Evaluation of Classification Methods for EEG Signals in Older Adults for Right-Hand Motor Imagery Movements
Evaluación de Métodos de Clasificación de Señales EEG de Adultos Mayores para Movimientos Imaginados de la Mano Derecha
DOI:
https://doi.org/10.17488/RMIB.47.2.1564Keywords:
Older adults, BCI, classification, EEG, motor imageryAbstract
Cognitive decline, characterized by the progressive loss of functions such as memory, attention, and speech, significantly affects the well-being of older adults, a population that is growing rapidly worldwide. In response to this issue, brain-computer interfaces (BCIs) represent a promising technological alternative to facilitate interaction with digital devices, particularly for individuals experiencing reduced motor abilities. This study proposes a methodology for classifying five imagined right-hand movements in older adults using EEG signals. Exhaustive experimentation was conducted, evaluating eight distinct representational features extracted from the EEG data and applying various machine learning algorithms, including ensemble methods, to develop a computational model that achieved an accuracy of 93.7%. Additionally, subsets of features capable of maintaining classification accuracy above 90% were identified. These findings support the feasibility of integrating BCI solutions tailored to the needs of older adults in assistive and rehabilitation applications.
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