Philosophy

MSEL is committed to the product of sharing data and code in an effort to create reproducible research.

Projects


Sleep Staging

Automated Unsupervised Classifier to use iEEG data and score it into AWAKE, N2, N3 sleep stages.

  • A full description of how to use the classifier is in the help of Matlab m-file.
  • Also available - features extracted from two patients data in day/night recordings. These matrixes can be used as an input and to provide an example of how to use the classifier.

Kremen V, Brinkmann BH, Van Gompel JJ, Stead SMM, St Louis EK, Worrell GA. "Automated Unsupervised Behavioral State Classification using Intracranial Electrophysiology." J Neural Eng. 2018. Full Paper

  • OBJECTIVE:

    Automated behavioral state classification in intracranial EEG (iEEG) recordings may be beneficial for iEEG interpretation and quantifying sleep patterns to enable behavioral state dependent neuromodulation therapy in next generation implantable brain stimulation devices. Here, we introduce a fully automated unsupervised framework to differentiate between awake (AW), sleep (N2), and slow wave sleep (N3) using intracranial EEG (iEEG) only and validated with expert scored polysomnography.

  • APPROACH:

    Data from eight patients undergoing evaluation for epilepsy surgery (age 40+/-11, 3 female) with intracranial depth electrodes for iEEG monitoring were included. Spectral power features (0.1 - 235Hz) spanning several frequency bands from a single electrode were used to classify behavioral states of patients into AW, N2, and N3. RESULTS: Overall, classification accuracy of 94%, with 94% sensitivity and 93% specificity across eight subjects using multiple spectral power features from a single electrode was achieved. Classification performance of N3 sleep was significantly better (95%, sensitivity 95%, specificity 93%) than that of the N2 sleep phase (87%, sensitivity 78%, specificity 96%).

  • SIGNIFICANCE:

    Automated, unsupervised, and robust classification of behavioral states based on iEEG data is possible, and it is feasible to incorporate these algorithms into future implantable devices with limited computational power, memory, and number of electrodes for brain monitoring and stimulation.

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Artificial Intelligence

Varatharajah et. al., "Integrating Artificial Intelligence with Real-time Intracranial EEG Monitoring to Automate Interictal Identification of Seizure Onset Zones in Focal Epilepsy".

This opensource software performs interictal SOZ classification using iEEG data. A typical workflow comprises:

  1. feature extraction
  2. feature preprocessing
  3. classification
See Instructions file to get started.


High Frequency Oscillations

High Frequency Oscillations (HFOs) are brief discrete events seen in EEG that are promising biomarkers of both epileptic neural tissue and cognitive processing.

Cimbalnik et. al., "Physiological and Pathological High Frequency Oscillations in Focal Human Epilepsy", manuscript in review

Guragain et. al., "Spatial Patterns and Variation in High-Frequency Oscillation Rates and Amplitudes in Intracranial EEG Recordings", manuscript in review

Cimbalnik et. al., "The CS Algorithm: A Novel Method for High Frequency Oscillation Detection in EEG”, manuscript in review


Multiscale Electrophysiology Format

The Multiscale Electrophysiology File (MEF) Format is an open-source file format for storing electrophysiology and other time-series data employing data compression, and encryption.


Seizure Forecasting

Accurate seizure forecasting could transform epilepsy care, allowing patients to modify activities to avoid risk, or take additional AED to stop seizures before they develop. Our laboratory is engaged in efforts to develop and validate robust algorithms for seizure forecasting.

Kaggle.com seizure prediction competition: