TY - JOUR
T1 - Decoding the timing and target locations of saccadic eye movements from neuronal activity in macaque oculomotor areas
AU - Ohmae, Shogo
AU - Takahashi, Toshimitsu
AU - Lu, Xiaofeng
AU - Nishimori, Yasunori
AU - Kodaka, Yasushi
AU - Takashima, Ichiro
AU - Kitazawa, Shigeru
N1 - Publisher Copyright:
© 2015 IOP Publishing Ltd.
PY - 2015/6/1
Y1 - 2015/6/1
N2 - Objective. The control of movement timing has been a significant challenge for brain-machine interfaces (BMIs). As a first step toward developing a timing-based BMI, we aimed to decode movement timing and target locations in a visually guided saccadic eye movement task using the activity of neurons in the primate frontal eye field (FEF) and supplementary eye field (SEF). Approach. For this purpose, we developed a template-matching method that could recruit a variety of neurons in these areas. Main results. As a result, we were able to achieve a favorable estimation of saccade onset: for example, data from 20 randomly sampled FEF neurons or 40 SEF neurons achieved a median estimation error of ∼10 ms with an interquartile range less than 50 ms (± ∼25 ms). In the best case, seven simultaneously recorded SEF neurons using a multi-electrode array achieved a comparable accuracy (10 ± 30 ms). The method was significantly better than a heuristic method that used only a group of movement cells with sharp discharges at the onset of saccades. The estimation of target location was less accurate but still favorable, especially when we estimated target location at a timing of 200 ms after the onset of saccade: the method was able to discriminate 16 targets with an accuracy of 90%, which differed not only in their directions (eight directions) but also in amplitude (10/20°) when we used data from 61 randomly sampled FEF neurons. Significance. The results show that the timing, amplitude and direction of saccades can be decoded from neuronal activity in the FEF and SEF and further suggest that timing-based BMIs can be developed by decoding timing information using the template-matching method.
AB - Objective. The control of movement timing has been a significant challenge for brain-machine interfaces (BMIs). As a first step toward developing a timing-based BMI, we aimed to decode movement timing and target locations in a visually guided saccadic eye movement task using the activity of neurons in the primate frontal eye field (FEF) and supplementary eye field (SEF). Approach. For this purpose, we developed a template-matching method that could recruit a variety of neurons in these areas. Main results. As a result, we were able to achieve a favorable estimation of saccade onset: for example, data from 20 randomly sampled FEF neurons or 40 SEF neurons achieved a median estimation error of ∼10 ms with an interquartile range less than 50 ms (± ∼25 ms). In the best case, seven simultaneously recorded SEF neurons using a multi-electrode array achieved a comparable accuracy (10 ± 30 ms). The method was significantly better than a heuristic method that used only a group of movement cells with sharp discharges at the onset of saccades. The estimation of target location was less accurate but still favorable, especially when we estimated target location at a timing of 200 ms after the onset of saccade: the method was able to discriminate 16 targets with an accuracy of 90%, which differed not only in their directions (eight directions) but also in amplitude (10/20°) when we used data from 61 randomly sampled FEF neurons. Significance. The results show that the timing, amplitude and direction of saccades can be decoded from neuronal activity in the FEF and SEF and further suggest that timing-based BMIs can be developed by decoding timing information using the template-matching method.
KW - brain-machine interface
KW - frontal eye field
KW - monkey
KW - supplementary eye field
KW - timing based BMI
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U2 - 10.1088/1741-2560/12/3/036014
DO - 10.1088/1741-2560/12/3/036014
M3 - Article
C2 - 25973676
AN - SCOPUS:84930226325
SN - 1741-2560
VL - 12
JO - Journal of neural engineering
JF - Journal of neural engineering
IS - 3
M1 - 036014
ER -