TY -的A2 Sinha G R盟——Subasi Abdulhamit AU -面Qaisar接受,赛义德PY - 2021 DA - 2021/11/09 TI -合奏运动图像的基于机器学习的分类任务在脑机接口SP - 1970769六世- 2021 AB -脑-机接口(BCI)许可人与障碍与现实世界不使用神经肌肉通路。bci是基于人工智能驾驶系统。他们收集大脑活动模式与心理过程,将它们转化为致动器的命令。BCI系统的潜在应用在康复中心。在这种背景下,一种新颖的方法设计了运动图像的自动识别(MI)的任务。贡献是一种有效的杂交的多尺度主元分析(MSPCA),小波包分解(WPD),从部分波段统计特征提取,基于整体学习分类器的分类MI的任务。预期的脑电图(EEG)信号分段和去噪。Daubechies的实现去噪算法小波变换(WT)纳入MSPCA。WT与5的分解。开始,小波包分解(WPD), 4级的分解,用于次能带的形成。 The statistical features are selected from each subband, namely, mean absolute value, average power, standard deviation, skewness, and kurtosis. Also, ratios of absolute mean values of adjacent subbands are computed and concatenated with other extracted features. Finally, the ensemble machine learning approach is used for the classification of MI tasks. The usefulness is evaluated by using the BCI competition III, MI dataset IVa. Results revealed that the suggested ensemble learning approach yields the highest classification accuracies of 98.69% and 94.83%, respectively, for the cases of subject-dependent and subject-independent problems. SN - 2040-2295 UR - https://doi.org/10.1155/2021/1970769 DO - 10.1155/2021/1970769 JF - Journal of Healthcare Engineering PB - Hindawi KW - ER -