| CPC G06N 3/084 (2013.01) [G06F 18/213 (2023.01); G06F 18/23 (2023.01); G06N 3/08 (2013.01)] | 7 Claims |
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5. A method for distinguishing neural waveforms, the method comprising:
providing a processor including a central processing unit capable of executing a computer program, a display, and a memory connected to the processor, wherein the memory stores program instructions for providing a multi-dimensional grouping of clustered feature ensembles, including instructions for
obtaining a plurality of neural waveforms in a pre-designated manner from neural signals sensed by way of at least one electrode;
obtaining a plurality of gradient waveforms by calculating pointwise slopes in each of the plurality of neural waveforms;
obtaining a plurality of codes using an encoder ensemble, the encoder ensemble composed of a plurality of encoders having a previously learned pattern estimation method and each having different numbers of hidden layers, the plurality of codes obtained as a plurality of features extracted by the plurality of encoders from the same plurality of gradient waveforms;
extracting a feature ensemble for each of the plurality of gradient waveforms by concatenating the plurality of codes extracted by the plurality of encoders, respectively;
distinguishing the plurality of neural waveforms corresponding respectively to the plurality of gradient waveforms by clustering a plurality of feature ensembles extracted respectively in correspondence to the plurality of gradient waveforms according to a pre-designated clustering technique; and
displaying the clustered plurality of feature ensembles that distinguish the plurality of neural waveforms on a display,
wherein the encoder ensemble includes a plurality of decoders that correspond respectively to the plurality of encoders, each of the decoders configured to receive a code extracted from the gradient waveforms by a corresponding encoder and to recover the gradient waveforms inputted to the corresponding encoder according to a learned pattern recovery method, and
wherein learning by the plurality of encoders is performed as an error, calculated from differences between the gradient waveforms and recovered waveforms recovered by the decoders, that is back-propagated by way of the decoders,
wherein the different number of hidden layers is set for each of the plurality of encoders so that each of the plurality of encoders outputs different features for the same plurality of gradient waveforms,
wherein the same plurality of gradient waveforms is input of each of the plurality of encoders.
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