An EEG signal smoothing algorithm using upscale and downscale representation.

Abstract

<i>Objective.</i>Effective smoothing of electroencephalogram (EEG) signals while maintaining the original signal's features is important in EEG signal analysis and brain-computer interface. This paper proposes a novel EEG signal-smoothing algorithm and its potential application in cognitive conflict (CC) processing.<i>Approach.</i>Instead of being processed in the time domain, the input signal is visualized in increasing line width, the representation frame of which is converted into a binary image. An effective thinning algorithm is employed to obtain a unit-width skeleton as the smoothed signal.<i>Main results.</i>Experimental results on data fitting have verified the effectiveness of the proposed approach on different levels of signal-to-noise (SNR) ratio, especially on high noise levels (SNR⩽5 dB), where our fitting error is only 86.4%-90.4% compared to that of its best counterpart. The potential application of the proposed algorithm in EEG-based CC processing is comprehensively evaluated in a classification and a visual inspection task. The employment of the proposed approach in pre-processing the input data has significantly boosted the<i>F</i>₁score of state-of-the-art models by more than 1%. The robustness of our algorithm is also evaluated via a visual inspection task, where specific CC peaks, i.e. the prediction error negativity and error-related positive potential (Pe), can be easily observed at multiple line-width levels, while the insignificant ones are eliminated.<i>Significance.</i>These results demonstrated not only the advance of the proposed approach but also its impact on classification accuracy enhancement.