A method for training a neuron network using a processor in communication with a memory includes determining features of a signal using the neuron network, determining an uncertainty measure of the features for classifying the signal, reconstructing the signal from the features using a decoder neuron network to produce a reconstructed signal, comparing the reconstructed signal with the signal to produce a reconstruction error, combining the uncertainty measure with the reconstruction error to produce a rank of the signal for a necessity of a manual labeling, labeling the signal according to the rank to produce the labeled signal; and training the neuron network and the decoder neuron network using the labeled signal.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for training a neuron network using a processor in communication with a memory, comprising: determining features of a signal using the neuron network; determining an uncertainty measure of the features for classifying the signal; reconstructing the signal from the features using a decoder neuron network to produce a reconstructed signal; comparing the reconstructed signal with the signal to produce a reconstruction error; combining the uncertainty measure with the reconstruction error to produce a rank of the signal for a necessity of a manual labeling; labeling the signal according to the rank to produce the labeled signal; and training the neuron network and the decoder neuron network using the labeled signal. 2. The method of claim 1, wherein the labeling comprises: transmitting a labeling request to an annotation device if the rank indicates the necessity of the manual labeling process. 3. The method of claim 1, wherein the determining features are performed by using an encoder neural network. 4. The method of claim 1, wherein the signal is an electroencephalogram (EEG) or an electrocardiogram (ECG). 5. The method of claim 1, wherein the reconstruction error is defined based on a Euclidean distance between the signal and the reconstructed signal. 6. The method of claim 1, wherein the rank is defined based on an addition of an entropy function and the reconstruction error. 7. An active learning system comprising: a human machine interface; a storage device including neural networks; a memory; a network interface controller connectable with a network being outside the system; an imaging interface connectable with an imaging device; and a processor configured to connect to the human machine interface, the storage device, the memory, the network interface controller and the imaging interface, wherein the processor executes instructions for classifying a signal using the neural networks stored in the storage device, wherein the neural networks perform steps of: determining features of the signal using the neuron network; determining an uncertainty measure of the features for classifying the signal; reconstructing the signal from the features using a decoder neuron network to produce a reconstructed signal; comparing the reconstructed signal with the signal to produce a reconstruction error; combining the uncertainty measure with the reconstruction error to produce a rank of the signal for a necessity of a manual labeling; labeling the signal according to the rank to produce the labeled signal; and training the neuron network and the decoder neuron network using the labeled signal. 8. The method of claim 7, wherein the labeling comprises: transmitting a labeling request to an annotation device if the rank indicates the necessity of the manual labeling process. 9. The method of claim 7, wherein the determining features are performed by using an encoder neural network. 10. The method of claim 7, wherein the signal is an electroencephalogram (EEG) or an electrocardiogram (ECG). 11. The method of claim 7, wherein the reconstruction error is defined based on a Euclidean distance between the signal and the reconstructed signal. 12. The method of claim 7, wherein the rank is defined based on an addition of an entropy function and the reconstruction error. 13. A non-transitory computer-readable medium storing software comprising instructions executable by one or more computers which, upon such execution, cause the one or more computers to perform operations comprising: determining features of a signal using the neuron network; determining an uncertainty measure of the features for classifying the signal; reconstructing the signal from the features using a decoder neuron network to produce a reconstructed signal; comparing the reconstructed signal with the signal to produce a reconstruction error; combining the uncertainty measure with the reconstruction error to produce a rank of the signal for a necessity of a manual labeling; labeling the signal according to the rank to produce the labeled signal; and training the neuron network and the decoder neuron network using the labeled signal. 14. The method of claim 13, wherein the labeling comprises: transmitting a labeling request to an annotation device if the rank indicates the necessity of the manual labeling process. 15. The method of claim 13, wherein the determining features are performed by using an encoder neural network. 16. The method of claim 13, wherein the signal is an electroencephalogram (EEG) or an electrocardiogram (ECG). 17. The method of claim 13, wherein the reconstruction error is defined based on a Euclidean distance between the signal and the reconstructed signal. 18. The method of claim 13, wherein the rank is defined based on an addition of an entropy function and the reconstruction error.