Brain-computer interface transfer learning method based on manifold embedding distribution alignment

A transfer learning and brain-computer interface technology, applied in the field of brain-computer interface transfer learning based on manifold embedding distribution alignment, which can solve problems such as unsatisfactory results.

Active Publication Date: 2020-09-29
GUANGZHOU GUANGDA INNOVATION TECH CO LTD
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  • Abstract
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  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, the transfer learning technology currently applied to the brain-co

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  • Brain-computer interface transfer learning method based on manifold embedding distribution alignment
  • Brain-computer interface transfer learning method based on manifold embedding distribution alignment
  • Brain-computer interface transfer learning method based on manifold embedding distribution alignment

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Embodiment Construction

[0068] The present invention will be further described in detail below in conjunction with the embodiments and the accompanying drawings, but the embodiments of the present invention are not limited thereto.

[0069] Such as figure 1 As shown, a brain-computer interface migration learning method based on manifold embedding distribution alignment of the present invention comprises the following steps:

[0070] S1. Obtain the EEG data of the source subjects separately D s and the EEG data of the target subject D t ;

[0071] S2. Preprocessing and feature extraction are performed on the EEG data;

[0072] S3. Construct a migration learning model based on manifold embedding distribution alignment, use the data to train the migration learning model, and solve the model parameters in the model to obtain a trained classifier;

[0073] S4. Using a classifier to classify the unlabeled EEG data of the target subject.

[0074] In step S1, the EEG data D of the source subject s Cont...

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Abstract

The invention discloses a brain-computer interface transfer learning method based on manifold embedding distribution alignment. The brain-computer interface transfer learning method comprises the following steps of acquiring EEG data of a source subject and EEG data of a target subject respectively; carrying out preprocessing and feature extraction on the EEG data; constructing a transfer learningmodel based on manifold embedding distribution alignment, and training the transfer learning model by using data to obtain a training model; and utilizing the classifier obtained by training to classify the label-free EEG data of the target subject. On the basis of Riemannian tangent plane mapping and manifold feature transformation, feature distribution alignment is integrated into classifier training, and an effective classifier is obtained through training. According to the method, the performance of the brain-computer interface system used by the target user can be effectively improved, and the training burden of the user is reduced.

Description

technical field [0001] The invention relates to the field of image super-resolution research in video surveillance, in particular to a brain-computer interface migration learning method based on manifold embedding distribution alignment. Background technique [0002] Brain Computer Interface (BCI, Brain Computer Interface) is to establish an external information exchange and control pathway between the human brain and the external environment through computers or other electronic devices that does not depend on peripheral nerves and muscle tissue. It collects EEG signals, converts them into control commands and transmits them to external devices through signal processing, so as to realize the external control of the human brain. Formed in the 1970s, this technology is a crossover technology involving neurology, medicine, signal detection, signal processing, pattern recognition and other fields. Brain-computer interfaces are currently mainly used in the field of medical reha...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06K9/62G06K9/46G06K9/54
CPCG06V10/40G06V10/20G06V2201/03G06F2218/08G06F2218/12G06F18/214G06F18/241
Inventor 杨飞宇顾正晖俞祝良
Owner GUANGZHOU GUANGDA INNOVATION TECH CO LTD
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