Session Detail

In Brain Machine Interfaces (BMI), goal-directed movement coordinated the reaching target in external environment through vision, and then the upper limb would be drove to execute the motor movement. To accomplish the goal success-fully, the reaching movement required on-line control to modi-fy or update the ongoing hand movement through visual feed-back in mostly condition. Moreover, during the on-line correc-tion of goal-directed movement, the visually derived relative position of hand and target was the key information for modi-fying the movement state. It had been found that the neural population in premotor cortex of monkeys encoded this infor-mation: visually derived relative position of hand and target. In aspect of intracortical micro stimulation, the forelimb areas in secondary motor cortex (M2) of rodent was considered equivalent to premotor area in monkeys. In this study, we demonstrated that the target-hand relative position could be decoded from the neural activity in M2 of rodents successfully. This decoding model worked as an internal error feedback scheme to decode the visual-feedback-related information from M2. By combing this internal error feedback model to present commonly used neural decoder in BMI could significantly improve the decoding performance.

 

Brain Machine Interface (BMI) was the method of transform-ing mental thoughts and imagination into actions. A real-time BMI system improved the quality of life of patients with se-vere neuromuscular disorders by enabling them to communi-cate with the outside world. At present, the important aspect of the real-time implementation of neural decoding algorithms on embedded systems has been often overlooked, notwith-standing the impact that limited hardware resources have on the efficiency/effectiveness of any given algorithm. In this study, the prediction model was built based on the neural activities (spikes) from rodent primary motor cortex (M1) and their corresponding trajectory of rodent forelimb by decoding algorithms, kernel sliced inverse regression (kSIR). Mean-while, a cost effective way of implementing and designing a demonstration platform for BMI research was presented, featuring a low-cost hardware implementation based on an open-source electronics platform Raspberry Pi. Here, the nonlinear decoding algorithm was implemented as separate signal analysis modules for the real-time decode of end effec-tor trajectory.

 

MI-BCI (Motor Imagery Brain-Computer Interface) with robotic feedback has been proven to be an effective tool for neurorehabilition. However, the brain activity shown in electroencephalography (EEG) is very complicated; therefore it requires training for a patient to master MI-BCI. On the other hand, studies has shown that non-invasive brain stimulation, such as TMS (transcranial magnetic stimulation) and tDCS (transcranial direct current stimulation) could improve motor functions of stroke patients and patients with Parkinson’s disease. In this study, we would design an experiment to find out whether non-invasive brain stimulation would increase the event-related desynchronization (ERD) phenomenon in EEG when performing MI tasks, and therefore provides effective EEG data for MI-BCI classification.

 

Parkinson’s disease (PD) is one of the neuron degenerative disorders which affects 1% of human population arising from the degeneration of the dopaminergic neurons of the substan-tia nigra pars compacta (SNc). Although scientists have come up with the medications for dopaminergic therapy which are able to improve the motor symptoms extremely in the early stages of PD, degeneration of dopaminergic neurons keep going on. Other treatments such as repetitive transcranial magnetic stimulation (rTMS) and cortical electrical stimulation (CES) have been applied based on the plastic-like mechanisms, which are considered to have the therapeutic potential for the brain degenerative diseases. In our previously studies, 6-hydroxydopamine (6-OHDA) induced Hemiparkinsonian rats model have been used to explore the gait performance through the combination of CES and theta burst stimulation modula-tion. In 6-OHDA-lesioned PD model rats, the results showed that CES with intermittent TBS (iTBS) protocol could improve gait performance based on stride length and speed. However, the mechanisms of TBS on PD are still unclear. In this research, apomorphine-induced rotation and rota-rod test were imple-mented to assess the motor performance in hemiparkinsonian rats. In the result, the falling time of rota-rod decreased and number of rotations increased as the progression of 6-OHDA lesion. For the CES-TBS, the number of rotations decreased after the intervention of iTBS compared to the increased rota-tion induced by continuous TBS (cTBS). In the electrocorti-cography (ECoG) we can observe the 30 Hz band on 6-OHDA ipsilateral lesioned site. The 30 Hz band ECoG was suppressed by cTBS but not in iTBS. We suppose the suppression of beta band oscillation by cTBS is mediated through primary motor cortex to subthalamic nucleus (M1-STN) projection and cou-pled with STN suppression. These findings agree with the hypothesis that M1-cTBS treatment may modulate parkin-sonian behavior and STN hyperactivity through M1-STN projection.

 

Accurate long-term control of prosthesis has been a significant issue for neural signals recorded at brain machine interface (BMI). The accuracy, stability and longevity are the key considerations of BMI applications. However, a lever-pressing task and electrode recordings were used to investigate the rodents encoding of hand velocity and trajectory in primary motor cortex (M1). The multiscale neural signals decoded by brain machine interfaces algorithm can be divided into two types: action potentials (spike) from individual neurons and local field potentials (LFP) from extracellular space around neurons. Since the different superiority of spikes and LFPs, this study proposed a decoding algorithm- kernel sliced inverse regression (kSIR) which combined spikes and LFPs as the multimodal inputs to decode the contents of the mind with high accuracy and stability. Results showed that the stability and accuracy were significantly improved, where the accuracy was improved from 0.88 ± 0.059 to 0.93 ± 0.061 for X-axis and 0.90 ± 0.022 to 0.97 ± 0.024 (R-squared, Mean ± SEM) for Y-axis. This implementation favorably lends itself toward the long-term decoding approach which can provide accurate real-time decoding of neural signals over periods of days.