Recurrent neural network predictors for EEG signal compression

Author

Bartolini, F. ; Cappellini, V. ; Nerozzi, S. ; Mecocci, A.

Author_Institution

Dipartimento di Ingegneria Elettronica, Firenze Univ., Italy

Volume

5

fYear

1995

fDate

9-12 May 1995

Firstpage

3395

Abstract

The progress of digital electroencephalography gave rise to the problem of EEG data recording. In the paper a DPCM scheme for EEG signal compression is discussed. In particular the performance of a class of predictors based on recurrent neural networks is presented. The training strategy is accurately described and the results of a comparison with some other classical linear and static neural predictors are given. The proposed recurrent neural predictor demonstrates to be competitive with the others in offering good performance at a very low computational cost

Keywords

data compression; differential pulse code modulation; electroencephalography; learning (artificial intelligence); medical signal processing; prediction theory; recurrent neural nets; EEG signal compression; computational cost; data recording; digital electroencephalography; performance; recurrent neural network predictors; training strategy; Computational efficiency; Digital recording; Electroencephalography; Error correction; Feedforward neural networks; Neural networks; Neurofeedback; Recurrent neural networks; Scalp; Vector quantization;

fLanguage

English

Publisher

ieee

Conference_Titel

Acoustics, Speech, and Signal Processing, 1995. ICASSP-95., 1995 International Conference on

Conference_Location

Detroit, MI

ISSN

1520-6149

Print_ISBN

0-7803-2431-5

Type

conf

DOI

10.1109/ICASSP.1995.479714

Filename

479714