Tan Group

Group Science

Brain-Computer Interface (BCI) is a technology that aims at decoding a user’s mental state or movement intent through brain signals, and using this information to move a computer interface, wheelchair, robotic arm or other device. Subcortical structures, such as the basal ganglia and thalamus, are an essential part of the motor control network but have remained overlooked in the context of BCIs. Our lab specifically capitalises the understanding about the role of these deep brain regions in motor control and movement disorders in developing novel BCIs. We utilise signals acquired from these structures to detect specific patterns, and then translate them into the actions of an end effector.

Central to any BCI is the notion that a specific goal can be achieved by volitional modulation of neural activity. BCI therefore provides a potential tool for neurofeedback training, in which patients may learn to modulate their own pathological neural activity through reinforcement learning. We identify pathological neural activity related to different disorders such as Parkinson’s disease, develop BCIs to train patients to regulate this activity, and evaluate the effect of this approach in reducing symptoms.

Another direction of the group is to use BCI to trigger Deep Brain Stimulation (DBS) to treat essential tremor. Current stimulation systems deliver continuous pulses of energy to brain regions that exhibit pathological activity. We focus on processing neural activity to decode movement status and pathological signatures, and use this information to control when stimulation is delivered.

Key Research Areas: 
Neurophysiology of human cortical-thalamic-basal ganglia network in healthy and impaired action.
Development of novel BCI systems based on subcortical signals for neuroprosthetic control and neural feedback training.
Development of BCI-DBS system for better treatment of essential tremor.
Research Techniques: 
Electrophysiology (EEG, LFP, MEG)
Neuromodulation (Transcranial Magnetic Stimulation, Transcranial Current Stimulation, Deep Brain Stimulation, Neurofeedback Training)
Real-time signal acquisition and processing
Statistical and theoretical modelling
Machine Learning