have an important role in daily life, not only in human interaction, but also in decision-making processes, and in the perception of the world around us. Due to the recent interest shown by the research community in establishing emotional interactions between humans and computers, the identification of the emotional state of the former became a need. This can be achieved through multiple measures, such as subjective self-reports, autonomic and neurophysiological measurements. In the last years, Electroencephalography (EEG) received considerable attention from researchers, since it can provide a simple, cheap, portable, and ease-to-use solution for identifying emotions. In this paper, we present a survey of the neurophysiological research performed from 2009 to 2016, providing a comprehensive overview of the existing works in emotion recognition using EEG signals. We focus our analysis in the main aspects involved in the recognition process (e.g., subjects, features extracted, classifiers), and compare the works per them. From this analysis, we propose a set of good practice recommendations that researchers must follow to achieve reproducible, replicable, well-validated and high-quality results. We intend this survey to be useful for the research community working on emotion recognition through EEG signals, and in particular for those entering this field of research, since it offers a structured starting point.