EEG-Based Automated System for Reach-and-Grasp Identification Using Amplitude Envelope Enabled Multivariate Spectral Information

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 $72.03~\pm ~2.39$ %, $66.19~\pm ~8.96$ % and $83.31~\pm ~1.67$ % 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. Note to Practitioners—The multichannel recordings has wide applications in brain-computer interface and human-machine interaction. This paper suggests a novel foundation for the use of information preserved in amplitude envelope of multichannel EEG signals. We have demonstrated the idea in four steps: 1) variation of the proposed amplitude envelope based spectral entropy (ASE) for various modulation cases; 2) extension of ASE for multivariate data analysis and its possible application; 3) correntropy based significant channel selection to reduce the system complexity; and 4) potential use of amplitude envelope based neuronal information to develop an automated system for reach-and-grasp identification.