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Motor impairments, frequently caused by neurological incidents like strokesor traumatic brain injuries, present substantial obstacles in rehabilitationtherapy. This research aims to elevate the field by optimizing motor imageryclassification algorithms within Brain-Computer Interfaces (BCIs). By improvingthe efficiency of BCIs, we offer a novel approach that holds significantpromise for enhancing motor rehabilitation outcomes. Utilizing unsupervisedtechniques for dimensionality reduction, namely Uniform Manifold Approximationand Projection (UMAP) coupled with K-Nearest Neighbors (KNN), we evaluate thenecessity 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 dimensionalityreduction 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 minimizesthe need for extensive data labeling and supervised deep learning techniques.This approach has significant implications not only for targeted therapies inmotor dysfunction but also for addressing regulatory, safety, and reliabilityconcerns in the rapidly evolving BCI field.

Applying Dimensionality Reduction as Precursor to LSTM-CNN Models for Classifying Imagery and Motor Signals in ECoG-Based BCIs