Tan Group

Cortical-thalamic-basal ganglia recordings

Cortical-thalamic-basal ganglia recordings.

The ultimate aim of our group is to develop novel Brain Computer Interfaces (BCI), based on signals recorded from deep inside the brain, in order to restore function and improve quality of life for people with neurological disorders.

Oscillatory neuronal activities recorded from the subthalamic nucleus (STN) deep inside the brain and over the motor cortex (C3Cz) are systematically modulated by voluntary movements.

Oscillatory neuronal activities recorded from the subthalamic nucleus (STN) deep inside the brain and over the motor cortex (C3Cz) are systematically modulated by voluntary movements.

Features in the frequency domain in STN local field potentials recorded from human patients can be used to decode the gripping force.

Features in the frequency domain in STN local field potentials recorded from humans can be used to decode the gripping force.

BCI system based on signals recorded from electrodes chronically implanted in the brain can be used to drive robotic limbs or communication aids in patients with paralysis or amputation; the system can also be used to close the loop for DBS for better treatment of movement disorders such as Parkinson’s disease and essential tremor.

Group Science

Brain-Computer Interfaces (BCI) are a technology that can be used to decode a user’s mental state or movement intent from brain signals, and then use this information to interact with a computer or other device. The basal ganglia and thalamus are essential parts of the brain’s motor control network, but have remained overlooked in the context of BCIs. Our group specifically capitalises on the understanding of the roles played by these deep brain regions in motor control and movement disorders to develop 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 people 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 help train people 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 continuously deliver pulses of electrical 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.

With this work, we aim to translate neuroscientific and engineering knowledge into clinical therapies with a view to restoring function, whilst at the same time answering fundamental neuroscience questions.

Key Research Areas
  • Neurophysiology of human cortical-thalamic-basal ganglia network in healthy and abnormal 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)
  • Neuromodulation (Transcranial Magnetic Stimulation, Transcranial Current Stimulation, Deep Brain Stimulation, Neurofeedback Training)
  • Real-time signal acquisition and processing
  • Statistical and theoretical modelling
  • Machine Learning
Selected Publications
Unit Publication
Torrecillos F
Tinkhauser G
Fischer P
Green AL
Aziz TZ
Foltynie T
Limousin P
Zrinzo L
Ashkan K
Brown P
Tan H
2018. J. Neurosci., 38(41):8905-8917.
Unit Publication
Fischer P
Chen CC
Chang YJ
Yeh CH
Pogosyan A
Herz DM
Cheeran B
Green AL
Aziz TZ
Hyam J
Little S
Foltynie T
Limousin P
Zrinzo L
Hasegawa H
Samuel M
Ashkan K
Brown P
Tan H
2018. J. Neurosci., 38(22):5111-5121.
Unit Publication
Tinkhauser G
Pogosyan A
Tan H
Herz DM
Kühn AA
Brown P
2017. Brain, 140(11):2968-2981.
Unit Publication
Fischer P
Pogosyan A
Herz DM
Cheeran B
Green AL
Fitzgerald J
Aziz TZ
Hyam J
Little S
Foltynie T
Limousin P
Zrinzo L
Brown P
Tan H
2017. eLife, 6:e23947
Unit Publication
Tan H
Pogosyan A
Ashkan K
Green AL
Aziz TZ
Foltynie T
Limousin P
Zrinzo L
Hariz M
Brown P
2016. eLife;5:e19089.
Datasets and resources

Like other Groups at the MRC BNDU, we are committed to best practice in open research.  We have created and curated a range of primary data, metadata and related resources that can be readily downloaded by external users from the MRC BNDU's Data Sharing Platform. We highlight below just a few examples of the datasets and other resources we have shared for the benefit of our stakeholders.

Fischer P
Brown P
Tan H
10.5287/bodleian:M81wpxae8
Torrecillos F
Brown P
Tan H
10.5287/bodleian:rbR9ARpjB