School of Entrepreneurship, Wuhan University of Technology, Wuhan Hubei Province, 430070, China.
School of Foreign Languages, Wuhan Business University, Wuhan, 430056, China.
Artif Intell Med. 2020 Jan;102:101766. doi: 10.1016/j.artmed.2019.101766. Epub 2019 Nov 19.
Due to growth in population, Individual persons with disabilities are increasing daily. To overcome the disability especially in Locked in State (LIS) due to Spinal Cord Injury (SCI), we planned to design four states moving robot from four imagery tasks signals acquired from three electrode systems by placing the electrodes in three positions namely T1, T3 and FP1. At the time of the study we extract the features from Continuous Wavelet Transform (CWT) and trained with Optimized Neural Network model to analyze the features. The proposed network model showed the highest performances with an accuracy of 93.86 % then that of conventional network model. To confirm the performances we conduct offline test. The offline test also proved that new network model recognizing accuracy was higher than the conventional network model with recognizing accuracy of 97.50 %. To verify our result we conducted Information Transfer Rate (ITR), from this analysis we concluded that optimized network model outperforms the other network models like conventional ordinary Feed Forward Neural Network, Time Delay Neural Network and Elman Neural Networks with an accuracy of 21.67 bits per sec. By analyzing classification performances, recognizing accuracy and Information Transformation Rate (ITR), we concluded that CWT features with optimized neural network model performances were comparably greater than that of normal or conventional neural network model and also the study proved that performances of male subjects was appreciated compared to female subjects.
由于人口增长，每天都有越来越多的个人残疾。为了克服残疾，特别是由于脊髓损伤（SCI）而导致的锁定状态（LIS），我们计划设计一个从三个电极系统获得的四个图像任务信号的四个状态移动机器人，将电极放置在 T1、T3 和 FP1 三个位置。在研究过程中，我们从连续小波变换（CWT）中提取特征，并通过优化神经网络模型进行训练以分析特征。所提出的网络模型表现出最高的性能，准确率为 93.86%，优于传统网络模型。为了确认性能，我们进行了离线测试。离线测试也证明了新网络模型的识别准确率高于传统网络模型，识别准确率为 97.50%。为了验证我们的结果，我们进行了信息传输率（ITR）分析，从该分析中我们得出结论，优化后的网络模型的性能优于传统的普通前馈神经网络、时滞神经网络和 Elman 神经网络等其他网络模型，准确率为 21.67 位/秒。通过分析分类性能、识别准确率和信息传输率（ITR），我们得出结论，与正常或传统的神经网络模型相比，CWT 特征与优化后的神经网络模型的性能相当更高，并且该研究还证明男性受试者的表现优于女性受试者。
