We also show that the proposed artifact removal algorithm 33% improves the accuracy of force decoding in terms of R 2 value compared to standard CAR filters.īrain–machine interface (BMI) provides an alternative artificial route for transmitting the brain commands to the patient’s limbs with the goal of movement restoration in different neurological disorders such as stroke and spinal cord injury ( Hochberg et al., 2012 Collinger et al., 2013 Bouton et al., 2016). Weighted CAR method can effectively reconstruct the original signal with average R 2 higher than 0.5 for input SNRs higher than −10 dB in case of adding simulated outlier and motion artifacts. In the simulation study, the average R 2 between the original and reconstructed signal of all channels after applying the proposed artifact removal method was computed for input SNRs in the range of −45 to 0 dB. We evaluated the efficacy of the proposed artifact algorithm on both simulation and real data. The proposed spatial filter adaptively identifies contribution of the common noise on the channels employing Kalman filter method. We suggest a weighted common average referencing (CAR) algorithm in order to valid interpretation of the force decoding from different data types. Here we combine LFP and MUA information to improve decoding accuracy of the force signal from the multi-channel intracortical data of freely moving rats. Various types of the information including single unit activity (SUA), multiunit activity (MUA) and local field potential (LFP) can be used as an input information to extract motor commands for control of the external devices in BMI. Direct estimation of kinetic information such as the force from cortical data has the same importance as kinematic information to make a functional BMI system. Intracortical data recorded with multi-electrode arrays provide rich information about kinematic and kinetic states of movement in the brain–machine interface (BMI) systems. 2Kerman Neuroscience Research Center, Institute of Neuropharmacology, Kerman University of Medical Sciences, Kerman, Iran.1Neuroscience and Neuroengineering Research Lab, Department of Biomedical Engineering, School of Electrical Engineering, Iran University of Science and Technology (IUST), Tehran, Iran.Abed Khorasani 1,2, Vahid Shalchyan 1 and Mohammad Reza Daliri 1*
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