Classification of chaotic signals using HMM classifiers: EEG-based mental task classification

Soroosh Solhjoo; Ali Motie Nasrabadi; Mohammad Reza Hashemi Golpayegani

Classification of chaotic signals using HMM classifiers: EEG-based mental task classification

Soroosh Solhjoo^*, Ali Motie Nasrabadi, Mohammad Reza Hashemi Golpayegani

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

24 Scopus citations

Abstract

Mental task classification using brain signals, mostly electroencephalogram (EEG), is an approach to understand human brain functions. As EEG seems to be chaotic, it is important to verify the capability of probabilistic and statistical processing tools (such as HMM-based classifiers) in working with chaotic signals. At first, we study the performance of HMM's in classification of different classes of synthetically generated chaotic signals. Then performance of such classifiers in EEG-based mental task classification is studied. Results show good performance in both cases.

Original language	English
Title of host publication	13th European Signal Processing Conference, EUSIPCO 2005
Pages	257-260
Number of pages	4
State	Published - 2005
Externally published	Yes
Event	13th European Signal Processing Conference, EUSIPCO 2005 - Antalya, Turkey Duration: 4 Sep 2005 → 8 Sep 2005

Publication series

Name	13th European Signal Processing Conference, EUSIPCO 2005

Conference

Conference	13th European Signal Processing Conference, EUSIPCO 2005
Country/Territory	Turkey
City	Antalya
Period	4/09/05 → 8/09/05

Keywords

Chaos
EEG-based mental task classification
Hidden markov models (HMM)

Cite this

@inproceedings{a246256cfac44ffdb96ba071707e3f97,

title = "Classification of chaotic signals using HMM classifiers: EEG-based mental task classification",

abstract = "Mental task classification using brain signals, mostly electroencephalogram (EEG), is an approach to understand human brain functions. As EEG seems to be chaotic, it is important to verify the capability of probabilistic and statistical processing tools (such as HMM-based classifiers) in working with chaotic signals. At first, we study the performance of HMM's in classification of different classes of synthetically generated chaotic signals. Then performance of such classifiers in EEG-based mental task classification is studied. Results show good performance in both cases.",

keywords = "Chaos, EEG-based mental task classification, Hidden markov models (HMM)",

author = "Soroosh Solhjoo and Nasrabadi, {Ali Motie} and Golpayegani, {Mohammad Reza Hashemi}",

year = "2005",

language = "English",

isbn = "1604238216",

series = "13th European Signal Processing Conference, EUSIPCO 2005",

pages = "257--260",

booktitle = "13th European Signal Processing Conference, EUSIPCO 2005",

note = "13th European Signal Processing Conference, EUSIPCO 2005 ; Conference date: 04-09-2005 Through 08-09-2005",

}

Classification of chaotic signals using HMM classifiers: EEG-based mental task classification. / Solhjoo, Soroosh; Nasrabadi, Ali Motie; Golpayegani, Mohammad Reza Hashemi.
13th European Signal Processing Conference, EUSIPCO 2005. 2005. p. 257-260 (13th European Signal Processing Conference, EUSIPCO 2005).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Solhjoo, Soroosh

AU - Nasrabadi, Ali Motie

AU - Golpayegani, Mohammad Reza Hashemi

PY - 2005

Y1 - 2005

N2 - Mental task classification using brain signals, mostly electroencephalogram (EEG), is an approach to understand human brain functions. As EEG seems to be chaotic, it is important to verify the capability of probabilistic and statistical processing tools (such as HMM-based classifiers) in working with chaotic signals. At first, we study the performance of HMM's in classification of different classes of synthetically generated chaotic signals. Then performance of such classifiers in EEG-based mental task classification is studied. Results show good performance in both cases.

AB - Mental task classification using brain signals, mostly electroencephalogram (EEG), is an approach to understand human brain functions. As EEG seems to be chaotic, it is important to verify the capability of probabilistic and statistical processing tools (such as HMM-based classifiers) in working with chaotic signals. At first, we study the performance of HMM's in classification of different classes of synthetically generated chaotic signals. Then performance of such classifiers in EEG-based mental task classification is studied. Results show good performance in both cases.

KW - Chaos

KW - EEG-based mental task classification

KW - Hidden markov models (HMM)

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M3 - Conference contribution

AN - SCOPUS:84863668641

SN - 1604238216

SN - 9781604238211

T3 - 13th European Signal Processing Conference, EUSIPCO 2005

SP - 257

EP - 260

BT - 13th European Signal Processing Conference, EUSIPCO 2005

Y2 - 4 September 2005 through 8 September 2005

ER -

Classification of chaotic signals using HMM classifiers: EEG-based mental task classification

Abstract

Publication series

Conference

Keywords

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