GMMPLS: A Novel Automatic State-Based Algorithm for Continuous Decoding in BMIs

Abstract

In this paper, a novel fully-automated state-based decoding method has been proposed for continuous decoding problems in brain-machine interface (BMI) systems, called Gaussian mixture of model (GMM)-assisted PLS (GMMPLS). In contrast to other state-based and hierarchical decoders, the proposed method does not demand any prior information about the desired output structure. Instead, GMMPLS uses the GMM algorithm to divide the desired output into a specific number of states (clusters). Next, a logistic regression model is trained to predict the probability membership of each time sample for each state. Finally, using the concept of the partial least square (PLS) algorithm, GMMPLS constructs a model for decoding the desired output using the input data and the achieved membership probabilities. The performance of the GMMPLS has been evaluated and compared to PLS, the nonlinear quadratic PLS (QPLS), and the bayesian PLS (BPLS) methods through a simulated dataset and two different real-world BMI datasets. The achieved results demonstrated that the GMMPLS significantly outperformed PLS, QPLS, and BPLS overall datasets. INDEX TERMSBrain-machine interfaces (BMI), Partial least square (PLS), State-based decoding, Continuous decoding, Gaussian mixture of model (GMM).

Consistent spectro-spatial features of human ECoG successfully decode naturalistic behavioral states

“…Motor control has been studied extensively ( Miller et al, 2007 , 2014 ). Algorithms have been developed to accurately translate hand movements and gestures, finger flexion ( Aoki et al, 1999 ; Zanos et al, 2008 ; Foodeh et al, 2021 ; Mirfathollahi et al, 2022 , 2023 ), and walking ( Tortora et al, 2020 ). Speech prostheses have also been developed to decode word and sentence representations directly from the temporal cortex ( Kellis et al, 2010 ; Bouchard et al, 2013 ; Angrick et al, 2019 ; Anumanchipalli et al, 2019 ; Moses et al, 2021 ; Willett et al, 2023 ).…”