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.
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Combining Mental States Recognition and Machine Learning for Neurorehabilitation
Semantic Scholar · Computer Science · 2023
Abstract
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.