ICLabel is an important plug-in function in EEGLAB, the most widely used software for EEG data processing. A powerful approach to automated processing of EEG data involves decomposing the data by Independent Component Analysis (ICA) and then classifying the resulting independent components (ICs) using ICLabel. While EEGLAB pipelines support high-performance computing (HPC) platforms running the open-source Octave interpreter, the ICLabel plug-in is incompatible with Octave because of its specialized neural network architecture. To enhance cross-platform compatibility, we developed a Python version of ICLabel that uses standard EEGLAB data structures. We compared ICLabel MATLAB and Python implementations to data from 14 subjects. ICLabel returns the likelihood of classification in 7 classes of components for each ICA component. The returned IC classifications were virtually identical between Python and MATLAB, with differences in classification percentage below 0.001%.

The rise of open-source neuroimaging data sharing has created new opportunities for advancing machine learning in neuroscience. However, this trend also highlights the need for a framework capable of leveraging these large-scale, publicly available datasets for advanced machine learning algorithms, particularly deep learning techniques. In this paper, we introduce EEG-SSL, a framework designed to work with large heterogenous EEG neuroimaging datasets shared in the standardized Brain Imaging Data Structure (BIDS) format using Self-Supervised Learning (SSL). SSL is a powerful deep-learning approach that benefits from large-scale datasets, even when unlabeled or minimally labeled. We show how information can be extracted from standardized EEG BIDS-formated data and discuss different considerations while processing at scale in our framework. We specify the software components that are needed to apply SSL to EEG and provide several standard implementations for each of the components. We also describe how each component can be extended with custom implementation while leveraging the supporting functions of the framework. We applied the framework to a large-scale BIDS-compliant dataset (ds004186) comprising resting-state EEG data from over 2,000 subjects, showcasing how SSL can be structured and deployed for potential big data in EEG.

Deep learning models are capable of extracting task-relevant information in complex brain dynamics from large corpora of raw EEG data. Given the small size of EEG datasets and the heterogeneity of each dataset’s channel montage, aggregating EEG datasets for large-scale training of deep learning models often requires a way to harmonize different channel locations effectively. Previous methods have focused on extracting features from raw EEG and projecting them onto a common space. However, these approaches underexploit the potential richness of EEG raw data. Here, we proposed a method to train deep learning models on raw EEG data with different channel montages using spatial attention on electrode coordinates. We test this approach on a gender classification task. We first show that increasing channel montage density increases performance. We then show that spatial attention increases model performance for different channel montage densities. Then, we show that in nonuniform montage settings, deep learning models trained on combined data with different channel montages performs significantly better than deep learning models trained on individual datasets with fixed montages. This work opens up the potential for future application of deep learning models on datasets collected by different labs across different recording settings.

Convolutional Neural Networks (CNNs) have achieved impressive performance on many computer vision-related tasks, such as object detection, image recognition, image retrieval, etc. These achievements benefit from the CNNs’ outstanding capability to learn discriminative features with deep layers of neuron structures and iterative training processes. This has inspired the EEG research community to adopt CNN in performing EEG classification tasks. However, CNNs learned features are not immediately interpretable, causing a lack of understanding of the CNNs’ internal working mechanism. To improve CNN interpretability, CNN visualization methods are applied to translate the internal features into visually perceptible patterns for qualitative analysis of CNN layers. Many CNN visualization methods have been proposed in the Computer Vision literature to interpret the CNN network structure, operation, and semantic concept, yet applications to EEG data analysis have been limited. In this work we use 3 different methods to extract EEG-relevant features from a CNN trained on raw EEG data: optimal samples for each classification category, activation maximization, and reverse convolution. We applied these methods to a high-performing Deep Learning model with state-of-the-art performance for an EEG sex classification task, and show that the model exploits differences between classes in the theta frequency band. We show that the visualization of a CNN model can reveal interesting EEG biomarkers. Using these tools, EEG researchers using Deep Learning can better identify the learned EEG features, possibly identifying new class-relevant biomarkers.