Brain-Machine Interface

Research Topic: EEG Classification Using Non-Invasive Devices

Brain–Machine Interface (BMI) technology enables human–machine interaction using brain signals and is widely applied in healthcare and rehabilitation.

Future applications include industrial assistance and unmanned system operation.

Traditional BMI systems often rely on invasive devices, which limits their practical use.

Non-invasive BMI has advanced with machine learning, but EEG signals are weak and susceptible to noise, making analysis challenging.

This research develops non-invasive BMI systems and evaluates methods for analyzing and classifying EEG data.