A Deep Learning Approach for Robotic Arm Control using Brain-Computer Interface
A Deep Learning Approach for Robotic Arm Control using Brain-Computer Interface
复制标题
使用脑机接口控制机械臂的深度学习方法
DOI：
10.46300/91011.2020.14.18
复制
发表时间：
2020
影响因子：
--
通讯作者：
J. Isnard
中科院分区：
文献类型：
--
作者：
T. Tanzi;J. Isnard
脑机接口（BCI）是一种能够使人类与外部策略进行沟通以达到预期结果的技术。本文提出了一种基于运动图像(MI) -脑电图（EEG）信号的机械外臂升降运动。使用ad8232放大器的3通道电极系统提取MI-EEG信号。电极放置在三个位置，即C3， C4和右乳突。对提取的脑电信号进行巴特沃斯滤波和sm -9小波包分解（WPD）处理，对原始脑电信号进行去噪处理。从去噪信号中提取熵、方差、标准差、协方差、谱质心等统计特征。然后应用统计特征来训练多层感知器(MLP) -深度神经网络（DNN），将手部运动分为两类；“不动手”和“动手”。得到的k-fold交叉验证准确率为85.41%，并计算了其他分类指标，如精密度、召回灵敏度、特异性和F1评分。训练后的模型与arduino接口，在实时环境中根据DNN模型预测的类别移动机械臂。提出的端到端低成本深度学习框架在实时BCI方面提供了实质性的改进。
Brain-Computer Interface (BCI) is atechnology that enables a human to communicate with anexternal stratagem to achieve the desired result. This paperpresents a Motor Imagery (MI) – Electroencephalography(EEG) signal based robotic hand movements of lifting anddropping of an external robotic arm. The MI-EEG signalswere extracted using a 3-channel electrode system with theAD8232 amplifier. The electrodes were placed on threelocations, namely, C3, C4, and right mastoid. Signalprocessing methods namely, Butterworth filter and Sym-9Wavelet Packet Decomposition (WPD) were applied on theextracted EEG signals to de-noise the raw EEG signal.Statistical features like entropy, variance, standarddeviation, covariance, and spectral centroid were extractedfrom the de-noised signals. The statistical features werethen applied to train a Multi-Layer Perceptron (MLP) -Deep Neural Network (DNN) to classify the hand movementinto two classes; ‘No Hand Movement’ and ’HandMovement’. The resultant k-fold cross-validated accuracyachieved was 85.41% and other classification metrics, suchas precision, recall sensitivity, specificity, and F1 Score werealso calculated. The trained model was interfaced withArduino to move the robotic arm according to the classpredicted by the DNN model in a real-time environment.The proposed end to end low-cost deep learning frameworkprovides a substantial improvement in real-time BCI.