Mobile QR Code QR CODE : The Transactions of the Korean Institute of Electrical Engineers
Title Neuronal Spike Train Decoding Methods for the Brain-Machine Interface Using Nonlinear Mapping
Authors 김경환(Kim, Kyunn-Hwan) ; 김성신(Kim, Sung-Shin) ; 김성준(Kim, Sung-June)
Page pp.468-474
ISSN 1975-8359
Keywords Brain-Machine Interface (BMI) ; Neural Signal ; Spike Train Decoding ; Support Vector Machine (SVM) ; Linear Filter
Abstract Brain-machine interface (BMI) based on neuronal spike trains is regarded as one of the most promising means to restore basic body functions of severely paralyzed patients. The spike train decoding algorithm, which extracts underlying information of neuronal signals, is essential for the BMI. Previous studies report that a linear filter is effective for this purpose and there is no noteworthy gain from the use of nonlinear mapping algorithms, in spite of the fact that neuronal encoding process is obviously nonlinear. We designed several decoding algorithms based on the linear filter, and two nonlinear mapping algorithms using multilayer perceptron (MLP) and support vector machine regression (SVR), and show that the nonlinear algorithms are superior in general. The MLP often showed unsatisfactory performance especially when it is carelessly trained. The nonlinear SVR showed the highest performance. This may be due to the superiority of the SVR in training and generalization. The advantage of using nonlinear algorithms were more profound for the cases when there are false-positive/negative errors in spike trains.