Combining Mental States Recognition and Machine Learning for Neurorehabilitation
- Resource Type
- Conference
- Authors
- Colafiglio, Tommaso; Sorino, Paolo; Lofu, Domenico; Lombardi, Angela; Narducci, Fedelucio; Di Noia, Tommaso
- Source
- 2023 IEEE International Conference on Systems, Man, and Cybernetics (SMC) Systems, Man, and Cybernetics (SMC), 2023 IEEE International Conference on. :3848-3853 Oct, 2023
- Subject
- Aerospace
Bioengineering
Communication, Networking and Broadcast Technologies
Computing and Processing
General Topics for Engineers
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Transportation
Support vector machines
Prototypes
Music
Machine learning
Electroencephalography
Neurorehabilitation
Biological control systems
Machine Learning
Brain-Computer Interfaces
Music Generation
- Language
- ISSN
- 2577-1655
Brain-computer interfaces are widely used to control machines using Electroencephalography (EEG) signals. Several low-cost electroencephalographs are available on the market that achieves good-quality EEG signals. One of the most intriguing issues for developing biofeedback systems is classifying users' emotional states using EEG signals and Machine Learning (ML) methods. In our study, we propose a novel ML-based biofeedback tool using a BCI to detect two different users' mental states: Focus, and Relaxation. We compared several ML algorithms achieving an average accuracy on the Test Set of 0.90 by using SVM. Finally, we propose a prototype for music generation according to the classification output that could be adopted in neurorehabilitation scenarios.