A Heterogeneous Label Space Transfer Learning Method for Brain-Computer Interface Calibration

Abstract

The invention discloses a heterogeneous label space migration learning method for brain-computer interface calibration, which relates to the field of brain-computer interface, including: labeling and grouping a new user's EEG signal sample set, and calculating the The average covariance matrix; group all samples of the EEG signal sample set of the auxiliary user according to the label category, and calculate the average covariance matrix in each group; according to the average covariance matrix that satisfies the set corresponding relationship, the auxiliary user's The sample is transformed, and the label of the new user is assigned to the auxiliary user sample according to the corresponding relationship, and the transformed auxiliary user data is obtained; the transformed auxiliary user data and the marked new user sample are combined as a training set, and the Build a machine learning model on the training set. The method of the present invention can improve the model learning ability of new users by means of the auxiliary user data in heterogeneous label space, greatly reduce the calibration data required by new users, thereby effectively reducing the calibration time.

Description

technical field [0001] The invention belongs to the field of brain-computer interface, and more specifically, relates to a method for learning transfer of heterogeneous label spaces for brain-computer interface calibration. Background technique [0002] Brain-computer interface is a system that provides a direct interaction channel between the brain and external devices (such as computers, robots, etc.), and is regarded as the ultimate form of human-computer interaction. It not only allows users to directly control the movement of external devices through brain signals, such as mechanical exoskeletons or drones; it can also be used to judge the current state of the brain, such as sleep status, fatigue and so on. The input signals of the brain-computer interface system include EEG, magnetoencephalogram, functional MRI, etc., among which EEG is the most common. EEG is usually collected from the surface of the scalp by a head-mounted EEG cap through electrodes, and the dischar...

AI Technical Summary

Problems solved by technology

[0005] In view of the above defects or improvement needs of the prior art, the present invention provides a heterogeneous label space transfer learning method for brain-computer interface calibration. When the user's label space is different, the auxiliary user data cannot be used, and only a large amount of new user data can be collected for calibration, resulting in a technical problem that the calibration time is greatly prolonged

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