Development of a real-time LORETA BCI system with a non-invasive brain stimulation priming stage to enhance the efficacy of EEG-BCI

开发实时 LORETA BCI 系统，具有非侵入性脑刺激启动阶段，以增强 EEG-BCI 的功效

• 批准号: 2605898
• 金额: --
• 依托单位: University of Glasgow
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2605898
• 关键词: Development; real; time; LORETA; BCI

EEG is a convenient methods of recording brain activity with excellent time resolution, making it an effective tool for neurofeedback ie. real time self-regulation of brain activity. A number of neurological disorders could be treated through neurofeedback such as epilepsy, insomnia, pain or depression. However currently large percentage of people are not able to master this technique. The aim of this project is to create neurofeedback system based on EEG and to mimic fMRI by estimating deep brain activity in real time through source localisation methods. We will create a system for real time brain source localisation based on existing off line solutions that are computationally inefficient. We hypothetise that more effective brain modulation can be achieved by targeting deep brain structures. We will test this hypothesis based on brain structure responsible for the control of movement. We will also combine EEG neurofeedback with transcranial direct current stimulation as a priming method to enhance the effect of neurofeedback.If successfully this approach (deep brain source neuromodulation combined with priming) could be used in first instance for rehabilitation of movement but could be extended to treatment of any neurological condition treated with neurofeedback to improve its efficacy and increase success of learning neurofeedback strategy.Collaboration with Dr Learmonth from the School of Psychology would be instrumental in applying transcranial direct current stimulation and interpreting its neurological mechanism of action.

脑电是一种方便的记录大脑活动的方法，具有良好的时间分辨率，使其成为神经反馈的有效工具。实时自我调节大脑活动。许多神经疾病可以通过神经反馈来治疗，如癫痫、失眠、疼痛或抑郁。然而，目前有很大比例的人不能掌握这项技术。该项目的目的是创建基于脑电的神经反馈系统，并通过源定位方法实时估计大脑深层活动来模拟功能磁共振成像。我们将创建一个基于现有离线解决方案的实时脑资源定位系统，这些解决方案在计算上效率低下。我们假设，通过瞄准大脑深层结构可以实现更有效的大脑调制。我们将基于负责控制运动的大脑结构来检验这一假说。我们还将脑电神经反馈与经颅直流电刺激相结合作为启动方法来增强神经反馈的效果。如果成功，这种方法(脑深部神经调节与启动相结合)可以首先用于运动康复，但也可以扩展到任何神经反馈治疗的治疗，以改善其疗效和增加学习神经反馈策略的成功率。与心理学院的利蒙德博士合作将有助于应用经颅直流电刺激并解释其神经作用机制。