Our lab is interested in the neurophysiological representation of decision making and behaviour, including the sensorimotor transformation from sensory input (e.g., visual, auditory, tactile, proprioceptive) to motor output (e.g., moving your hand from one location to another). We have several parallel avenues of research into these topics.

Through our collaboration with Julio Martinez-Trujillo's primate lab at Western University in London, Ontario, we look at how ensembles of neurons in lateral prefrontal cortex represent various stages of behaviour including attention, sensory processing, memory, motor planning, and motor execution. We then use machine learning tools to build decoders that can process the neuronal activity in real-time and predict behavioural outcomes. It is possible to then use the predicted outcome to control an external device (e.g., robot arm). If the monkey can see the robot then the monkey and the robot can adapt to each other for better control. This closed-loop brain control system is called a brain-machine interface (BMI) or brain-computer interface (BCI).

We also use BCIs in humans undergoing awake brain surgery for the implantation of deep brain stimulation electrodes for the treatment of movement disorders like Parkinson's disease, essential tremor, and focal dystonia. BCI technology enables unique experiments that examine learning and plasticity in the brain. We also use BCIs to examine how volitional control of brain signals interacts with motor symptoms to motivate the development of a new class of therapeutic devices.

Finally, we are also looking forward to developing brain-controlled assistive devices using long-term electrode implants in severely disabled humans.