iCONNECT aims to give severely paralyzed people the means to communicate by merely imagining to talk or make hand gestures. Imagining specific movements generates spatiotemporal patterns of neuronal activity in the brain which I intend to record and decode with an intracranial Brain-Computer Interface (BCI) system. Many people suffer from partial or full loss of control over their body due to stroke, disease or trauma, and this will increase with population ageing. With both duration and quality of life beyond 60 increasing in the western world, more and more people will suffer from the consequences of function loss (mostly stroke) with the prospect of living for decades with the handicap, and will stand to benefit from restorative technology that has yet to be developed. I believe that functionality can be restored with brain implants. My goal is to develop a BCI that can interpret activity patterns on the surface of the brain in real-time. For this we need to discover how the brain codes for (imagined) actions, how codes can be captured and decoded and how an intracranial BCI system impacts on a user. I will use state of the art techniques (7 Tesla MRI and electrocorticography, ECoG) to explore brain codes and develop decoding strategies. Interactions between user and implanted device will be studied in paralyzed people. I will directly link decoded movements to animated visual feedback of the same body part, expecting to induce a feeling of ownership of the animation, and thereby a sense of actual movement. This research is only possible because of the latest developments in imaging of human brain activity, machine learning techniques, and micro systems technology. My lab is unique in bringing together all these techniques. Success of the project will lead to deeper understanding of how sensorimotor functions are represented in the human brain. The ability to ‘read’ the brain will add a new dimension to the field of neural prosthetics.
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