title: Neural decoding using a parallel sequential Monte Carlo method on point processes with ensemble effect.
abstract: Sequential Monte Carlo estimation on point processes has been successfully applied to predict the movement from neural activity. However, there exist some issues along with this method such as the simplified tuning model and the high computational complexity, which may degenerate the decoding performance of motor brain machine interfaces. In this paper, we adopt a general tuning model which takes recent ensemble activity into account. The goodness-of-fit analysis demonstrates that the proposed model can predict the neuronal response more accurately than the one only depending on kinematics. A new sequential Monte Carlo algorithm based on the proposed model is constructed. The algorithm can significantly reduce the root mean square error of decoding results, which decreases 23.6% in position estimation. In addition, we accelerate the decoding speed by implementing the proposed algorithm in a massive parallel manner on GPU. The results demonstrate that the spike trains can be decoded as point process in real time even with 8000 particles or 300 neurons, which is over 10 times faster than the serial implementation. The main contribution of our work is to enable the sequential Monte Carlo algorithm with point process observation to output the movement estimation much faster and more accurately.
authors.affiliation: Qiushi Academy for Advanced Studies, Zhejiang University, Hangzhou 310027, China ; Department of Biomedical Engineering, Zhejiang University, Hangzhou 310027, China.
authors.forename: Kai
authors.identifier: 0000-0001-5656-2246
authors.initials: K
authors.lastname: Xu
authors.affiliation: Qiushi Academy for Advanced Studies, Zhejiang University, Hangzhou 310027, China ; Key Laboratory of Biomedical Engineering of Ministry of Education, Zhejiang University, Hangzhou 310027, China.
authors.forename: Yiwen
authors.identifier: 0000-0002-1878-6182
authors.initials: Y
authors.lastname: Wang
authors.affiliation: Qiushi Academy for Advanced Studies, Zhejiang University, Hangzhou 310027, China ; Department of Biomedical Engineering, Zhejiang University, Hangzhou 310027, China.
authors.forename: Fang
authors.initials: F
authors.lastname: Wang
authors.affiliation: Qiushi Academy for Advanced Studies, Zhejiang University, Hangzhou 310027, China ; Department of Biomedical Engineering, Zhejiang University, Hangzhou 310027, China.
authors.forename: Yuxi
authors.initials: Y
authors.lastname: Liao
authors.affiliation: Qiushi Academy for Advanced Studies, Zhejiang University, Hangzhou 310027, China ; Department of Biomedical Engineering, Zhejiang University, Hangzhou 310027, China.
authors.forename: Qiaosheng
authors.identifier: 0000-0003-0485-3126
authors.initials: Q
authors.lastname: Zhang
authors.affiliation: Qiushi Academy for Advanced Studies, Zhejiang University, Hangzhou 310027, China ; Department of Biomedical Engineering, Zhejiang University, Hangzhou 310027, China.
authors.forename: Hongbao
authors.identifier: 0000-0002-2985-1007
authors.initials: H
authors.lastname: Li
authors.affiliation: Qiushi Academy for Advanced Studies, Zhejiang University, Hangzhou 310027, China ; Department of Biomedical Engineering, Zhejiang University, Hangzhou 310027, China ; Key Laboratory of Biomedical Engineering of Ministry of Education, Zhejiang University, Hangzhou 310027, China.
authors.forename: Xiaoxiang
authors.initials: X
authors.lastname: Zheng
mesh_terms: D000465:Algorithms; D000818:Animals; D001696:Biomechanical Phenomena; D006801:Humans; D008959:Models, Neurological; D008962:Models, Theoretical; D009010:Monte Carlo Method; D011597:Psychomotor Performance