A user-independent brain-computer interface device according to the present invention comprises: a memory for storing a program for classifying tasks from brain signals of a user using a task classification learning model of brain signals, which is built based on metric learning; and a processor for performing the program. The task classification learning model of brain signals is configured to: create user label data by adding identification numbers for each user to a task label indicating a motion imaginary class to be classified by paradigm for brain signal acquisition; build an artificial neural network based on anchor data randomly extracted from the user label data, positive data consisting of data having the same user label as the anchor data, and negative data composed of data having a user label different from the anchor data, and perform metric learning using a similarity loss function that reduces a distance between a feature vector of the anchor data and a feature vector of the positive data and increases a distance between the feature vector of the anchor data and a feature vector of the negative data in a process of constructing each artificial neural network; and build a classification network using the weights of the artificial neural network. Accordingly, it is possible to provide a user-independent brain-computer interface that can be used independently by each user, and thus, user convenience can be improved.
- BRAIN-COMPUTER INTERFACE APPARATUS BASED ON FEATURE EXTRACTION REFLECTING SIMILARITY BETWEEN USERS USING DISTANCE LEARNING AND TASK CLASSIFICATION METHOD USING THE SAME