Brain Machine Interface Team

Invasive brain/bio-medical signal sensing and processing

Closed-loop stimulation & recording system for homeostasis regulation of Parkinson’s disease

 Parkinson’s disease (PD) is a progressive and degenerative brain disease, including motor dysfunctions and other non-motor symptoms caused by gradual loss of dopaminergic neurons in substantia nigra of the brain. However, techniques for treating PD in clinical practice have side effects, and the mechanism of treatment is not clearly known. We develop the multi-modal closed-loop system that overcomes the limitations of the existing levodopa therapy and deep brain stimulation(DBS). 

Stimulation artifact cancellation for simultaneous stimulation and recording system

 In conventional closed-loop DBS systems, the stimulation artifact is too large to observe neural signals during stimulation. We  contributes to the development of an advanced closed-loop system by removing stimulation artifacts and restoring spikes signals. 

Non-invasive brain/bio-medical signal sensing and processing

Diagnosis of Alzheimer’s disease using portable fNIRS

 Alzheimer’s disease (AD) is a chronic progressive neurodegenerative brain disease that typically presents as dementia. Although a cure for AD is currently lacking, medication therapies in the early stage can alleviate disease progression. Therefore, we develop an early diagnosis and management system for AD. 

Signal processing and machine learning techniques for brain connectivity and bio-signal quality enhancement

 Because the bio-signals measured with non-invasive devices are suffered by several artifacts, It is required to process signals before analyzing. Also due to the size and complexity of the data, a machine learning technique can be an effective tool for finding features. Therefore we are studying how to apply these techniques effectively to the bio-signal. 

Additional researches

Sensory signal processing for artificial biomimetic sensors (olfactory, auditory and tactile)

 We develop hardware-software convergence technology for cognitive sensory signal generation by simulating human mental sense and processing signals similar to human, and implements bio-mimetic AI for artificial touch.

Calcium imaging signal processing

 In the calcium imaging data, we find the hidden implications of the experiment through neuron detection, SNR enhancement, and behavioral experiment analysis. 

Human-inspired networks for the next generation communication  (humanoid)

 Humanoid will require amazing ability to process extensive information from various sensors and to react as fast as possible. In the case of humans, they process many sensory data in energy efficient way. Furthermore, they can react fast against fatal sensory inputs with autonomic reflex. We study the ability of information processing in human and implement these ability for the innovative humanoid robots. 

Related projects

Related Societies

Related Conferences