Systems and methods for detection of neurophysiological signal oscillations

1 . A A computer-implemented method for the identification of oscillations within electromagnetic (EM) activity of a brain of a patient, the method implemented on a computing device comprising at least one processor in communication with at least one non-transitory computer readable medium, the method comprising:

a. receiving, at the computing device, a plurality of EM measurements indicative of EM activity of the brain and corresponding times;

b. transforming, using the computing device, the plurality of EM measurements into a three-dimensional time-frequency space;

c. removing aperiodic activity to perform a first filtering on the plurality of EM measurements in the three-dimensional time-frequency space using the computing device, wherein the time-frequency space includes a plurality of frequency points, wherein the first filtering removes EM measurements of the plurality of EM measurements containing pink noise to generate a flattened three-dimensional time-frequency space by providing thresholds within the time-frequency space to identify periodic signals for each frequency point of the plurality of frequency points;

d. constructing, using the computing device, a first plurality of bounding boxes, each bounding box of the first plurality of bounding boxes enclosing a contiguous group of the EM measurements of the plurality of EM measurements to capture onset and offset times of candidate oscillations within the flattened three-dimensional time-frequency space, wherein the contiguous group of the EM measurements of the plurality of EM measurements are detected where spectral power is above the thresholds within the time-frequency space;

e. performing a second filtering, using the computing device, on the first plurality of bounding boxes to reject bounding boxes of the plurality of bounding boxes enclosing less than two candidate oscillation cycles to generate a second plurality of bounding boxes, wherein candidate oscillations are detected based on detecting harmonic peaks in a corresponding bounding box of the plurality of bounding boxes;

f. performing, using the computing device, an autocorrelation on the EM measurements of the plurality of EM measurements within each bounding box of the second plurality of bounding boxes to identify a corresponding frequency for each corresponding bounding box of the second plurality of bounding boxes by analyzing intervals of peaks and troughs of the candidate oscillations in the corresponding bounding box to identify the corresponding frequency for the corresponding bounding box;

g. performing a third filtering, using the computing device, on the second plurality of bounding boxes to retain only the bounding boxes of the second plurality of bounding boxes containing candidate oscillations with corresponding frequencies equal to a center frequency of the candidate oscillations to generate a third plurality of bounding boxes, wherein the center frequency is calculated as a center of frequencies of a plurality of EM signals in the corresponding bounding box;

h. displaying, using the computing device, the candidate oscillations in the third plurality of bounding boxes to a user; and

i. providing, based on the candidate oscillations, at least one of (1) targeted neurofeedback to the patient, (2) phase-locked neuromodulation to the patient, or (3) therapeutic treatment to the patient.

2 . The computer-implemented method of claim 1 , further comprising determining for each candidate oscillation the onset time, the offset time, the center frequency, a frequency range, a number of cycles, and a degree of asymmetry.

3 . The computer-implemented method of claim 1 , wherein the candidate oscillations include rhythmic, repeating patterns of neural activity including at least one of delta, theta, alpha, beta, and low gamma band oscillations.

4 . The computer-implemented method of claim 1 , wherein the second plurality of bounding boxes with at least two candidate oscillation cycles represent event-related potentials and evoked responses from a group of neurons by an external stimulus or event.

5 . The computer-implemented method of claim 1 , further comprising grouping the candidate oscillations by frequency range to generate a normalized power map.

6 . The computer-implemented method of claim 1 , further comprising incorporating at least one of a brain-computer interface and a neurofeedback system.

7 . The computer-implemented method of claim 1 , wherein the plurality of EM measurements include at least one of electroencephalography (EEG) signals, magnetoencephalography (MEG) signals, electrocorticography (ECOG) signals, stereo EEG (SEEG) signals, single neuronal recordings, and local field potentials (LFP).

8 . A system for identifying oscillations within electromagnetic (EM) activity of a brain of a patient, the system comprising at least one processor in communication with at least one memory device, wherein the at least one processor is configured to:

a. receive a plurality of EM measurements indicative of EM activity of the brain and corresponding times;

b. transform the plurality of EM measurements into a three-dimensional time-frequency space;

c. remove aperiodic activity to perform a first filtering on the plurality of EM measurements in the three-dimensional time-frequency space, wherein the time-frequency space includes a plurality of frequency points, wherein the first filtering remove EM measurements of the plurality of EM measurements containing pink noise to generate a three-dimensional flattened time-frequency space by providing thresholds within the time-frequency space to identify periodic signals for each frequency point of the plurality of frequency points;

d. construct a first plurality of bounding boxes, each bounding box of the first plurality of bounding boxes enclosing a contiguous group of the EM measurements of the plurality of EM measurements to capture onset and offset times of candidate oscillations within the flattened three-dimensional time-frequency space, wherein the contiguous group of the EM measurements of the plurality of EM measurements are detected where spectral power is above the thresholds within the time-frequency space;

e. perform a second filtering on the first plurality of bounding boxes to reject bounding boxes of the first plurality of bounding boxes enclosing less than two candidate oscillation cycles to generate a second plurality of bounding boxes, wherein candidate oscillations are detected based on detecting harmonic peaks in a corresponding bounding box of the plurality of bounding boxes;

f. perform an autocorrelation on the EM measurements of the plurality of EM measurements within each bounding box of the second plurality of bounding boxes to identify a corresponding frequency for each corresponding bounding box of the second plurality of bounding boxes by analyzing intervals of peaks and troughs of the candidate oscillations in the corresponding bounding box to identify the corresponding frequency for the corresponding bounding box;

g. perform a third filtering of the second plurality of bounding boxes to retain only the bounding boxes of the second plurality of bounding boxes containing candidate oscillations with corresponding frequencies equal to a center frequency of the candidate oscillations to generate a third plurality of bounding boxes, wherein the center frequency is calculated as a center of frequencies of a plurality of EM signals in the corresponding bounding box;

h. communicate the candidate oscillations in the third plurality of bounding boxes to a user; and

i. provide, based on the candidate oscillations, at least one of (1) targeted neurofeedback to the patient, (2) phase-locked neuromodulation to the patient, or (3) therapeutic treatment to the patient.

