METHOD AND APPARATUS FOR OPERATING TERMINAL BY USING BRAIN WAVES

• US 20080154148A1
• Filed: 12/20/2007
• Published: 06/26/2008
• Est. Priority Date: 12/20/2006
• Status: Abandoned Application

• Current Assignee: Samsung Electronics Co. Ltd.
• Original Assignee: Samsung Electronics Co. Ltd.
• Inventors: Chung, Beom-Su, Jeon, Seong-Joon
• Application Number:
  US11/961,207
• Publication Number:
  US 20080154148A1
• Time in Patent Office: Days
• US Class Current: 600/544
• CPC Class Codes: G06F 3/015 Input arrangements based on nervous system activity detection, e.g. brain waves [EEG] detection, electromyograms [EMG] detection, electrodermal response detection

Abstract

Disclosed are a method and an apparatus for operating a terminal by using brain waves. The method includes receiving a current brain wave of a terminal user; finding the existence of data matched to the received current brain wave among previously constructed database having reduced data sizes of brain waves of the terminal user and corresponding to at least one codeword; and operating the terminal according to a codeword corresponding to the matched brain wave data when the matched data is found. The apparatus includes a memory for storing previously constructed database having reduced data sizes of brain waves of a terminal user and corresponding to at least one codeword; a data processing unit for processing a current brain wave of the terminal user; a data search unit for finding data matched to the processed current brain wave; and a control unit for operating the terminal according to a codeword corresponding to the matched data.

Claims

1. A method for operating a terminal, the method comprising the steps of:

receiving a current brain wave of a user of the terminal;

finding an existence of data matched to the received current brain wave among a previously constructed database having reduced data sizes of brain waves of the user of the terminal and corresponding to at least one codeword; and

operating the terminal according to a codeword corresponding to the brain wave matched data when the matched data is found.

2. The method as claimed in claim 1, wherein constructing the database having reduced data sizes comprises:

computing a first data matrix on the brain waves of the user of the terminal;

computing a first covariance matrix on the first data matrix;

performing a Discrete Karhunen-Lo'"'"'eve Transform (DKLT) on the first data matrix by using the first covariance matrix;

determining the number of separation values of the DKLT transformed first data matrix by a prescribed number;

extracting respective elements corresponding to the determined separation values among the first covariance matrices and determining a second covariance matrix;

performing the DKLT on the first data matrix by using the determined second covariance matrix and determining a second data matrix; and

configuring the database by using the second data matrix.

3. The method as claimed in claim 2, wherein the separation value is determined by:

$P_{m;<br><br>a/b}^r<br><br>\left(k\right)=\frac{{\left[{\stackrel{\_}{\tilde{z}}}_{m/a}^r<br><br>\left(k\right)-{\stackrel{\_}{\tilde{z}}}_{m/b}^r<br><br>\left(k\right)\right]}^2}{\underset{i=1}{\overset{I}{\sum <br><br>}}<br><br><br><br>{\left[{\tilde{z}}_{m/a;<br><br>i}^r<br><br>\left(k\right)-{\stackrel{\_}{\tilde{z}}}_{m/a}^r<br><br>\left(k\right)\right]}^2+\underset{i=1}{\overset{I}{\sum <br><br>}}<br><br><br><br>{\left[{\tilde{z}}_{m/b;<br><br>i}^r<br><br>\left(k\right)-{\stackrel{\_}{\tilde{z}}}_{m/b}^r<br><br>\left(k\right)\right]}^2},$

where ‘

a’ and

‘

b’

represent brain wave data different from each other, ‘

m’

represents a serial number corresponding to a brain wave detection part, and ‘

r’

represents the number of times brain wave data is detected.

4. The method as claimed in claim 1, wherein receiving a current brain wave comprises:

computing a first data matrix on the received current brain wave;

computing a first covariance matrix on the first data matrix;

performing a DKLT on the first data matrix by using the first covariance matrix;

determining the number of separation values of the DKLT transformed first data matrix by a prescribed number;

extracting respective elements corresponding to the determined separation values among the first covariance matrices and determining a second covariance matrix; and

performing the DKLT on the first data matrix by using the determined second covariance matrix and determining a second data matrix.

