Session 2 - Details




Beyond Proof of Concept: Can Brain-Computer Implants improve daily life in people with Locked-in Syndrome?

Nick Ramsay

People with severe loss of motor control, such as complete paralysis, can suffer from an inability to communicate and are excluded from social interaction. Until recently, there was no solution to offer to these patients. In November 2016 we presented the first case of an implanted Brain-Computer Interface system that enabled a late-stage ALS patient with Locked-In Syndrome to control spelling software at home, without help (www.neuroprosthesis.eu). Key to this system is the principle that the brain generates motor signals even when they do not reach the muscles, which can be detected and interpreted in real-time. I will present use of the BCI implant by the first participant at home over a period of almost 5 years, and progress with 2 more participants included since. In addition I will discuss envisioned next developments and hurdles in moving the BCI research for communication into the home of end users.

Understanding Deep Learning Models for Brain Signals

Tonio Ball

Deep learning with convolutional neural networks (CNNs) is increasingly used for brain signal analysis and interfacing. Little is however known about the internal representations of brain signals that emerge from the training of deep learning systems. Such knowledge might increase their value both for medical applications where black-box algorithms are inacceptable, and for neuroscientific discovery. Here I summarize recent progress in understanding deep learning models for brain signals. Specifically, we studied how CNNs decode hand movement parameters from intracranial EEG (iEEG) signals during a continuous motor task. Our findings reveal that individual CNN units became specialized to the extraction of either iEEG amplitude or phase information originating in the motor cortex in physiological frequency bands. This segregation into two functionally different neural populations became more distinct in the later networks layers. Our study thus provides insights into the principles of how deep networks learn to represent brain signals, and may facilitate the development of more transparent deep learning models for neuroscience and technology.

Computational Neuroscience Group