Motor imagery (MI) is a well-documented technique used by subjects in BCI (Brain Computer Interface) experiments to modulate brain activity within the motor cortex and surrounding areas of the brain. In our term project, we conducted an experiment in which the subjects were instructed to perform motor imagery that would be divided into two classes (Right and Left). Experiments were conducted with two different types of electrodes (Gel and POLiTag) and data for individual subjects was collected. In this paper, we will apply different machine learning (ML) methods to create a decoder based on offline training data that uses evidence accumulation to predict a subject's intent from their modulated brain signals in real-time.

翻译：运动想象（MI）是脑机接口（BCI）实验中受试者广泛采用的一种技术手段，通过该技术可调节运动皮层及周边脑区的脑电活动。在本课程项目中，我们开展了一项实验，要求受试者执行两类运动想象任务（右手与左手）。实验采用两种不同类型电极（凝胶电极与POLiTag电极）进行，并收集了每名受试者的个体数据。本文将应用多种机器学习方法，基于离线训练数据构建解码器，通过证据累积机制从受试者实时调制的脑电信号中预测其运动意图。