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Abstract
Manual and semi-automatic identification of artifacts and unwanted physiological signals in large intracerebral electroencephalographic (iEEG) recordings is time consuming and inaccurate. To date, unsupervised methods to accurately detect iEEG artifacts are not available. This study introduces a novel machine-learning approach for detection of artifacts in iEEG signals in clinically controlled conditions using convolutional neural networks (CNN) and benchmarks the method’s performance against expert annotations. The method was trained and tested on data obtained from St Anne’s University Hospital (Brno, Czech Republic) and validated on data from Mayo Clinic (Rochester, Minnesota, U.S.A). We show that the proposed technique can be used as a generalized model for iEEG artifact detection. Moreover, a transfer learning process might be used for retraining of the generalized version to form a data-specific model. The generalized model can be efficiently retrained for use with different EEG acquisition systems and noise environments. The generalized and specialized model F1 scores on the testing dataset were 0.81 and 0.96, respectively. The CNN model provides faster, more objective, and more reproducible iEEG artifact detection compared to manual approaches.
Highlights
EEG artifacts and undesired signals can be generated by biological phenomena, acquisition instrumentation, or external sources
We showed that a generalized convolutional neural networks (CNN) model can be used for intracerebral EEG (iEEG) classification with data acquired by different acquisition systems with different parameters of measurements
CNNs require large datasets for training, and this can be a significant limitation if the training data requires time consuming annotation of the primary data, as in the case here of iEEG
Summary
EEG artifacts and undesired signals can be generated by biological phenomena (eye blinks, head movement, muscle activity, cardiac signals), acquisition instrumentation (signal discontinuities, transient filter effects), or external sources (electromagnetic inductive noise). Automated detection and removal of scalp EEG artifacts have been widely explored. IEEG recordings were assumed to be largely, immune to eye movement and muscle artifacts. This assumption has more recently been proven incorrect (Ball et al 2009; Jerbi et al 2009; Kovach et al 2011) and there is a generally recognized need for automated, unbiased, methods to remove iEEG artifacts (Hu et al 2007), in wide-
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