We are developing novel methods to diagnose and cure neurological diseases using artificial intelligence with big data from EEG/MEG/ECoG signals.
Our efforts have so far allowed the creation of a robotic hand, the movements of which are controlled by human intention using Brain?Machine Interface (BMI) technology with ECoG and MEG.
A severely paralyzed patient with amyotrophic lateral sclerosis (ALS) used our BMI robot and succeeded in controlling the robotic hand and PC speller through his intentions to move his paralyzed hand.
Moreover, we have also demonstrated that undergoing training to use a BMI robotic hand induced plastic changes in the cortical activities of patients with phantom limb pain and modulated their pain based on the cortical alterations. In a similar fashion, we are developing a novel method to diagnose neurological diseases using machine-learning techniques with brain signals. We aim to improve medicine through the creation of novel technologies in collaboration with various researchers in informatics, engineering and neuroscience.
Our main topics:
1) Development and clinical application of BMI using ECoG/MEG
2) BMI neurofeedback to understand and treat neurological diseases
3) Computer-aided diagnosis of neurological diseases using MEG/EEG
Funding:
We are supported by JST, AMED, KAKENHI and other various funding agencies.