Title :
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
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;
Conference_Titel :
Acoustics, Speech, and Signal Processing, 1995. ICASSP-95., 1995 International Conference on
Conference_Location :
Detroit, MI
Print_ISBN :
0-7803-2431-5
DOI :
10.1109/ICASSP.1995.479714
