Supervised machine learning algorithms for brain signal classification

Abstract

Introduction/purpose: The brain wave application is widespread in recent years, especially in the applications that aid the impaired people suffered from amputation or paralysis. The objective of this research is to assess how well different supervised machine learning algorithms classify brain signals, with an emphasis on improving the precision and effectiveness of brain-computer interface applications. Method: In this work, brain signal data was analyzed using a number of well-known supervised learning models, such as Support Vector Machines (SVM) and Neural Networks (NN). The data set was taken from a previous study. Twenty five participants imagined moving their right arm (elbow and wrist) while the brain signals were recorded during that process. The dataset was prepared for the analysis by the application of meticulous preprocessing and feature extraction procedures. Then the resulting data were subjected to classification. Results: The study highlights how crucial feature selection and model modification are for maximizing classification results. Supervised machine learning methods have great potential for classifying brain signals, particularly SVM and NN. Conclusion: The use of SVM and NN has the potential to completely transform the creation of cutting-edge brain-computer interfaces. The integration of these models with real-time data should be investigated in future studies.

Keywords

• supervised machine learning
• eeg
• brain signals
• classifications
• feature extraction