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About

Daniel Planelles is a researcher specializing in Brain-Computer Interface (BCI) and Brain-Machine Interface (BMI) systems, with a particular focus on leveraging electroencephalographic (EEG) signals to restore and enhance mobility for individuals with motor disabilities. His work sits at the intersection of neural signal processing, machine learning, and rehabilitation engineering, exploring how non-invasive brain activity measurements can be translated into meaningful control signals for assistive technologies. Planelles has made significant contributions to the field through his development of Support Vector Machine (SVM)-based classification frameworks capable of distinguishing multiple mental tasks from EEG recordings. His most influential work, "SVM-based Brain–Machine Interface for controlling a robot arm through four mental tasks" (2014), has accumulated 124 citations and demonstrated a practical pathway for robotic arm control using thought alone. Complementing this, his research on detecting arm movement intention prior to physical execution — accumulating 57 citations — represents a notable advance in predictive neural decoding using non-invasive methods. His investigations into both online classification systems and passive robot-assisted EEG decoding further underscore his commitment to translating laboratory findings into viable rehabilitation applications, making his body of work highly relevant for researchers advancing neuroprosthetics and assistive robotics.