Biomedical Engineering & Multimodal Data Analysis for Wearable Neurotechnology
The Intelligent Biomedical Sensing (IBS) group, led by Dr. Alexander von Lühmann concentrates on Machine Learning and Instruments for Comprehensive Brain-Body Monitoring. The IBS lab develops miniaturized wearable neurotechnology and body-worn sensors for unobtrusive monitoring of the embodied brain in the everyday world.
It uses machine learning on the multimodal sensor data, together with environmental context information, to contribute to a paradigm shift in individualized Comprehensive understanding of physical and mental health: Toward intelligent assessment and treatment of physical and mental states and risk factors. The expertise of the group encompasses
- Biomedical Electrical Engineering: Development of novel wearable sensing technology for brain and body that is non-invasive/non-hazardous, unobtrusive, multimodal and robust. Current focus in instrumentation development: functional Near Infrared Spectroscopy (fNIRS), diffuse optical tomography (DOT) and Oximetry, Electroencephalography (EEG), Electro -myo-, -oculo-, -cardiograpy (ExG).
- Machine Learning: Exploration of innovative methods for the extraction of biomarkers from complex multivariate bio signals derived from diffuse optics and electrophysiology. Physiological modelling, latent component analysis, and physiological transfer functions considering non-stationary and non-instantaneous relationships, context sensitivity, and automatic data annotation.
Alexander von Lühmann, Heidrun Wabnitz, Tilmann Sander, Klaus-Robert Müller
M3BA: A Mobile, Modular, Multimodal Biosignal Acquisition architecture for miniaturized EEG-NIRS based hybrid BCI and monitoring
Alexander von Lühmann, Yilei Zheng, Antonio Ortega-Martinez, Swathi Kiran, David C. Somers, Alice Cronin-Golomb, Louis N. Awad, Terry D. Ellis, David A. Boas, Meryem A. Yücel
Alexander von Lühmann, Zois Boukouvalas, Klaus-Robert Müller, Tülay Adalı
A new blind source separation framework for signal analysis and artifact rejection in functional Near-Infrared Spectroscopy
Alexander von Lühmann, Xinge Li, Klaus-Robert Müller, David A. Boas, Meryem A. Yücel
Improved physiological noise regression in fNIRS: A multimodal extension of the General Linear Model using temporally embedded Canonical Correlation Analysis
Alexander von Lühmann, Antonio Ortega-Martinez, David A. Boas, Meryem Ayşe Yücel
Using the General Linear Model to Improve Performance in fNIRS Single Trial Analysis and Classification: A Perspective
From August 29th to 30th the "Neuroscience of the Everyday World" conference convened neuroscientists, researchers and experts to discuss the challenges and opportunities of continuous, multimodal brain measurements in naturalistic environments. The conference was organized, among others, by BIFOLD Research grouplead Dr. Alexander von Lühmann.