Original title: Applying Dimensionality Reduction as Precursor to LSTM-CNN Models for Classifying Imagery and Motor Signals in ECoG-Based BCIs
Authors: Soham Bafana
This research delves into refining motor imagery classification in Brain-Computer Interfaces (BCIs) to aid motor rehabilitation for individuals with neurological impairments. Their novel approach focuses on boosting BCI efficiency. They employ dimensionality reduction techniques like UMAP with KNN to evaluate if supervised methods like LSTM and CNN are necessary for classification. Participants showing high KNN scores after dimensionality reduction also excel in accuracy using supervised deep learning models. This approach minimizes the need for extensive data labeling and supervised techniques due to personalized model needs and ample neural training data. It not only promises targeted therapies for motor dysfunction but also addresses safety and reliability concerns in the evolving BCI landscape. This innovative strategy holds potential for revolutionizing motor rehabilitation therapies and advancing the robustness of Brain-Computer Interfaces.
Original article: https://arxiv.org/abs/2311.13507