Analyzing recognition of EEG based human attention and emotion using Machine learning. (2022)

• Record Type: Journal Article
• Authors: Shabbir Alam, Mohammad; Zura A. Jalil, Siti; Upreti, Kamal
• Is Part Of: Materials today. Volume 56:Part 6(2022)
• Page Start: 3349
• Page End: 3354
• Publication Date: 2022
• Subjects: Emotion -- Recognition -- Machine Learning -- Electro-Encephalograph (EEG) -- Brain Computer Interface (BCI); Materials science -- Congresses -- Periodicals; 620.1
• DOI: 10.1016/j.matpr.2021.10.190
• Languages: English
• ISSNs: 2214-7853

Abstract

Abstract: An emotionally recognised area of research has already been quite prominent. EEG brain signals have recently been used to recognise an individual's mental condition. Attention often plays a key role in human development, but needs more study. This article offers a noble method of acknowledgment of human attention by sophisticated machine learning algorithms. Scalp-EEG signalling is a cost-effective, single-swinged mechanism dependent on time. Many trials have shown possible support for emotional identification through brain EEG waves. This paper examines and suggests a modern technology for the identification of emotions through the application of new computer learning principles. Ablations experiments also demonstrate the clear and important benefit to the efficiency of our RGNN model from the adjacent matrix and two regularizers. Finally, neuronal researches reveal key brain regions and inter-channel relationships for EEG related emotional awareness.