The nonlinear energy operator (NEO) accentuates high-frequency content and demonstrates efficacy as a computationally efficient tool for spike detection in simulated signals and real EEGs.
The nonlinear energy operator is an effective and computationally efficient tool for spike detection in EEG signals.
Nonlinear energy operator (NEO) gives the estimate of energy content of a linear oscillator. This has been used to quantify the AM-FM modulating signals present in a sinusoid. In this paper, we give a new interpretation of NEO and extend its use in stochastic signals. We show that NEO accentuates the high-frequency content. This instantaneous nature of NEO and its very low computational burden make it an ideal tool for spike detection. The efficacy of the proposed method has been tested with simulated signals as well as with real electroencephalograms (EEG's).
Mukhopadhyay et al. (1998) studied Spike detection in electroencephalograms (EEGs). Nonlinear energy operator (NEO) was evaluated on Efficacy in spike detection. The nonlinear energy operator (NEO) accentuates high-frequency content and demonstrates efficacy as a computationally efficient tool for spike detection in simulated signals and real EEGs.