On-line recognition of emotions via electroencephalography

• Published in: Revista IEEE América Latina Vol. 20; no. 5; pp. 806 - 812
• Main Authors: Altamirano Asher Weiss, Kevin, Concatto, Fernando, Celeste Ghizoni Teive, Raimundo, Garcia Ramirez, Alejandro Rafael
• Format: Journal Article
• Language: English
• Published: Los Alamitos IEEE 01.05.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Algorithms; Brain modeling; Brain-Computer Interface; Data analysis; Data collection; Electroencephalography; Emotion recognition; Emotions; Face recognition; Filtering; Human-computer interface; Machine learning; On-line Processing; Pattern recognition; Real-time systems; Signal processing; Support vector machines
• ISSN: 1548-0992; 1548-0992
• DOI: 10.1109/TLA.2022.9693565

Automated pattern recognition of brain signals can bring about new experiences, enhancing applications in a wide array of areas. One of its fields of study is the recognition of emotions via electroencephalography (EEG), which shows exclusive advantages compared to other methods. However, research with brain-computer interfaces (BCI) is usually structured in two sequential stages: data collection and data analysis. These stages leave a gap in the perspective of a functional system in a production environment since the practitioner needs to wait a considerable length of time until they can see the results of the current activity. An on-line classification system of emotions (positive, neutral, and negative) was developed using open resources in this work. Five machine learning models were trained with the SEED IV dataset, which is labeled with different emotions. The models were trained and tested using nested cross-validation and grid search to obtain the best hyperparameters. The algorithm implementation in Python was integrated with the OpenBCI software to capture the EEG signals, process them, and command the simulations. The best average accuracy obtained for a single subject was 76.19%, and the average accuracy for all subjects was 57.07%. The average execution time for signal processing and prediction, combined, was around one millisecond, which demonstrates the potential for applications with real-time characteristics.