Performance Evaluation of Neural Network and Linear Predictors for Near-Lossless Compression of EEG Signals

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Population

EEG signals

Comparison

Neural network predictors vs Linear predictors

Authors

Discussion

May facilitate EEG transmission over networks; leaves open prospective clinical validation of diagnostic fidelity.

Population

EEG signals

Intervention

Neural network predictors (single-layer perceptron [SLP], multilayer perceptron [MLP], and Elman network [EN])

Comparator

Linear predictors (autoregressive model [AR] and finite-impulse response filter [FIR])

Outcome

Theoretical bound delta 0 for near-lossless compression and corresponding bit rate rp

Single-layer perceptron yields the best results for near-lossless compression of EEG signals, facilitating efficient transmission over networks.

Sriraam et al. (2008) studied this question.

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