Use of ANN and Complexity Measures in Cognitive EEG Discrimination

Author

Fan, Fei-yan ; Li, Ying-jie ; Qiu, Yi-hong ; Zhu, Yi-sheng

Author_Institution

Sch. of Commun. & Inf. Eng., Shanghai Univ.

fYear

2006

Firstpage

4638

Lastpage

4641

Abstract

The purpose of this paper is to apply BP ANN to the discrimination of three kinds of subjects (clinical diagnosed 62 schizophrenic patients, 48 depressive patients and 26 normal controls) respectively in resting state with eyes closed and three cognitive tasks, with EEG complexity measures used as feature vectors. EEG activity is recorded from 16 scalp electrodes and recordings are digitized for off-line processing. Features vectors based on Lep-Ziv complexity and classification with ANN are implemented in Matlab6.5. The comparison between the results of classifying in four states is illustrated and discussed. The classification accuracies achieved are 60% and over. The results show that ANN is an effective approach for discrimination of these three kinds of objects both in baseline and some cognitive states

Keywords

backpropagation; biomedical electrodes; cognition; diseases; electroencephalography; mathematics computing; medical signal processing; neural nets; signal classification; BP ANN; Lep-Ziv complexity; Matlab6.5; cognitive EEG discrimination; complexity measures; depressive patients; feature vectors; off-line processing; scalp electrodes; schizophrenic patients; signal classification; Aging; Artificial neural networks; Biomedical engineering; Biomedical measurements; Diseases; Drugs; Electroencephalography; Eyes; Scalp; Sequences;

fLanguage

English

Publisher

ieee

Conference_Titel

Engineering in Medicine and Biology Society, 2005. IEEE-EMBS 2005. 27th Annual International Conference of the

Conference_Location

Shanghai

Print_ISBN

0-7803-8741-4

Type

conf

DOI

10.1109/IEMBS.2005.1615504

Filename

1615504