Brain-computer interface apparatus for minimizing signal correction process between users using clustering technology based on brain activity and method of operating the same

Patent metadata

Field	Value
Publication number	US-2021386351-A1
Application number	US-202117155281-A
Country	US
Kind code	A1
Filing date	Jan 22, 2021
Priority date	Jun 12, 2020
Publication date	Dec 16, 2021
Grant date	—

Abstract

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.

First claim

What is claimed is:

1 . A brain-computer interface apparatus capable of minimizing a signal correction process between users using clustering technology based on brain activity, the brain-computer interface apparatus comprising:
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.

2 . The brain-computer interface apparatus according to claim 1 , further comprising a brain wave measurement device for, using a plurality of measurement electrodes attached to a plurality of regions of the learning subjects or the measurement subject, measuring a plurality of brain signals for each of the regions,
wherein the brain signals exhibit different frequency powers according to the regions.

3 . The brain-computer interface apparatus according to claim 2 , wherein the brain wave measurement device measures brain signals having frequency powers of a first band at which a frequency power of 8 Hz to 12 Hz is measured, a second band at which a frequency power of 12 Hz to 18 Hz is measured, and a third band at which a frequency power of 18 Hz to 30 Hz is measured according to positions of the regions.

4 . The brain-computer interface apparatus according to claim 3 , wherein the feature extractor extracts at least one clustering feature of first clustering features corresponding to the first band, second clustering features corresponding to the second band, and third clustering features corresponding to the third band.

5 . The brain-computer interface apparatus according to claim 1 , wherein the feature extractor determines a plurality of clustering features for each band by averaging power spectral density (PSD) of the frequency powers for each band, and determines two clustering features among the determined clustering features through principal component analysis (PCA).

6 . The brain-computer interface apparatus according to claim 5 , wherein the clustering model generator generates the clustering models using the determined two clustering features and cluster analysis (k-means clustering).

7 . The brain-computer interface apparatus according to claim 1 , wherein the brain wave processor determines an intention of the measurement subject using an intention determination model corresponding to the determined clustering model, and thus reduces the signal correction process by reducing a machine learning process for the newly measured brain signal of the measurement subject.

8 . The brain-computer interface apparatus according to claim 1 , wherein the clustering model generator generates the clustering models so that inter-subject variability (ISV) of each of the clustering models is determined to be smaller than inter-subject variability (ISV) of all of the learning subjects.

9 . A method of operating a brain-computer interface apparatus capable of minimizing a signal correction process between users clustering technology based on using brain activity, the method comprising:
extracting, in a feature extractor, a plurality of clustering features using frequency powers for each band of brain signals measured from a subjects; generating, in a clustering model generator, a plurality of clustering models based on the extracted clustering features; and 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, in a brain wave processor.

10 . The method according to claim 9 , further comprising measuring, in a brain wave measurement device, a plurality of brain signals for each of a plurality of regions of the learning subjects or the measurement subject by using a plurality of measurement electrodes attached to the regions,
wherein the brain signals exhibit different frequency powers according to the regions.

11 . The method according to claim 10 , wherein the measuring of a plurality of brain signals for each of the regions comprises measuring brain signals having frequency powers of a first band at which a frequency power of 8 Hz to 12 Hz is measured, a second band at which a frequency power of 12 Hz to 18 Hz is measured, and a third band at which a frequency power of 18 Hz to 30 Hz is measured according to positions of the regions.

12 . The method according to claim 9 , wherein the determining of an intention of the measurement subject using the constructed intention determination model comprises reducing the signal correction process by reducing a machine learning process for the newly measured brain signal of the measurement subject as an intention of the measurement subject is determined using an intention determination model corresponding to the determined clustering model.

Assignees

• Univ Korea Res & Bus Found

Inventors

• Kim Dong Joo
• Lee Se Ho
• Kim Young Tak

Classifications

• A61B5/31 Primary

  for electroencephalography [EEG] · CPC title

• A61B5/374 Primary

  Detecting the frequency distribution of signals, e.g. detecting delta, theta, alpha, beta or gamma waves · CPC title

• G06F18/2135

  based on approximation criteria, e.g. principal component analysis · CPC title

• G06F18/23213

  with fixed number of clusters, e.g. K-means clustering · CPC title

• A61B5/7264

  Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems · CPC title