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.

The technology aims to develop an AI-based integrated system for emotional detections (anger, sadness, happiness,neutral)multimodal physiological signals (ECG, EEG,PPG),apply to patients with cardiovascular disease. Patients monitor their emotionalphysical statusadminister bio-neuro-feedback to improve their well-being, track disease progression,prevent adverse prognosis.

Migraine patients account for about 640 million people (15) in the world,about 1.75 million (9.1) in Taiwan. Our team had developed a wearable brain-computer interface system with the newly developed graphene dry electrodeadaptive noise subspace reconstruction technology. This system combined the physiological indicators before migraine attacksuses AI technology to build an intelligent migraine early warning system,combined BrainSTIM electrical stimulation technology to assist migraine patients.

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.