RNN Classification of Spectral EEG/EMG Data Associated with Facial Movements for Drone Control
RNN Classification of Spectral EEG/EMG Data Associated with Facial Movements for Drone Control
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DOI：
10.1109/southeastcon51012.2023.10115185
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发表时间：
2023-04
期刊：
影响因子：
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通讯作者：
Akintomide Adebile;Shaen Mehrzed;Destinee Hicks;Joshua Hale;Darryl Wiltz;R. Alba-Flores
中科院分区：
文献类型：
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作者：
Akintomide Adebile;Shaen Mehrzed;Destinee Hicks;Joshua Hale;Darryl Wiltz;R. Alba-Flores
这项研究涉及开发一种无人机控制系统，该系统通过使用长短期记忆（LSTM）和门控循环单元（GRU）等循环神经网络（RNN）将前额的脑电图和肌电图与不同的面部运动联系起来。由于目前的无人机控制方法主要局限于手持设备，常规操作员在飞行时是主动参与的，无法进行任何被动控制。无人机的被动控制将在各种应用中被证明是有利的，因为无人机操作员可以专注于其他任务。讨论了所选方法的优点和一些备选系统设计的优点。在这项研究中，使用来自OpenBCI头带的电极在前额皮质的三个位置（fp1， fpz和fp2）获取EEG信号，并观察快速傅里叶变换（FFT）频率-幅度分布的模式。在记录0- 60Hz频率的脑电图信号的同时，在两个耳垂上放置两个参考电极，重复五种不同的面部表情。在脑电图测量期间收到的肌电信号噪声没有被过滤掉，但被观察到是最小的。首先为所做的动作创建一个数据集，然后通过平均误差（MAE）和统计误差偏差分析进行分类，然后通过将FFT振幅与动作相关联，使用LSTM和GRU神经网络进行分类。平均而言，LSTM网络的分类准确率为78.6%，GRU网络的分类准确率为81.8%。
This research involves developing a drone control system that functions by relating EEG and EMG from the forehead to different facial movements using recurrent neural networks (RNN) such as long-short term memory (LSTM) and gated recurrent Unit (GRU). As current drone control methods are largely limited to handheld devices, regular operators are actively engaged while flying and cannot perform any passive control. Passive control of drones would prove advantageous in various applications as drone operators can focus on additional tasks. The advantages of the chosen methods and those of some alternative system designs are discussed. For this research, EEG signals were acquired at three frontal cortex locations (fp1, fpz, and fp2) using electrode from an OpenBCI headband and observed for patterns of Fast Fourier Transform (FFT) frequency- amplitude distributions. Five different facial expressions were repeated while recording EEG signals of 0- 60Hz frequencies with two reference electrodes placed on both earlobes. EMG noise received during EEG measurements was not filtered away but was observed to be minimal. A dataset was first created for the actions done, and later categorized by a mean average error (MAE), a statistical error deviation analysis and then classified with both an LSTM and GRU neural network by relating FFT amplitudes to the actions. On average, the LSTM network had classification accuracy of 78.6%, and the GRU network had a classification accuracy of 81.8%.