Systems and methods for identifying biological structures associated with neuromuscular source signals

A method, comprising:

• using at least computer hardware processor to perform:
• applying a source separation technique to the time series of neuromuscular signals recorded by a plurality of neuromuscular sensors to obtain a plurality of neuromuscular source signals and corresponding mixing information;
• providing features, obtained from the plurality of neuromuscular source signals and/or the corresponding mixing information, as input to a trained statistical classifier and obtaining corresponding output;
• and
• identifying, based on the output of the trained statistical classifier, and for each of one or more of the plurality of neuromuscular source signals, an associated set of one or more biological structures.

At least one non-transitory computer-readable storage medium storing processor-executable instructions that, when executed by the at least one computer hardware processor, cause the at least one computer hardware processor to perform:

• applying a source separation technique to the time series of neuromuscular signals recorded by a plurality of neuromuscular sensors to obtain a plurality of neuromuscular source signals and corresponding mixing information;
• providing features, obtained from the plurality of neuromuscular source signals and/or the corresponding mixing information, as input to a trained statistical classifier and obtaining corresponding output;
• and
• identifying, based on the output of the trained statistical classifier, and for each of one or more of the plurality of neuromuscular source signals, an associated set of one or more biological structures.