VME-DWT: An Efficient Algorithm for Detection and Elimination of Eye Blink From Short Segments of Single EEG Channel

Objective: Recent advances in development of low-cost single-channel electroencephalography (EEG) headbands have opened new possibilities for applications health monitoring and brain-computer interface (BCI) systems. These recorded EEG signals, however, are often contaminated by eye blink artifacts that can yield the fallacious interpretation brain activity. This paper proposes an efficient algorithm, VME-DWT, to remove blinks a short segment single channel. Method: The proposed algorithm: (a) locates intervals using Variational Mode Extraction (VME) (b) filters only interval automatic Discrete Wavelet Transform (DWT) algorithm. performance VME-DWT is compared with Decomposition (AVMD) DWT-based algorithms, suppressing Results: detects 95% from signals SNR ranging -8 +3 dB. shows superiority AVMD DWT higher mean value correlation coefficient (0.92 vs. 0.83, 0.58) lower RRMSE (0.42 0.59, 0.87). Significance: be suitable algorithm removal systems as it is: computationally-efficient, signal filtered millisecond time resolution, automatic, no human intervention required, (c) low-invasive, without contamination remained unaltered, (d) low-complexity, need artifact reference.