Compact and interpretable convolutional neural network architecture for electroencephalogram based motor imagery decoding

Recently, due to the popularity of deep learning, the applicability of deep Neural Networks (DNN) algorithms such as the convolutional neural networks (CNN) has been explored in decoding electroencephalogram (EEG) for Brain-Computer Interface (BCI) applications. This allows decoding of the EEG signals end-to-end, eliminating the tedious process of manually tuning each process in the decoding pipeline. However, the current DNN architectures, consisting of multiple hidden layers and numerous parameters, are not developed for EEG decoding and classification tasks, making them underperform when decoding EEG signals. Apart from this, a DNN is typically treated as a black box and interpreting what the network learns in solving the classification task is difficult, hindering from performing neurophysiological validation of the network. This thesis proposes an improved and compact CNN architecture for motor imagery decoding based on the adaptation of SincNet, which was initially developed for speaker recognition task from the raw audio input. Such adaptation allows for a very compact end-to-end neural network with state-of-the-art (SOTA) performances and enables network interpretability for neurophysiological validation in terms of cortical rhythms and spatial analysis. In order to validate the performance of proposed algorithms, two datasets were used; the first is the publicly available BCI Competition IV dataset 2a, which is often used as a benchmark in validating motor imagery (MI) classification algorithms, and a primary data that was initially collected to study the difference between motor imagery and mental rotation task associated motor imagery (MI+MR) BCI. The latter was also used in this study to test the plausibility of the proposed algorithm in highlighting the differences in cortical rhythms. In both datasets, the proposed Sinc adapted CNN algorithms show competitive decoding performance in comparisons with SOTA CNN models, where up to 87% decoding accuracy was achieved in BCI Competition IV dataset 2a and up to 91% decoding accuracy when using the primary MI+MR data. Such decoding performance was achieved with the lowest number of trainable parameters (26.5% - 34.1% reduction in the number of parameters compared to its non-Sinc counterpart). In addition, it was shown that the proposed architecture performs a cleaner band-pass, highlighting the necessary frequency bands that focus on important cortical rhythms during task execution, thus allowing for the development of the proposed Spatial Filter Visualization algorithm. Such characteristic was crucial for the neurophysiological interpretation of the learned spatial features and was not previously established with the benchmarked SOTA methods

A reusable benchmark of brain-age prediction from M/EEG resting-state signals

Population-level modeling can define quantitative measures of individual aging by applying machine learning to large volumes of brain images. These measures of brain age, obtained from the general population, helped characterize disease severity in neurological populations, improving estimates of diagnosis or prognosis. Magnetoencephalography (MEG) and Electroencephalography (EEG) have the potential to further generalize this approach towards prevention and public health by enabling assessments of brain health at large scales in socioeconomically diverse environments. However, more research is needed to define methods that can handle the complexity and diversity of M/EEG signals across diverse real-world contexts. To catalyse this effort, here we propose reusable benchmarks of competing machine learning approaches for brain age modeling. We benchmarked popular classical machine learning pipelines and deep learning architectures previously used for pathology decoding or brain age estimation in 4 international M/EEG cohorts from diverse countries and cultural contexts, including recordings from more than 2500 participants. Our benchmarks were built on top of the M/EEG adaptations of the BIDS standard, providing tools that can be applied with minimal modification on any M/EEG dataset provided in the BIDS format. Our results suggest that, regardless of whether classical machine learning or deep learning was used, the highest performance was reached by pipelines and architectures involving spatially aware representations of the M/EEG signals, leading to R^2 scores between 0.60-0.71. Hand-crafted features paired with random forest regression provided robust benchmarks even in situations in which other approaches failed. Taken together, this set of benchmarks, accompanied by open-source software and high-level Python scripts, can serve as a starting point and quantitative reference for future efforts at developing M/EEG-based measures of brain aging. The generality of the approach renders this benchmark reusable for other related objectives such as modeling specific cognitive variables or clinical endpoints