# Evaluating the structure of cognitive tasks with transfer learning

arXiv: https://arxiv.org/abs/2308.02408 · HTML: https://mlsanity.com/paper/2308.02408/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 37 of its 103 references that are in the map.
- First posted on arXiv 28 Jul 2023.

## Builds on (most cited first, 30 of 37)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [An Empirical Study of Training Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.02057/) (2021)
- [Understanding intermediate layers using linear classifier probes](https://mlsanity.com/paper/1610.01644/) (2016)
- [A Comprehensive Survey on Transfer Learning](https://mlsanity.com/paper/1911.02685/) (2019)
- [Fine-Tuning can Distort Pretrained Features and Underperform Out-of-Distribution](https://mlsanity.com/paper/2202.10054/) (2022)
- [Taskonomy: Disentangling Task Transfer Learning](https://mlsanity.com/paper/1804.08328/) (2018)
- [EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfaces](https://mlsanity.com/paper/1611.08024/) (2016)
- [Which Tasks Should Be Learned Together in Multi-task Learning?](https://mlsanity.com/paper/1905.07553/) (2019)
- [A Survey of Unsupervised Deep Domain Adaptation](https://mlsanity.com/paper/1812.02849/) (2018)
- [Characterizing and Avoiding Negative Transfer](https://mlsanity.com/paper/1811.09751/) (2018)
- [Task2Vec: Task Embedding for Meta-Learning](https://mlsanity.com/paper/1902.03545/) (2019)
- [Deep learning-based electroencephalography analysis: a systematic review](https://mlsanity.com/paper/1901.05498/) (2019)
- [Uncovering the structure of clinical EEG signals with self-supervised learning](https://mlsanity.com/paper/2007.16104/) (2020)
- [XSleepNet: Multi-View Sequential Model for Automatic Sleep Staging](https://mlsanity.com/paper/2007.05492/) (2020)
- [Representation Similarity Analysis for Efficient Task taxonomy & Transfer Learning](https://mlsanity.com/paper/1904.11740/) (2019)
- [MOABB: Trustworthy algorithm benchmarking for BCIs](https://mlsanity.com/paper/1805.06427/) (2018)
- [A deep learning architecture for temporal sleep stage classification using multivariate and multimodal time series](https://mlsanity.com/paper/1707.03321/) (2017)
- [Factors of Influence for Transfer Learning across Diverse Appearance Domains and Task Types](https://mlsanity.com/paper/2103.13318/) (2021)
- [Transfer Learning in Brain-Computer Interfaces](https://mlsanity.com/paper/1512.00296/) (2015)
- [Machine-Learning-Based Diagnostics of EEG Pathology](https://mlsanity.com/paper/2002.05115/) (2020)
- [Low-Dimensional Structure in the Space of Language Representations is Reflected in Brain Responses](https://mlsanity.com/paper/2106.05426/) (2021)
- [Data augmentation for learning predictive models on EEG: a systematic comparison](https://mlsanity.com/paper/2206.14483/) (2022)
- [Transfer Learning for Motor Imagery Based Brain-Computer Interfaces: A Complete Pipeline](https://mlsanity.com/paper/2007.03746/) (2020)
- [ChronoNet: A Deep Recurrent Neural Network for Abnormal EEG Identification](https://mlsanity.com/paper/1802.00308/) (2018)
- [Neural Language Taskonomy: Which NLP Tasks are the most Predictive of fMRI Brain Activity?](https://mlsanity.com/paper/2205.01404/) (2022)
- [Zero-Shot Task Transfer](https://mlsanity.com/paper/1903.01092/) (2019)
- [Self-supervised representation learning from electroencephalography signals](https://mlsanity.com/paper/1911.05419/) (2019)
- [2021 BEETL Competition: Advancing Transfer Learning for Subject Independence & Heterogenous EEG Data Sets](https://mlsanity.com/paper/2202.12950/) (2022)
- [Faster independent component analysis by preconditioning with Hessian approximations](https://mlsanity.com/paper/1706.08171/) (2017)
- [A Plug&Play P300 BCI Using Information Geometry](https://mlsanity.com/paper/1409.0107/) (2014)

## Built on by (most cited first, 8 of 8)

- [The largest EEG-based BCI reproducibility study for open science: the MOABB benchmark](https://mlsanity.com/paper/2404.15319/) (2024)
- [Calibration-free online test-time adaptation for electroencephalography motor imagery decoding](https://mlsanity.com/paper/2311.18520/) (2023)
- [A Systematic Evaluation of Euclidean Alignment with Deep Learning for EEG Decoding](https://mlsanity.com/paper/2401.10746/) (2024)
- [EEG Foundation Challenge: From Cross-Task to Cross-Subject EEG Decoding](https://mlsanity.com/paper/2506.19141/) (2025)
- [Transfer Learning between Motor Imagery Datasets using Deep Learning -- Validation of Framework and Comparison of Datasets](https://mlsanity.com/paper/2311.16109/) (2023)
- [Learning aligned EEG representations with subject-specific encoders](https://mlsanity.com/paper/2606.16462/) (2026)
- [ERP-XTTN: Interpretable Prototype-Guided Cross-Attention for Cross-Subject ERP Classification](https://mlsanity.com/paper/2606.02939/) (2026)
- [Neuron-Level Architecture Growth: A Controlled Evaluation for EEG Time-Series Decoding](https://mlsanity.com/paper/2609.33880/) (2026)

## Abstract (arXiv)

Electroencephalography (EEG) decoding is a challenging task due to the limited availability of labelled data. While transfer learning is a promising technique to address this challenge, it assumes that transferable data domains and task are known, which is not the case in this setting. This study investigates the transferability of deep learning representations between different EEG decoding tasks. We conduct extensive experiments using state-of-the-art decoding models on two recently released EEG datasets, ERP CORE and M3CV, containing over 140 subjects and 11 distinct cognitive tasks. We measure the transferability of learned representations by pre-training deep neural networks on one task and assessing their ability to decode subsequent tasks. Our experiments demonstrate that, even with linear probing transfer, significant improvements in decoding performance can be obtained, with gains of up to 28% compare with the pure supervised approach. Additionally, we discover evidence that certain decoding paradigms elicit specific and narrow brain activities, while others benefit from pre-training on a broad range of representations. By revealing which tasks transfer well and demonstrating the benefits of transfer learning for EEG decoding, our findings have practical implications for mitigating data scarcity in this setting. The transfer maps generated also provide insights into the hierarchical relations between cognitive tasks, hence enhancing our understanding of how these tasks are connected from a neuroscientific standpoint.