title: Monte Carlo point process estimation of electromyographic envelopes from motor cortical spikes for brain-machine interfaces.
abstract: OBJECTIVE
  Representation of movement in the motor cortex (M1) has been widely studied in brain-machine interfaces (BMIs). The electromyogram (EMG) has greater bandwidth than the conventional kinematic variables (such as position, velocity), and is functionally related to the discharge of cortical neurons. As the stochastic information of EMG is derived from the explicit spike time structure, point process (PP) methods will be a good solution for decoding EMG directly from neural spike trains. Previous studies usually assume linear or exponential tuning curves between neural firing and EMG, which may not be true.
  
  
  METHODS
  In our analysis, we estimate the tuning curves in a data-driven way and find both the traditional functional-excitatory and functional-inhibitory neurons, which are widely found across a rat's motor cortex. To accurately decode EMG envelopes from M1 neural spike trains, the Monte Carlo point process (MCPP) method is implemented based on such nonlinear tuning properties.
  
  
  RESULTS
  Better reconstruction of EMG signals is shown on baseline and extreme high peaks, as our method can better preserve the nonlinearity of the neural tuning during decoding. The MCPP improves the prediction accuracy (the normalized mean squared error) 57% and 66% on average compared with the adaptive point process filter using linear and exponential tuning curves respectively, for all 112 data segments across six rats. Compared to a Wiener filter using spike rates with an optimal window size of 50 ms, MCPP decoding EMG from a point process improves the normalized mean square error (NMSE) by 59% on average.
  
  
  CONCLUSIONS
  These results suggest that neural tuning is constantly changing during task execution and therefore, the use of spike timing methodologies and estimation of appropriate tuning curves needs to be undertaken for better EMG decoding in motor BMIs.
authors.affiliation: Qiushi Academy for Advanced Studies, Zhejiang University, Hangzhou 310027, People's Republic of China. Dept. of Biomedical Engineering, Zhejiang University, Hangzhou 310027, People's Republic of China.
authors.forename: Yuxi
authors.initials: Y
authors.lastname: Liao
authors.forename: Xiwei
authors.initials: X
authors.lastname: She
authors.forename: Yiwen
authors.initials: Y
authors.lastname: Wang
authors.forename: Shaomin
authors.initials: S
authors.lastname: Zhang
authors.forename: Qiaosheng
authors.initials: Q
authors.lastname: Zhang
authors.forename: Xiaoxiang
authors.initials: X
authors.lastname: Zheng
authors.forename: Jose C
authors.initials: JC
authors.lastname: Principe
mesh_terms: D000200:Action Potentials / Q000502:physiology*; D000818:Animals; D062207:Brain-Computer Interfaces; D004576:Electromyography / Q000379:methods*; D009010:Monte Carlo Method; D009044:Motor Cortex / Q000502:physiology*; D009068:Movement / Q000502:physiology*; D051381:Rats