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Results: The accuracy of the classification algorithms improved significantly (up to 27.18% in the BCI Competition IV dataset, and up to 42.56% with records made with an Emotiv EPOC+). In addition, the proposed filtering algorithm has similar or better results when compared with the Filter Bank Common Spatial Pattern (FBCSP), which has disadvantages in a multiclass classification.
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by AD Hitchins · 1968 · Cited by 38 — The techniques of Nomarski interference contrast microscopy and phase-contrast microscopy were compared for their utility in monitoring sporulation and ...
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Methods: A graph Laplacian quadratic form using the Phase Locking Value (PLV) is applied to generate a new filtered signal in the preprocessing stage.
The mean squares of each channel (including a channel weighting for the surround channels) are summed and averaged over a 400 millisecond interval. All samples ...
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Objective: The performance of an EEG-based brain-computer interface (BCI) system is highly dependent on signal preprocessing. This manuscript presents a filtering method to improve the feature classification algorithms typically used in BCI.
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Conclusion: This paper shows how our PLV-based filtering between EEG channels could improve the performance of a BCI.
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