The present disclosure relates to a technical idea for minimizing a signal correction process between users using brain activity-based clustering technology. More specifically, the present disclosure relates to technology for minimizing a signal correction process between users by clustering a brain signal of a measurement subject into a specific clustering model and determining an intention of the measurement subject using an intention determination model learned on the specific clustering model. The brain-computer interface apparatus according to one embodiment of the present disclosure may include a feature extractor for extracting a plurality of clustering features using frequency powers for each band of brain signals measured from a plurality of learning subjects; a clustering model generator for generating a plurality of clustering models based on the extracted clustering features; and a brain wave processor for constructing an intention determination model by performing machine learning of brain signals for each of the generated clustering models, determining a newly measured brain signal of a measurement subject as any one of the clustering models, and determining an intention of the measurement subject using the constructed intention determination model.
BRAIN-COMPUTER INTERFACE APPARATUS FOR MINIMIZING SIGNAL CORRECTION PROCESS BETWEEN USERS USING CLUSTERING TECHNOLOGY BASED ON BRAIN ACTIVITY AND METHOD OF OPERATING THE SAME