DECODING GRATING ORIENTATION FROM MICROELECTRODE ARRAY RECORDINGS IN MONKEY CORTICAL AREA V4

We propose an invasive brain-machine interface (BMI) that decodes the orientation of a visual grating from spike train recordings made with a 96 microelectrodes array chronically implanted into the prelunate gyrus (area V4) of a rhesus monkey. The orientation is decoded irrespective of the grating&#...

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Bibliographic Details
Published in:International journal of neural systems 2010-04, Vol.20 (2), p.95-108
Main Authors: MANYAKOV, NIKOLAY V., VAN HULLE, MARC M.
Format: Article
Language:English
Subjects:
Action Potentials - physiology
Algorithms
Animals
Brain Mapping
Discriminant Analysis
Macaca mulatta
Microelectrodes
Models, Neurological
Orientation - physiology
Pattern Recognition, Automated - methods
Photic Stimulation - methods
Principal Component Analysis
Pyramidal Cells - physiology
User-Computer Interface
Visual Cortex - cytology
Visual Cortex - physiology
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Summary:We propose an invasive brain-machine interface (BMI) that decodes the orientation of a visual grating from spike train recordings made with a 96 microelectrodes array chronically implanted into the prelunate gyrus (area V4) of a rhesus monkey. The orientation is decoded irrespective of the grating's spatial frequency. Since pyramidal cells are less prominent in visual areas, compared to (pre)motor areas, the recordings contain spikes with smaller amplitudes, compared to the noise level. Hence, rather than performing spike decoding, feature selection algorithms are applied to extract the required information for the decoder. Two types of feature selection procedures are compared, filter and wrapper. The wrapper is combined with a linear discriminant analysis classifier, and the filter is followed by a radial-basis function support vector machine classifier. In addition, since we have a multiclass classification problen, different methods for combining pairwise classifiers are compared.
ISSN:0129-0657
1793-6462
DOI:10.1142/S0129065710002280