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An EEG signal smoothing algorithm using upscale and downscale representation*
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AI
DOI：10.1088/1741-2552/add297.png)
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Abstract
En 中文
Objective. 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. Approach. 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. Main results. 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 F1 score 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. Significance. These results demonstrated not only the advance of the proposed approach but also its impact on classification accuracy enhancement.
Keywords:
electroencephalogram
signal processing
skeletonization
thinning
cognitive conflict
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