  Motor impairments, frequently caused by neurological incidents like strokes
or traumatic brain injuries, present substantial obstacles in rehabilitation
therapy. This research aims to elevate the field by optimizing motor imagery
classification algorithms within Brain-Computer Interfaces (BCIs). By improving
the efficiency of BCIs, we offer a novel approach that holds significant
promise for enhancing motor rehabilitation outcomes. Utilizing unsupervised
techniques for dimensionality reduction, namely Uniform Manifold Approximation
and Projection (UMAP) coupled with K-Nearest Neighbors (KNN), we evaluate the
necessity of employing supervised methods such as Long Short-Term Memory (LSTM)
and Convolutional Neural Networks (CNNs) for classification tasks. Importantly,
participants who exhibited high KNN scores following UMAP dimensionality
reduction also achieved high accuracy in supervised deep learning (DL) models.
Due to individualized model requirements and massive neural training data,
dimensionality reduction becomes an effective preprocessing step that minimizes
the need for extensive data labeling and supervised deep learning techniques.
This approach has significant implications not only for targeted therapies in
motor dysfunction but also for addressing regulatory, safety, and reliability
concerns in the rapidly evolving BCI field.
