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BCI System using a Novel Processing Technique Based on Electrodes Selection for Hand Prosthesis Control ?

机译：BCI系统使用基于电极选择的新型处理技术，用于手术室控制

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This work proposes an end-to-end model architecture, from feature extraction to classification using an Artificial Neural Network. The feature extraction process starts from an initial set of signals acquired by electrodes of a Brain-Computer Interface (BCI). The proposed architecture includes the design and implementation of a functional six Degree-of-Freedom (DOF) prosthetic hand. A Field Programmable Gate Array (FPGA) translates electroencephalography (EEG) signals into movements in the prosthesis. We also propose a new technique for selecting and grouping electrodes, which is related to the motor intentions of the subject. We analyzed and predicted two imaginary motor-intention tasks: opening and closing both fists and flexing and extending both feet. The model implemented with the proposed architecture showed an accuracy of 93.7% and a classification time of 8.8y?s for the FPGA. These results present the feasibility to carry out BCI using machine learning techniques implemented in a FPGA card.
机译：这项工作提出了一种端到端模型架构，从特征提取到使用人工神经网络进行分类。特征提取处理从脑电脑接口（BCI）的电极获取的初始信号开始。所提出的架构包括设计和实现功能六个自由度（DOF）假肢手。现场可编程门阵列（FPGA）将脑电图（EEG）信号转换为假体的运动。我们还提出了一种选择和分组电极的新技术，该技术与受试者的电机意图有关。我们分析并预测了两个虚构的电动意图任务：打开和关闭拳头并弯曲和延伸双脚。用拟议架构实现的模型显示了93.7％的精度，为FPGA的分类时间为8.8y。这些结果在使用FPGA卡中实现的机器学习技术来实现开展BCI的可行性。

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