Akila V, Christaline J Anita, Edward A Shirly
Department of ECE, SRM Institute of Science and Technology, Vadapalani, Chennai 600026, India.
Diagnostics (Basel). 2024 May 13;14(10):1008. doi: 10.3390/diagnostics14101008.
Recent research in the field of cognitive motor action decoding focuses on data acquired from Functional Near-Infrared Spectroscopy (fNIRS) and its analysis. This research aims to classify two different motor activities, namely, mental drawing (MD) and spatial navigation (SN), using fNIRS data from non-motor baseline data and other motor activities. Accurate activity detection in non-stationary signals like fNIRS is challenging and requires complex feature descriptors. As a novel framework, a new feature generation by fusion of wavelet feature, Hilbert, symlet, and Hjorth parameters is proposed for improving the accuracy of the classification. This new fused feature has statistical descriptor elements, time-localization in the frequency domain, edge feature, texture features, and phase information to detect and locate the activity accurately. Three types of independent component analysis, including FastICA, Picard, and Infomax were implemented for preprocessing which removes noises and motion artifacts. Two independent binary classifiers are designed to handle the complexity of classification in which one is responsible for mental drawing (MD) detection and the other one is spatial navigation (SN). Four different types of algorithms including nearest neighbors (KNN), Linear Discriminant Analysis (LDA), light gradient-boosting machine (LGBM), and Extreme Gradient Boosting (XGBOOST) were implemented. It has been identified that the LGBM classifier gives high accuracies-98% for mental drawing and 97% for spatial navigation. Comparison with existing research proves that the proposed method gives the highest classification accuracies. Statistical validation of the proposed new feature generation by the Kruskal-Wallis H-test and Mann-Whitney U non-parametric test proves the reliability of the proposed mechanism.
认知运动动作解码领域的最新研究聚焦于从功能近红外光谱（fNIRS）获取的数据及其分析。本研究旨在利用来自非运动基线数据和其他运动活动的fNIRS数据，对两种不同的运动活动进行分类，即心理绘图（MD）和空间导航（SN）。在fNIRS这样的非平稳信号中进行准确的活动检测具有挑战性，需要复杂的特征描述符。作为一个新颖的框架，提出了一种通过融合小波特征、希尔伯特、symlet和 Hjorth 参数来生成新特征的方法，以提高分类的准确性。这种新的融合特征具有统计描述符元素、频域中的时间定位、边缘特征、纹理特征和相位信息，能够准确地检测和定位活动。为了去除噪声和运动伪影，实施了三种类型的独立成分分析，包括快速独立成分分析（FastICA）、皮卡德（Picard）和信息最大化（Infomax）算法。设计了两个独立的二元分类器来处理分类的复杂性，其中一个负责心理绘图（MD）检测，另一个负责空间导航（SN）。实施了四种不同类型的算法，包括最近邻（KNN）、线性判别分析（LDA）、轻梯度提升机（LGBM）和极端梯度提升（XGBOOST）。已经确定，LGBM分类器具有较高的准确率——心理绘图为98%，空间导航为97%。与现有研究的比较证明，所提出的方法具有最高的分类准确率。通过Kruskal-Wallis H检验和Mann-Whitney U非参数检验对所提出的新特征生成进行统计验证，证明了所提出机制的可靠性。