9 . The system of claim 8 , wherein the at least one processor is further configured to determine for each candidate oscillation the onset time, the offset time, the center frequency, a frequency range, a number of cycles, and a degree of asymmetry.

10 . The system of claim 8 , wherein the candidate oscillations include rhythmic, repeating patterns of neural activity including at least one of delta, theta, alpha, beta, and low gamma band oscillations.

11 . The system of claim 8 , wherein the second plurality of bounding boxes with at least two candidate oscillation cycles represent event-related potentials and evoked responses from a group of neurons by an external stimulus or event.

12 . The system of claim 8 , wherein the at least one processor is further configured to group candidate oscillations by frequency range to generate a normalized power map.

13 . The system of claim 8 , wherein the at least one processor is further configured to incorporate at least one of a brain-computer interface and a neurofeedback system.

14 . The system of claim 8 , wherein the plurality of EM measurements include at least one of electroencephalography (EEG) signals, magnetoencephalography (MEG) signals, electrocorticography (ECoG) signals, stereo EEG (sEEG) signals, single neuronal recordings, and local field potentials (LFP).

15 . The system of claim 8 , wherein the at least one processor is further configured to use the candidate oscillations in the third plurality of bounding boxes to provide targeted feedback on a magnitude of a user's alpha oscillation to improve attention and improve task performance.

16 . The system of claim 8 , wherein the at least one processor is further configured to adjust a phase of electrical stimulation based on the candidate oscillations in the third plurality of bounding boxes.

17 . At least one non-transitory computer-readable media having computer-executable instructions embodied thereon, when executed by a computing device including at least one processor in communication with at least one memory device, the computer-executable instructions cause the at least one processor to:

a. receive a plurality of EM measurements indicative of EM activity of a brain of a patient and corresponding times;

b. transform the plurality of EM measurements into a three-dimensional time-frequency space;

c. remove aperiodic activity to perform a first filtering on the plurality of EM measurements in the three-dimensional time-frequency space, wherein the time-frequency space includes a plurality of frequency points, wherein the first filtering remove EM measurements of the plurality of EM measurements containing pink noise to generate a three-dimensional flattened time-frequency space by providing thresholds within the time-frequency space to identify periodic signals for each frequency point of the plurality of frequency points;

d. construct a first plurality of bounding boxes, each bounding box of the first plurality of bounding boxes enclosing a contiguous group of the EM measurements of the plurality of EM measurements to capture onset and offset times of candidate oscillations within the flattened three-dimensional time-frequency space, wherein the contiguous group of the EM measurements of the plurality of EM measurements are detected where spectral power is above the thresholds within the time-frequency space;

e. perform a second filtering on the first plurality of bounding boxes to reject bounding boxes of the first plurality of bounding boxes enclosing less than two candidate oscillation cycles to generate a second plurality of bounding boxes, wherein candidate oscillations are detected based on detecting harmonic peaks in a corresponding bounding box of the plurality of bounding boxes;

f. perform an autocorrelation on the EM measurements of the plurality of EM measurements within each bounding box of the second plurality of bounding boxes to identify a corresponding frequency for each corresponding bounding box of the second plurality of bounding boxes by analyzing intervals of peaks and troughs of the candidate oscillations in the corresponding bounding box to identify the corresponding frequency for the corresponding bounding box;

g. perform a third filtering of the second plurality of bounding boxes to retain only the bounding boxes of the second plurality of bounding boxes containing candidate oscillations with corresponding frequencies equal to a center frequency of the candidate oscillations to generate a third plurality of bounding boxes, wherein the center frequency is calculated as a center of frequencies of a plurality of EM signals in the corresponding bounding box;

h. communicate the candidate oscillations in the third plurality of bounding boxes to a user; and

i. provide, based on the candidate oscillations, at least one of (1) targeted neurofeedback to the patient, (2) phase-locked neuromodulation to the patient, or (3) therapeutic treatment to the patient.

18 . The at least one non-transitory computer-readable media of claim 17 , wherein the computer-executable instructions cause the at least one processor to determine for each candidate oscillation the onset time, the offset time, the center frequency, a frequency range, a number of cycles, and a degree of asymmetry.

19 . The at least one non-transitory computer-readable media of claim 17 , wherein the candidate oscillations include rhythmic, repeating patterns of neural activity including at least one of delta, theta, alpha, beta, and low gamma band oscillations.

20 . The at least one non-transitory computer-readable media of claim 17 , wherein the second plurality of bounding boxes with at least two candidate oscillation cycles represent event-related potentials and evoked responses from a group of neurons by an external stimulus or event.