Detection of reaching intention using EEG signals and nonlinear dynamic system identification. (July 2019)

• Record Type: Journal Article
• Authors: Mirzaee, Mitra Soltani; Moghimi, Sahar
• Is Part Of: Computer methods and programs in biomedicine. Volume 175(2019)
• Page Start: 151
• Page End: 161
• Publication Date: 2019-07
• Subjects: Laguerre expansion technique -- Low frequency EEG -- Movement related cortical potentials -- Nonlinear dynamic modeling -- Volterra kernels; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2019.04.023
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: We detected movement intention before execution of self-paced arm reaching movements. The proposed approach considered the nonlinear dynamics of the electrophysiological activity for detecting movement intention. Prediction was carried out point by point over the low-frequency EEG signals, without any feature extraction. The proposed approach did not break the data into trials or intervals, hence providing a feasible approach to be applicable in practice. Abstract: Background and objectives: Low frequency electroencephalography (EEG) signals are associated with preparation of movement and thus provide valuable information for brain-machine interface applications. The purpose of this study was to detect movement intention from EEG signals before execution of self-paced arm reaching movements. Methods: Ten healthy individuals were recruited. Movement onset was determined from surface electromyography recordings time-locked with EEG signals. Unlike previous studies, which employed feature extraction and classification for decoding, a nonlinear dynamic multiple-input/single output (MISO) model was developed. The MISO model consisted of a cascade of Volterra structures and a threshold block, generating the binary output corresponding to intention/no-intention. The modeling process included input selection from a pool of different EEG channels. The predictive performance of the model was evaluated using the receiver operating characteristics curve, from which the optimum threshold was also selected. The Mann–Whitney statistics was employed to select the significant EEG channels for the output by examining the statistical significance of improvement in the predictive capability of the model when the respective channels were included. Results: With the proposed approach, movement intention was detected approximately 500 ms before the movement onset and on average, with an accuracy of 96.37 ± 0.94%, a sensitivity of 77.93 ± 4.40% and a specificity of 98.52 ± 1.19%. Conclusions: The model output can be converted to motion commands for neuroprosthetic devices and exoskeletons in future applications.