基于前馈网络的脑机接口手动作图像任务分类

Mohd Shuhanaz Zanar Azalan, M. Paulraj, Sazali bin Yaacob
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引用次数: 7

摘要

本文提出了一种简单的脑机接口(BMI)系统,该系统可以将模拟手部运动时大脑信号的节奏变化转换为实际活动的运动命令。在分析过程中使用了四种不同的想象任务。一种非基于刺激的脑机接口方法使用19通道脑电图电极从10个不同的受试者获取大脑信号。从每个通道提取5个光谱波段特征,并将其与相应的心理任务相关联。然后使用前馈神经网络对特征进行分类。使用不同比例的训练/测试数据集进行训练。然后对所建立的网络模型进行了有效性检验。通过仿真对所建立的网络模型的性能进行了评价。结果表明,所提出的协议方法和频率子带范围选择都可以作为简单BMI系统运动图像任务分类的通用方法。
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Classification of hand movement imagery tasks for brain machine interface using feed-forward network
In this paper, a simple Brain Machine Interface (BMI) system that translates a change of rhythm from brain signal while performing a simulation of hand movement mentally into a real activity movement command is proposed. Four different imaginary tasks are used in the analysis process. A non-stimulus-based BCI approach is used to acquire the brain signal from ten different subjects using 19 channel EEG electrodes. Five spectral band features from each channel are extracted and associated to the respective mental tasks. The features are then classified using Feed-Forward Neural Network. The training is conducted using different ratio of training/testing data set. The developed network models are then tested for its validity. The performance of the developed network models are evaluated through simulation. The result shows that the proposed of both protocol approach and frequency sub band range selection can be an alternative general procedure to classify motor imagery task for a simple BMI system.
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