k-nearest neighbors (KNN) is a simple, yet powerful classification algorithm used in machine learning that identifies the k closest data points to a given input and assigns a label based on the majority class among those neighbors. It relies on the distance metric to determine how close the points are to each other, often utilizing Euclidean distance. This method is particularly useful for classifying data in brain-computer interface applications, as it can adaptively learn from new input patterns without a complex training phase.
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