2021 BEETL competition: advancing transfer learning for subject independence & heterogenous EEG data sets

Transfer learning and meta-learning offer some of the most promising avenues to unlock the scalability of healthcare and consumer technologies driven by biosignal data. This is because regular machine learning methods cannot generalise well across human subjects and handle learning from different, heterogeneously collected data sets, thus limiting the scale of training data available. On the other hand, the many developments in transfer- and meta-learning fields would benefit significantly from a real-world benchmark with immediate practical application. Therefore, we pick electroencephalography (EEG) as an exemplar for all the things that make biosignal data analysis a hard problem. We design two transfer learning challenges around a. clinical diagnostics and b. neurotechnology. These two challenges are designed to probe algorithmic performance with all the challenges of biosignal data, such as low signal-to-noise ratios, major variability among subjects, differences in the data recording sessions and techniques, and even between the specific BCI tasks recorded in the dataset. Task 1 is centred on the field of medical diagnostics, addressing automatic sleep stage annotation across subjects. Task 2 is centred on Brain-Computer Interfacing (BCI), addressing motor imagery decoding across both subjects and data sets. The successful 2021 BEETL competition with its over 30 competing teams and its 3 winning entries brought attention to the potential of deep transfer learning and combinations of set theory and conventional machine learning techniques to overcome the challenges. The results set a new state-of-the-art for the real-world BEETL benchmarks

Eye Blink Artifact Removal in EEG Using Tensor Decomposition

Part 2: MHDW WorkshopInternational audienceEEG data are usually contaminated with signals related to subject’s activities, the so called artifacts, which degrade the information contained in recordings. The removal of this additional information is essential to the improvement of EEG signals’ interpretation. The proposed method is based on the analysis, using Tucker decomposition, of a tensor constructed using continuous wavelet transform. Our contribution is an automatic method which processes simultaneously spatial, temporal and frequency information contained in EEG recordings in order to remove eye blink related information. The proposed method is compared with a matrix based removal method and shows promising results regarding reconstruction error and retaining the texture of the artifact free signal

Deep learning methods in electroencephalography

The volume, variability and high level of noise in electroencephalographic (EEG) recordings of the electrical brain activity make them difficult to approach with standard machine learning techniques. Deep learning methods, especially artificial neural networks inspired by the structure of the brain itself are better suited for the domain because of their end-to-end approach. They have already shown outstanding performance in computer vision and they are increasingly popular in the EEG domain. In this chapter, the state-of-the-art architectures and approaches to classification, segmentation, and enhancement of EEG recordings are described in applications to brain-computer interfaces, medical diagnostics and emotion recognition. In the experimental part, the complete pipeline of deep learning for EEG is presented on the example of the detection of erroneous responses in the Eriksen flanker task with results showing advantages over a traditional machine learning approach. Additionally, the refined list of public EEG data sources suitable for deep learning and guidelines for future applications are given