University of Hertfordshire

A method for classifying mental tasks in the space of EEG transforms

Research output: Contribution to conferencePaperpeer-review

Standard

A method for classifying mental tasks in the space of EEG transforms. / Cordeiro De Amorim, Renato; Mirkin, Boris; Q. Gan, John.

2009. Paper presented at UK Workshop on Computational Intelligence, Nottingham, United Kingdom.

Research output: Contribution to conferencePaperpeer-review

Harvard

Cordeiro De Amorim, R, Mirkin, B & Q. Gan, J 2009, 'A method for classifying mental tasks in the space of EEG transforms', Paper presented at UK Workshop on Computational Intelligence, Nottingham, United Kingdom, 7/09/09 - 9/09/09.

APA

Cordeiro De Amorim, R., Mirkin, B., & Q. Gan, J. (2009). A method for classifying mental tasks in the space of EEG transforms. Paper presented at UK Workshop on Computational Intelligence, Nottingham, United Kingdom.

Vancouver

Cordeiro De Amorim R, Mirkin B, Q. Gan J. A method for classifying mental tasks in the space of EEG transforms. 2009. Paper presented at UK Workshop on Computational Intelligence, Nottingham, United Kingdom.

Author

Cordeiro De Amorim, Renato ; Mirkin, Boris ; Q. Gan, John. / A method for classifying mental tasks in the space of EEG transforms. Paper presented at UK Workshop on Computational Intelligence, Nottingham, United Kingdom.

Bibtex

@conference{617a12789a60483e912c406f9ddc29fd,
title = "A method for classifying mental tasks in the space of EEG transforms",
abstract = "In this article we describe a new method for supervised classification of EEG signals. This method applies to the power spectrum density data and assignsclass-dependent information weights to individual pixels, so that the decision is defined by the summary weights of the most informative pixel features. Weexperimentally analyze several versions of the approach. The informative features appear to be rather similar among different individuals, thus supporting the view that there are subject independent general brain patterns for the same mental task",
author = "{Cordeiro De Amorim}, Renato and Boris Mirkin and {Q. Gan}, John",
note = "Renato Cordeiro De Amorim, Boris Mirkin, John Q. Gan, {\textquoteleft}A method for classifying mental tasks in the space of EEG transforms{\textquoteright}, paper presented at the UK Workshop on Computational Intelligence, Nottingham, UK, 7-9 September, 2009. ; UK Workshop on Computational Intelligence ; Conference date: 07-09-2009 Through 09-09-2009",
year = "2009",
language = "English",

}

RIS

TY - CONF

T1 - A method for classifying mental tasks in the space of EEG transforms

AU - Cordeiro De Amorim, Renato

AU - Mirkin, Boris

AU - Q. Gan, John

N1 - Renato Cordeiro De Amorim, Boris Mirkin, John Q. Gan, ‘A method for classifying mental tasks in the space of EEG transforms’, paper presented at the UK Workshop on Computational Intelligence, Nottingham, UK, 7-9 September, 2009.

PY - 2009

Y1 - 2009

N2 - In this article we describe a new method for supervised classification of EEG signals. This method applies to the power spectrum density data and assignsclass-dependent information weights to individual pixels, so that the decision is defined by the summary weights of the most informative pixel features. Weexperimentally analyze several versions of the approach. The informative features appear to be rather similar among different individuals, thus supporting the view that there are subject independent general brain patterns for the same mental task

AB - In this article we describe a new method for supervised classification of EEG signals. This method applies to the power spectrum density data and assignsclass-dependent information weights to individual pixels, so that the decision is defined by the summary weights of the most informative pixel features. Weexperimentally analyze several versions of the approach. The informative features appear to be rather similar among different individuals, thus supporting the view that there are subject independent general brain patterns for the same mental task

M3 - Paper

T2 - UK Workshop on Computational Intelligence

Y2 - 7 September 2009 through 9 September 2009

ER -