The amplitude envelope is a crucial parameter to analyse natural systems as it provides useful amplitude modulation (AM) based information. In many cases, power spectral entropy (PSE) of a non-stationary signal is not able to discriminate AM based information. This paper proposes amplitude envelope based spectral entropy (ASE) which quantifies AM related information in the spectral domain and superiority is justified in comparison with PSE. Moreover, ASE is extended for multivariate signal analysis using joint AM based information. Efficacy of ASE is shown in various scenarios. Further, multivariate ASE is utilized for the development of a reach-and-grasp identification system using multichannel electroencephalogram (EEG) recordings. In this system, a novel correntropy based channel selection method is proposed to reduce system complexity. The number of EEG channels are reduced by almost 50% using the proposed channel selection method. The selected channels are decomposed into intrinsic mode functions (IMFs) using multivariate decomposition method. The AM based information present in these IMFs is obtained using multivariate ASE. Support vector machine classifier with radial basis function kernel is utilized to identify the type of grasp. Pearson correlation coefficient-based feature ranking is applied to select the significant features. The highest classification performance is achieved using five features with accuracy, sensitivity and specificity of 72.03 ± 2.39%, 66.19 ± 8.96% and 83.31 ± 1.67% respectively, which is better than compared method. The proposed reach-and-grasp identification method can be used to develop real time systems to avail natural control of neuroprosthetic devices.

One of the most crucial use of hands in daily life is grasping. Sometimes people with neuromuscular disorders become incapable of moving their hands. This article proposes a grasp motor imagery identification approach based on multivariate fast iterative filtering (MFIF). The proposed methodology involves the selection of relevant electroencephalogram (EEG) channels based on the neurophysiology of the brain. The selected EEG channels have been decomposed into five components using MFIF. Information potential based features are extracted from the decomposed EEG components. The extracted features are smoothed using a moving average filter. The smoothed features are classified using the k-nearest neighbors classifier. The cross-subject classification accuracy, precision, and F1-score of 98.25%, 98.31%, and 98.24%, respectively, is obtained. While the average classification accuracy, precision and F1-score for multiple subjects is 98.43%, 98.62%, and 98.41%, respectively. The proposed methodology can be used for the development of a low cost EEG based grasp identification system.