Brain Analysis
You cannot read minds – or can you? For those who cannot communicate at all, advanced speech prosthetics controlled by 'mind-reading' could be the way out. Ground-breaking study conducted from our Brain Analysis team has added an important piece to the puzzle that will propel the technology forward.
Brain Analysis team explores novel machine learning methods combined with neuroscientific approaches to understand how the human brain functions. We contact data collection studies using different non-invasive modalities (e.g., EEG, fMRI, fNIRS) in a unimodal and bimodal approach.
Our aim is to develop novel non-invasive BCI methods to help people suffering from neurological diseases. Particular focus of our research is in the area of inner speech detection that could ´give voice´ to people suffering from Amyotrophic lateral sclerosis (ALS) or Locked-in syndrome (LIS).
Hardware
EEG/fMRI compatible 64 channels headset for synchronous recordings, Brain Products
Master theses
2024, Nathan Hiruy, EEG-based control of ground robot via Brain Computer Interface.
2023, Maxime Arnaud, Inner speech detection using bimodal inner speech dataset.
2022, Valeria Buenrostro-Leiter, Fellipe Rollin, Brain Signal Analysis for Inner detection, link External link..
2022, Lisa Jonsson, Using machine learning to analyse EEG brain signals for inner speech detection.
Awards
2022, 1st place winners BCI Hackathon, PyEPosers - An ECoG Hand Pose Data Analysis Project, link External link.
Contact
Current projects
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2nd study on Inner speech decoding
Bimodal synchronous electroencephalography-functional magnetic resonance imaging dataset for inner-speech recognition
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NeuraMind
Decoding brain signals for detection of frontotemporal dementia and Alzheimer's disease using non-invasive electroencephalography and artificial intelligence
Completed projects
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1st study on Inner speech decoding
Bimodal asynchronous electroencephalography-functional magnetic resonance imaging dataset for inner-...
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