Keywords
AI-artificial intelligence
CleveLabs
Human-Computer Interaction (HCI) modules
How to Cite
Abstract
This paper investigates the use of artificial intelligence for emotion recognition from EEG signals collected with a low‑cost headset in ecologically valid conditions. We adopt a design with substantial variability of EEG data, which is a large, multisubject dataset. By concentrating on one individual, we aim to build a highly accurate, personalized AI model that predicts that person’s emotional states.
Overall, this paper demonstrates that precise, reliable, and personalized emotion recognition is achievable even with a lightweight headset and real‑world protocols. This focused approach provides a practical springboard for scaling to larger populations and integrating emotion detection into diverse human-computer interaction (HCI) modules.
References
Britton, Jeffrey W, and Frey, Lauren C, and Hopp, Jennifer L, and Korb, Pearce, and Koubeissi, Mohamad Z., and Lievens, William E, and Pestana-Knight, Elia M, and St Louis, Erk. Electroencephalography (EEG): An introductory text and atlas of normal and abnormal findings in adults, children, and infants (2016).
M. Teplan. Fundamentals of EEG measurement (2002).
Dzedzickis, Andrius and Kaklauskas, Artras and Bucinskas, Vytautas. Human emotion recognition: Review of sensors and methods (2020).
G. Gigilashvili, I. Gotsiridze, Z. Mgaloblishvili, Z. Ghurtskaia, M. Tsiklauri. Medical Engineering Laboratory System (2012). https://gtu.ge/book/clevemed_lab.pdf
N.Kukhilava, T.Tsmindashvili, R.Kalandadze, A.Gupta, S.Katamadze, F. Brémont, L.M. Ferrari, P. M ¨uller, and B. E. Wirth. Evaluation in EEG Emotion Recognition: State-of-the-Art Review and Unified Framework (2025).https://github.com/EmotionLab/EEGain
Gramfort, Alexandre, and Luessi, Martin, and Larson, Eric, and Engemann, Denis A, and Strohmeier, Daniel, and Brodbeck, Christian, and Parkkonen, Lauri, and Hinen, Matti. MNE software for processing MEG and EEG data (2014).
Ji, Shuiwang and Xu, Wei and Yang, Ming and Yu, Kai. 3D convolutional neural networks for human action recognition (2012).
Ding, Yi, and Robinson, Neethu, and Zhang, Su, and Zeng, Qiuhao, and Guan, Cuntai. TSception: Capturing Temporal Dynamics and Spatial Asymmetry From EEG for Emotion Recognition (2022).
Lawhern, Vernon J, and Solon, Amelia J, and Waytowich, Nicholas R., and Gordon, Stephen M, and Hung, Chou P., and Lance, Brent J. EEGNet: a compact convolutional neural network for EEG-based brain–computer interfaces (2018).