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Evaluating Fast Adaptability of Neural Networks for Brain-Computer Interface

Anupam Sharma, Krishna Miyapuram

TL;DR

The paper tackles fast adaptability in EEG-based BCIs, addressing how classifiers generalize to unseen individuals and tasks. It compares Model-Agnostic Meta-Learning (MAML) with standard transfer learning using a CNN based on EEGNet, but with layer-normalization to suit small EEG datasets and long time-series, and constrains fine-tuning to ten iterations for real-time calibration. Empirically, transfer learning with layer-norm consistently outperforms MAML in both cross-subject and cross-task scenarios on the Physionet EEG Motor Movement/Imagery dataset, showing faster adaptation and competitive accuracy with minimal data. The work also proposes a practical evaluation strategy for fast adaptability and demonstrates that architectural adjustments (layer-norm) plus simple transfer learning yield robust, rapid calibration suitable for real-world BCI deployments.

Abstract

Electroencephalography (EEG) classification is a versatile and portable technique for building non-invasive Brain-computer Interfaces (BCI). However, the classifiers that decode cognitive states from EEG brain data perform poorly when tested on newer domains, such as tasks or individuals absent during model training. Researchers have recently used complex strategies like Model-agnostic meta-learning (MAML) for domain adaptation. Nevertheless, there is a need for an evaluation strategy to evaluate the fast adaptability of the models, as this characteristic is essential for real-life BCI applications for quick calibration. We used motor movement and imaginary signals as input to Convolutional Neural Networks (CNN) based classifier for the experiments. Datasets with EEG signals typically have fewer examples and higher time resolution. Even though batch-normalization is preferred for Convolutional Neural Networks (CNN), we empirically show that layer-normalization can improve the adaptability of CNN-based EEG classifiers with not more than ten fine-tuning steps. In summary, the present work (i) proposes a simple strategy to evaluate fast adaptability, and (ii) empirically demonstrate fast adaptability across individuals as well as across tasks with simple transfer learning as compared to MAML approach.

Evaluating Fast Adaptability of Neural Networks for Brain-Computer Interface

TL;DR

The paper tackles fast adaptability in EEG-based BCIs, addressing how classifiers generalize to unseen individuals and tasks. It compares Model-Agnostic Meta-Learning (MAML) with standard transfer learning using a CNN based on EEGNet, but with layer-normalization to suit small EEG datasets and long time-series, and constrains fine-tuning to ten iterations for real-time calibration. Empirically, transfer learning with layer-norm consistently outperforms MAML in both cross-subject and cross-task scenarios on the Physionet EEG Motor Movement/Imagery dataset, showing faster adaptation and competitive accuracy with minimal data. The work also proposes a practical evaluation strategy for fast adaptability and demonstrates that architectural adjustments (layer-norm) plus simple transfer learning yield robust, rapid calibration suitable for real-world BCI deployments.

Abstract

Electroencephalography (EEG) classification is a versatile and portable technique for building non-invasive Brain-computer Interfaces (BCI). However, the classifiers that decode cognitive states from EEG brain data perform poorly when tested on newer domains, such as tasks or individuals absent during model training. Researchers have recently used complex strategies like Model-agnostic meta-learning (MAML) for domain adaptation. Nevertheless, there is a need for an evaluation strategy to evaluate the fast adaptability of the models, as this characteristic is essential for real-life BCI applications for quick calibration. We used motor movement and imaginary signals as input to Convolutional Neural Networks (CNN) based classifier for the experiments. Datasets with EEG signals typically have fewer examples and higher time resolution. Even though batch-normalization is preferred for Convolutional Neural Networks (CNN), we empirically show that layer-normalization can improve the adaptability of CNN-based EEG classifiers with not more than ten fine-tuning steps. In summary, the present work (i) proposes a simple strategy to evaluate fast adaptability, and (ii) empirically demonstrate fast adaptability across individuals as well as across tasks with simple transfer learning as compared to MAML approach.
Paper Structure (16 sections, 4 equations, 3 figures, 4 tables, 1 algorithm)

This paper contains 16 sections, 4 equations, 3 figures, 4 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Work
  3. Methodology
  4. Training strategies
  5. Model architecture
  6. Experiments
  7. Dataset and pre-processing
  8. Batch-norm vs layer-norm
  9. Adaptability across individuals
  10. Adaptability across task activities
  11. Evaluation metrics
  12. Results and Discussion
  13. Batch-norm vs layer-norm
  14. Adaptability across individuals
  15. Adaptability across task activities
  16. ...and 1 more sections

Figures (3)

  • Figure 1: Performance analysis (in accuracy) of models with batch-norm and with layer-norm
  • Figure 2: Performance analysis (in accuracy) of training strategies when adapted to newer individuals
  • Figure 3: Performance analysis (test accuracy) of training strategies when model trained on one activity is adapted to other activities