Seungmin Park

About

Seungmin Park is a researcher whose work lies at the intersection of brain–computer interfaces (BCIs) and deep learning, with a particular focus on decoding user intent from neural signals. His most cited study, "User State Classification Based on Functional Brain Connectivity Using a Convolutional Neural Network" (2021, 4 citations), introduces a novel approach that leverages functional brain connectivity patterns—rather than raw signal features—to classify cognitive states. By feeding connectivity matrices into a convolutional neural network, Park demonstrates how deep learning can more robustly interpret neural activity associated with motor imagery or mental tasks, addressing a key challenge in BCI reliability. This work contributes to the broader goal of enabling seamless, thought-driven control of external devices, such as robotic prosthetics or computer cursors, without requiring physical movement. Park’s research bridges neuroscience and artificial intelligence, offering a pathway toward more adaptive and user-friendly BCI systems. While his citation count is still growing, his methodological innovation in combining network neuroscience with CNNs marks a significant step forward in the field, making his work of particular interest to students and researchers exploring next-generation neural interfaces.

Research Focus

Key Achievements

Top Papers

1. 1
   User State Classification Based on Functional Brain Connectivity Using a Convolutional Neural Network

   4 citations · 2021

Key Collaborators

Contact & Links