EEG SIGNAL CLASSIFICATION USING FEW-SHOT LEARNING

Abstract

Electroencephalogram (EEG) signals are crucial in various applications, including Motor Imagery, Emotion Recognition, Visual Evoked Potentials, and Mental Workload assessment. However, EEG classification remains challenging due to limited labelled data, high noise levels, and substantial inter- and intra-subject variability. This thesis addresses these challenges by leveraging Few-Shot Learning (FSL) techniques to enable e!ective learning from minimal data for EEG signal classification. To overcome key limitations, this research integrates Data Augmentation, Transfer Learning, and Self-Supervised Learning (SSL) within the FSL framework. Specifically, it focuses on (1) developing EEG-specific data augmentation strategies to mitigate data scarcity, (2) designing transfer learning methodology to facilitate e”cient knowledge transfer across subjects, and (3) formulating SSL methods to enhance FSL with minimal labelled data. Firstly, the thesis presents a comprehensive literature review of FSL techniques in EEG classification, detailing data augmentation, transfer learning, and SSL methodologies. It establishes best practices for FSL for EEG classification and provides standardized guidelines for reporting results in future studies. Secondly, it explores data augmentation techniques to reduce dependence on limited EEG datasets by generating realistic augmented samples. It introduces Auto- Augmentation for Emotion Recognition in EEG - A Class and Subject Invariant Approach (ADAPTER) framework, which, when integrated with the cross-subject model Self-Organizing Graph Neural Network (SOGNN), achieves around 2% F1- score gain over vanilla SOGNN achieving 88.54% of cross-subject accuracy on SEED. Thirdly, recognizing the need for improved subject adaptation, the thesis proposes a novel framework called Transfer and Robust Adaptation of New Subjects in EEG vi Technology (TRANSIT-EEG). It combines a subject-specific data-augmentation - Individualised Denoising Probabilistic Model (IDPM) with Low-Rank Adaptation (LoRA) based transfer learning on an enhanced SOGNN model called Self-Organizing Graph Attention Transformer (SOGAT). Experimental evaluations on SEED and Phyaat datasets demonstrate superior cross-subject F1 scores of 91.53% and 87.78%, respectively. Finally, the work addresses cross-device generalization in EEG classification through two Self-Supervised Learning frameworks: (i) Self-Supervised Enhancement for Multidimensional Emotion Recognition using GNNs for EEG (SS-EMERGE) and (ii) Unified Framework for Yielding EEG-based Emotion Recognition Model with Self-Supervised Learning (UNIFY-ESSL). SS-EMERGE employs a multidimensional architecture to capture temporal, spectral, and spatial features. A meiosis-based data-augmentation pretext task drives cross-subject generalization. The model delivers Macro-F1 scores of 92.35% and 81.51% on SEED and SEED-IV, respectively. When fine-tuned with only half of the labels, it still achieves 86.13% and 76.75% on SEED and SEED-IV, respectively. UNIFY-ESSL evaluates Contrastive Learning (SimCLR) and Contrastive Predictive Coding (CPC) based pretext tasks alongside a proposed data sampling strategy. The experimental results show that SimCLR attains F1- scores of 82.62%, 87.83%, and 89.05% on SEED, DEAP, and DREAMER datasets, respectively, while CPC achieves 81.35%, 82.27%, and 91.23%. It improves cross- dataset generalization, with a 1-2% performance gain on DREAMER and maintained performance on DEAP despite channel reduction, although SEED experiences a 3% F1-score drop due to significant channel reduction. These contributions enable realistic data augmentation, rapid adaptation to new subjects for personalization, and unified modeling across datasets—advancing robust, adaptable, and generalizable EEG classification for diverse real-world applications.