CIF: Small: Advanced Ion Channel Models for Neurological Signal Processing -- Theory and Application to Brain-Computer Interfacing

研究人员正在研究大脑内神经元产生的电噪声信号的科学理论和工程后果。他们正在加深对这种神经元噪音的了解，主要是为了改善脑机接口(BCI)，这提高了各种瘫痪残疾患者的生活质量，如卢格里克？S病。通过了解背景噪声的统计特征，他们能够减少其对瘫痪患者大脑产生的意识信号的干扰。这为轮椅、定制的互联网浏览器和家庭环境创造了更可靠的控制信号。此外，研究人员正在应用他们的高级神经元模型来深入了解未患病的脑组织及其创建和传输信息的过程。这对于理解天然和合成神经网络的功能具有重要意义。具体地说，研究人员开发了一个新的神经元离子通道的数学和随机模型，该模型考虑了量子力学和热力学的考虑。将熵最大化原理应用于热平衡状态下的一组量子离子通道，解释了在神经和脑电记录中普遍存在的所谓的1/f噪声。由这些模型得到的参数与一种新的信号处理方法--倍频程平均谱校正相结合，大大降低了1/f噪声对脑电信号的干扰影响。因此，一种名为稳态视觉诱发电位(SSVEP)BCI的实验性脑接口的刺激频率可以增加到30赫兹以上的高伽马频段。这大大减少了这些SSVEP BCI的负面影响，使它们首次对瘫痪患者、飞行员和外科医生的平视显示器以及高性能游戏控制具有实际用途。

The investigators are studying the scientific theory and engineering consequences of the electrical noise signals generated by neurons inside the brain. They are developing a deeper understanding of this neuronal noise primarily to improve brain-computer interfaces (BCIs), which enhance the quality of life of patients with various paralyzing disabilities such as Lou Gehrig?s disease. By understanding the statistical characteristics of the background noise, they are able to reduce its interference with the conscious signal generated by the brain of the paralyzed patient. This creates a much more reliable control signal for wheelchairs, customized internet browsers, and the home environment. In addition, the investigators are applying their advanced neuron models to gain insight into non-diseased brain tissue and the processes by which it creates and transmits information. This promises to have significant implications for understanding the functioning of both natural and synthetic neural networks.Specifically, the investigators have developed a novel mathematical and stochastic model for neuronal ion channels that takes into account quantum mechanical and thermodynamic considerations. The principle of entropy maximization applied to a population of these quantum ion channels in thermal equilibrium explains the ubiquitous presence of the so-called 1/f-noise in neural and electroencephalographic (EEG) recordings. The parameters derived from these models are combined with a new signal processing paradigm called octave-averaged spectral rectification to dramatically reduce the interfering effect of 1/f-noise on EEG signals. As a result, the stimulus frequencies of an experimental type of brain interface called steady-state visual evoked potential (SSVEP) BCIs can be increased into the high-gamma band above 30Hz. This dramatically reduces the negative side effects of these SSVEP BCIs and makes them of practical use for the first time to paralyzed patients as well as heads-up displays for pilots and surgeons, and high-performance game control.