S-Space College of Engineering/Engineering Practice School (공과대학/대학원) Dept. of Electrical and Computer Engineering (전기·정보공학부) Journal Papers (저널논문_전기·정보공학부)
Improvement of Spike Train Decoder under Spike Detection and Classification Errors Using Support Vector Machine
Cited 4 time in Web of Science Cited 4 time in Scopus
- Issue Date
- Springer Verlag
- Med Biol Eng Comput 44:124-130
- Brain–machine interface ; Spike train decoding ; Nonlinear mapping ; Support vector machine
- The successful decoding of kinematic variables
from spike trains of motor cortical neurons is essential for
cortical neural prosthesis. Spike trains from each single
unit must be extracted from extracellular neural signals
and, thus, spike detection and sorting procedure is
indispensable but the detection and sorting may involve
considerable error. Thus, a decoding algorithm should be
robust with respect to spike train errors. Here, we show
that spike train decoding algorithms employing nonlinear
mapping, especially a support vector machine (SVM),
may be more advantageous contrary to previous results
which showed that an optimal linear filter is sufficient.
The advantage became more conspicuous in the case of
erroneous spike trains. Using the SVM, satisfactory
training of the decoder could be achieved much more
easily, compared to the case of using a multilayer perceptron,
which has been employed in previous studies.
Tests were performed on simulated spike trains from
primary motor cortical neurons with a realistic distribution
of preferred direction. The results suggest the possibility
that a neuroprosthetic device with a low-quality
spike sorting preprocessor can be achieved by adopting a
spike train decoder that is robust to spike sorting errors.
- 0140-0118 (print)
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