Based on our long-term accumulated healthy brain image database, we used meta-analysismultivariate analytical algorithms to extract the cognitive biological characteristics of individual subjects. By using machine learning / artificial intelligence algorithms, these features were further calculated for capturing individual cognition according to related brain networks. This biological index could be a potential marker for evaluating individual brain health, cognitive functions,risk for neurodegenerative diseases.

The AR-assisted endoscopic navigation system for brain surgery combines CT/MRI images, 3D cerebrovascular/nerve models,endoscopic images for surgical planningnavigation. AR glasses can display the 3D navigation inside the patient＇s skull, providing surgeons with intuitive 3D surgical navigation. In addition, the cerebrovascular/nerve model is also superimposed on the endoscopic image, allowing the surgeon to predict the upcoming surgical situation. The AR display endoscopic images can be simultaneously transmitted to the remote site for assistance through 5G communication.

We have developed a movement assistive system using EEG-controlled functional electric stimulation system. Users’ movement intentions are recognized by brain computer interface, implemented by deep learning network,the detected users’ intentions are then translated into commands to trigger a functional electric stimulation (FES) device to activate users’ particular movements, such as grasping, hand raising, holding a cup, etc. In our study, we have designed our own wireless dry-electrode EEG systemour own FES system.

PCA-BM includes two models: “Automatic BMs Segmentation AI Model”" Distant Brain Failure Prediction Model". The former one uses C2FNAS (coarse to fine network architecture search) to detect the location, size,number of brain metastases. The latter uses radiomics to extract numerous radiographic featuresemploys machine learning methods such as XGBoost to establish a prognostic model of brain metastases. PCA-BM provides more precision treatment decisions for patientsimproves personalizedaccurate overall stereotactic radiosurgery planning.

We have developed the device recorded the neuronal action potential of multiple mouse brainstheir cerebellum with high sampling rate (30 kHz)multiple channels to accelerate the study of the pathogenesis of neurodegenerative diseases. The team hopes to extend the multi-channel BCI devicebrainwave technology to more neurodegenerative disease biomarker analysis to help pharmaceutical companies to explore the pathogenesis of neurodegenerative diseaseshelp in new drug developmentclinical diagnosis.

This project designs a biomedical theranostic chip for neural disorders. It can not only function as an implantable device for treating the Parkinson＇s disease, depression, dementia, but also monitor physiological signals in wearable devices. For the development of medical device, the NeuLive system based on the microchip can accelerate the pre-clinical data collectionverification.

The Al-based web diagnostic system provides an online assessment tool for diagnosing schizophrenia. The Explainable Deep Neural Network classifier is deployed to analyze gray matterwhite matter to derive diagnostic classification of schizophrenia. The structural brain abnormalities associated with schizophrenia is visualized on the AI-based web diagnostic system at individual level.

MPT0G211 is a highly selective HDAC6 inhibitor, with comprehensive IP protection. MPT0G211 is mainly focused on multiple myelomabrain tumor indications. For IND package, we have completed CMC in drug substance, CMC in drug product, ADME,most preclinical toxicology studies, only the 28-days repeated dose toxicology study in dog is left. Once the 28-day repeated dose toxicology study in dog is completed, the IND package will be filed in the United StatesTaiwan in Q2 2022.

Our brain waves consist of a lot of nonlinear characteristics. Holo-Hilbert cross-frequency phase clustering (HHCFPC) is a cross-frequency coupling connectivity analysis based on the nonlinear Holo-Hilbert Spectral Analysis (HHSA) method. It is achieved by clustering the vectors of "phase difference" between the phase of a first layer IMF (obtained by HHSA) from one EEG channelthe phase of a second layer IMF from the other EEG channel. The results of HHCFPC can be used as a guide to optimize the parameters (e.g. frequency,frequencydepth of AM) of non-invasive brain stimulations.