An innovative pipeline integrating wavelet transform, k-nearest neighbors (k-NN), and temporal wrapping approaches is presented in this work for the classification of eye blinks in electroencephalogram (EEG) recordings. The proposed process commences with raw EEG data preprocessing followed by wavelet treatment to extract significant time and frequency domain properties. These characteristics are subsequently utilized by a k-NN classifier to enhance the accuracy of eye blink recognition by using spatial relationships within EEG signals. Notably, the integration of a novel time wrapping technique deals with temporal dynamics, supplying the model with resilience to fluctuations in eye blink patterns across time. The pipeline’s enhanced efficiency can be observed through validation using a customized EEG dataset, particularly when navigating the time complexity associated with eye blink events. Evaluations performed against current techniques demonstrate the efficacy of the proposed solution. The outcomes not only demonstrate the methodology’s accuracy, but also its possible use in real-world settings featuring clinical diagnostics and human-computer interactions. This work conveys a strong basis for the discipline of EEG-based eye blink classification, having implications to enhance neuroscience and facilitating beneficial uses in healthcare and assistive technologies.