5. The method as claimed in claim 4, wherein the separation value is determined by:

$P_{m;<br><br>a/b}^r<br><br>\left(k\right)=\frac{{\left[{\stackrel{\_}{\tilde{z}}}_{m/a}^r<br><br>\left(k\right)-{\stackrel{\_}{\tilde{z}}}_{m/b}^r<br><br>\left(k\right)\right]}^2}{\underset{i=1}{\overset{I}{\sum <br><br>}}<br><br><br><br>{\left[{\tilde{z}}_{m/a;<br><br>i}^r<br><br>\left(k\right)-{\stackrel{\_}{\tilde{z}}}_{m/a}^r<br><br>\left(k\right)\right]}^2+\underset{i=1}{\overset{I}{\sum <br><br>}}<br><br><br><br>{\left[{\tilde{z}}_{m/b;<br><br>i}^r<br><br>\left(k\right)-{\stackrel{\_}{\tilde{z}}}_{m/b}^r<br><br>\left(k\right)\right]}^2},$

where ‘

a’ and

‘

b’

represent brain wave data different from each other, ‘

m’

represents a serial number corresponding to a brain wave detection part, and ‘

r’

represents the number of times brain wave data is detected.

6. An apparatus for operating a terminal, the apparatus comprising:

a memory for storing a previously constructed database having reduced data sizes of brain waves of a user of the terminal and corresponding to at least one codeword;

a data processing unit for processing a current brain wave of the user of the terminal;

a data search unit for finding data matched to the processed current brain wave among the database; and

a control unit for operating the terminal according to a codeword corresponding to the brain wave matched data when the matched data is found.

7. The apparatus as claimed in claim 6, wherein the database stored in the memory is constructed by:

computing a first data matrix on the brain waves of the user of the terminal;

computing a first covariance matrix on the first data matrix;

performing a Discrete Karhunen-Lo'"'"'eve Transform (DKLT) on the first data matrix by using the first covariance matrix;

determining the number of separation values of the DKLT transformed first data matrix by a prescribed number;

extracting respective elements corresponding to the determined separation values among the first covariance matrices and determining a second covariance matrix;

performing the DKLT on the first data matrix by using the determined second covariance matrix and determining a second data matrix; and

configuring the database by using the second data matrix.

8. The apparatus as claimed in claim 7, wherein the separation value is determined by:

$P_{m;<br><br>a/b}^r<br><br>\left(k\right)=\frac{{\left[{\stackrel{\_}{\tilde{z}}}_{m/a}^r<br><br>\left(k\right)-{\stackrel{\_}{\tilde{z}}}_{m/b}^r<br><br>\left(k\right)\right]}^2}{\underset{i=1}{\overset{I}{\sum <br><br>}}<br><br><br><br>{\left[{\tilde{z}}_{m/a;<br><br>i}^r<br><br>\left(k\right)-{\stackrel{\_}{\tilde{z}}}_{m/a}^r<br><br>\left(k\right)\right]}^2+\underset{i=1}{\overset{I}{\sum <br><br>}}<br><br><br><br>{\left[{\tilde{z}}_{m/b;<br><br>i}^r<br><br>\left(k\right)-{\stackrel{\_}{\tilde{z}}}_{m/b}^r<br><br>\left(k\right)\right]}^2},$

where ‘

a’ and

‘

b’

represent brain wave data different from each other, ‘

m’

represents a serial number corresponding to a brain wave detection part, and ‘

r’

represents the number of times brain wave data is detected.

9. The apparatus as claimed in claim 6, wherein the data processing unit carries out:

computing a first data matrix on the current brain wave of the user of the terminal;

computing a first covariance matrix on the first data matrix;

performing a DKLT on the first data matrix by using the first covariance matrix;

determining the number of separation values of the DKLT transformed first data matrix by a prescribed number;

extracting respective elements corresponding to the determined separation values among the first covariance matrices and determining a second covariance matrix;

performing the DKLT on the first data matrix by using the determined second covariance matrix and determining a second data matrix; and

reducing a size of the current brain wave by using the second data matrix.

10. The apparatus as claimed in claim 9, wherein the separation value is determined by:

$P_{m;<br><br>a/b}^r<br><br>\left(k\right)=\frac{{\left[{\stackrel{\_}{\tilde{z}}}_{m/a}^r<br><br>\left(k\right)-{\stackrel{\_}{\tilde{z}}}_{m/b}^r<br><br>\left(k\right)\right]}^2}{\underset{i=1}{\overset{I}{\sum <br><br>}}<br><br><br><br>{\left[{\tilde{z}}_{m/a;<br><br>i}^r<br><br>\left(k\right)-{\stackrel{\_}{\tilde{z}}}_{m/a}^r<br><br>\left(k\right)\right]}^2+\underset{i=1}{\overset{I}{\sum <br><br>}}<br><br><br><br>{\left[{\tilde{z}}_{m/b;<br><br>i}^r<br><br>\left(k\right)-{\stackrel{\_}{\tilde{z}}}_{m/b}^r<br><br>\left(k\right)\right]}^2},$

where ‘

a’ and

‘

b’

represent brain wave data different from each other, ‘

m’

represents a serial number corresponding to a brain wave detection part, and ‘

r’

represents the number of times brain wave data is detected.