Analysis of EEG signals of blinking patterns in order to control a wheelchair for people with ALS
Alexsandra J.Cordero, Maria C.Orihuela, Fabricio Nava, Leonardo S.Sandoval, Alonso B.Castañeda y Maria A.Rejas
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Abstract
Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease with no cure, emphasizing the need for innovative approaches to improve patients' quality of life. Brain-computer interfaces (BCIs) offer promising solutions using EEG signals. In this study two methodologies are proposed: analyzing signals of open and closed eyes and short and long blinks. By collecting EEG data, applying appropriate filters, and extracting relevant features such as power spectral density (PSD) and power spectral entropy (PSE), these BCIs allow for the classification of different eye states and types of blinking. Statistical analysis confirms significant differences between these conditions. These BCIs provide a practical and efficient means of wheelchair control, encouraging greater mobility and independence for ALS patients. Although more research is required to refine these approaches, they have great potential to improve the general well-being and quality of life of people living with ALS.