Evaluation of Denoising Techniques for EOG signals based on SNR Estimation
This work addresses noise reduction in EOG signals for biomedical applications, but it is incremental as it compares existing methods on a new dataset.
The paper evaluated four denoising algorithms for EOG signals using SNR estimation, finding that FIR bandpass filters performed best with a 15 dB improvement in SNR.
This paper evaluates four algorithms for denoising raw Electrooculography (EOG) data based on the Signal to Noise Ratio (SNR). The SNR is computed using the eigenvalue method. The filtering algorithms are a) Finite Impulse Response (FIR) bandpass filters, b) Stationary Wavelet Transform, c) Empirical Mode Decomposition (EMD) d) FIR Median Hybrid Filters. An EOG dataset has been prepared where the subject is asked to perform letter cancelation test on 20 subjects.