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

1. A system, comprising:

a plurality of neuromuscular sensors, each of which is configured to record a time-series of neuromuscular signals from a surface of a user's body;

at least one computer hardware processor; and

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 the 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.

2. The system of claim 1 ,

wherein the plurality of neuromuscular source signals includes a first neuromuscular source signal and a second neuromuscular source signal,

wherein the identifying comprises identifying a first set of one or more biological structures associated with the first neuromuscular source signal and a second set of one or more biological structures associated with the second neuromuscular source signal, and

wherein the first set of biological structures is different from the second set of biological structures.

3. The system of claim 2 , wherein the first set of one or more biological structures includes at least one extensor muscle and wherein the second set of one or more biological structures includes at least one flexor muscle.

4. The system of claim 2 , wherein the first set of biological structures includes at least one muscle, at least one tendon, and/or at least one motor unit.

5. The system of claim 2 , wherein the processor-executable instructions further cause the at least one computer hardware processor to perform:

providing at least some of the plurality of neuromuscular source signals as input to a trained statistical model different from the trained statistical classifier, the trained statistical model having at least a first input associated with the first set of one or more biological structures and second input associated with the second set of one or more biological structures, the providing comprising:

providing the first neuromuscular source signal or data derived from the first neuromuscular source signal to the first input of the trained statistical model; and

providing the second neuromuscular source signal or data derived from the second neuromuscular source signal to the second input of the trained statistical model; and

controlling at least one device based, at least in part, on output of the trained statistical model.

6. The system of claim 5 , wherein controlling of the at least one device comprises: predicting, based on an output of the trained statistical model, whether an onset

of a motor action will occur within a threshold amount of time; and

when it is predicted that the onset of the motor action will occur within the threshold amount of time, sending a control signal to the at least one device prior to completion of the motor action by the user.

7. The system of claim 5 , wherein the trained statistical model is a recurrent neural network.

8. The system of claim 1 , wherein the plurality of neuromuscular sensors are arranged on a wearable device configured to be worn on or around a body part of the user.

9. The system of claim 1 , wherein the plurality of neuromuscular sensors comprises sensors selected from the group consisting of electromyography (EMG) sensors, mechanomyography (MMG) sensors, and sonomyography (SMG) sensors.

10. The system of claim 1 , wherein applying the source separation technique to the time-series of neuromuscular signals recorded by the plurality of neuromuscular sensors comprises applying independent components analysis (ICA) to the time-series of neuromuscular signals recorded by the plurality of neuromuscular sensors.

11. The system of claim 1 , wherein applying the source separation technique to the time-series of neuromuscular signals recorded by the plurality of neuromuscular sensors comprises applying non-negative matrix factorization (NNMF) to the time-series of neuromuscular signals recorded by the plurality of neuromuscular sensors.

12. The system of claim 1 , wherein the providing comprises:

providing at least some of the corresponding mixing information or information derived from the corresponding mixing information as input to the trained statistical classifier.

13. The system of claim 1 , wherein the providing comprises:

providing at least some of the plurality of neuromuscular source signals or information derived from the plurality of neuromuscular source signals as input to the trained statistical classifier.

14. The system of claim 1 , wherein the processor-executable instructions further cause the at least one hardware processor to perform:

updating or retraining the trained statistical classifier at least in part by using information obtained from the time-series of neuromuscular signals recorded by the plurality of neuromuscular sensors.

15. The system of claim 1 , wherein the processor-executable instructions further cause the at least one hardware processor to perform:

generating the trained statistical classifier using an supervised learning technique.

16. The system of claim 1 , wherein the identifying comprises:

assigning a plurality of labels to the plurality of neuromuscular signals, wherein different labels in the plurality of labels indicate that different neuromuscular signals correspond to different sets of biological structures.

17. The system of claim 1 , wherein the identifying comprises:

assigning a plurality of labels to the plurality of neuromuscular signals, wherein a first label in the plurality of labels identifies a first set of biological structures.

18. 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.

19. 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.