Together, we are developing a brain-computer-interface (BCI) to non-invasively decode electrical signals into precise control signals using Deep Learning architectures.
Our interdisciplinary and diverse team comprises chemical, electrical, mechanical and neural engineering students as well as computer science and robotics students from 12 nationalities.
We are neuroTUM, a student-run association focused on developing neurotechnology to educate and connect students. Our first project is the development of a brain-computer interface (BCI) to help tetraplegic individuals regain independence. In the future, our technology aims to analyze brain activity, to interpret intentions and enable control of wheelchairs or translation of thoughts into speech.
Have you ever wondered how to design experiments in the domain of neurotechnology? In this article we’ll take you through the experimental teams’ workflow for designing electroencephalography (EEG) experiments for brain-computer interface (BCI) applications.
Signal processing is a vital step in Brain-Computer Interfaces (BCIs), enabling the transformation of raw EEG data into meaningful information. This article explores the essential tasks performed by neuroTUM's signal processing team to extract valuable insights from brain activity. From noise filtering to artifact removal and spectral analysis, signal processing plays a pivotal role in advancing BCIs, unlocking new frontiers in neuroscience and human-computer interaction.
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