# On The Effects Of Data Normalisation For Domain Adaptation On EEG Data

arXiv: https://arxiv.org/abs/2210.01081 · HTML: https://mlsanity.com/paper/2210.01081/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 86 references that are in the map.
- First posted on arXiv 3 Oct 2022.

## Builds on (most cited first, 14 of 14)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Domain-Adversarial Training of Neural Networks](https://mlsanity.com/paper/1505.07818/) (2015)
- [Unsupervised Domain Adaptation by Backpropagation](https://mlsanity.com/paper/1409.7495/) (2014)
- [Adversarial Discriminative Domain Adaptation](https://mlsanity.com/paper/1702.05464/) (2017)
- [Deep CORAL: Correlation Alignment for Deep Domain Adaptation](https://mlsanity.com/paper/1607.01719/) (2016)
- [Domain Generalization: A Survey](https://mlsanity.com/paper/2103.02503/) (2021)
- [Return of Frustratingly Easy Domain Adaptation](https://mlsanity.com/paper/1511.05547/) (2015)
- [A review of domain adaptation without target labels](https://mlsanity.com/paper/1901.05335/) (2019)
- [Correlation Alignment for Unsupervised Domain Adaptation](https://mlsanity.com/paper/1612.01939/) (2016)
- [Deep Subdomain Adaptation Network for Image Classification](https://mlsanity.com/paper/2106.09388/) (2021)
- [Domain-Adversarial Neural Networks](https://mlsanity.com/paper/1412.4446/) (2014)
- [MS-MDA: Multisource Marginal Distribution Adaptation for Cross-subject and Cross-session EEG Emotion Recognition](https://mlsanity.com/paper/2107.07740/) (2021)
- [Learning Domain-Invariant Subspace using Domain Features and Independence Maximization](https://mlsanity.com/paper/1603.04535/) (2016)
- [Spatial Graph Convolutional Neural Network via Structured Subdomain Adaptation and Domain Adversarial Learning for Bearing Fault Diagnosis](https://mlsanity.com/paper/2112.06033/) (2021)

## Built on by (most cited first, 13 of 13)

- [Toward cross-subject and cross-session generalization in EEG-based emotion recognition: Systematic review, taxonomy, and methods](https://mlsanity.com/paper/2212.08744/) (2022)
- [Don't Push the Button! Exploring Data Leakage Risks in Machine Learning and Transfer Learning](https://mlsanity.com/paper/2401.13796/) (2024)
- [Unveiling Thoughts: A Review of Advancements in EEG Brain Signal Decoding into Text](https://mlsanity.com/paper/2405.00726/) (2024)
- [Evaluating the structure of cognitive tasks with transfer learning](https://mlsanity.com/paper/2308.02408/) (2023)
- [Multi-Source and Test-Time Domain Adaptation on Multivariate Signals using Spatio-Temporal Monge Alignment](https://mlsanity.com/paper/2407.14303/) (2024)
- [Latent Alignment with Deep Set EEG Decoders](https://mlsanity.com/paper/2311.17968/) (2023)
- [PSDNorm: Test-Time Temporal Normalization for Deep Learning in Sleep Staging](https://mlsanity.com/paper/2503.04582/) (2025)
- [Neural Brain Fields: A NeRF-Inspired Approach for Generating Nonexistent EEG Electrodes](https://mlsanity.com/paper/2601.00012/) (2025)
- [SincVAE: A new semi-supervised approach to improve anomaly detection on EEG data using SincNet and variational autoencoder](https://mlsanity.com/paper/2406.17537/) (2024)
- [Convolutional Monge Mapping Normalization for learning on sleep data](https://mlsanity.com/paper/2305.18831/) (2023)
- [ARFT-Transformer: Modeling Metric Dependencies for Cross-Project Aging-Related Bug Prediction](https://mlsanity.com/paper/2601.14731/) (2026)
- [Don't stop me now: How Validation Criteria Affect Checkpoint Selection and Early Stopping](https://mlsanity.com/paper/2602.22107/) (2026)
- [Leveraging a Foundation Model for the EEG-Based Diagnosis of Alzheimer's Disease](https://mlsanity.com/paper/2608.27719/) (2026)

## Abstract (arXiv)

In the Machine Learning (ML) literature, a well-known problem is the Dataset Shift problem where, differently from the ML standard hypothesis, the data in the training and test sets can follow different probability distributions, leading ML systems toward poor generalisation performances. This problem is intensely felt in the Brain-Computer Interface (BCI) context, where bio-signals as Electroencephalographic (EEG) are often used. In fact, EEG signals are highly non-stationary both over time and between different subjects. To overcome this problem, several proposed solutions are based on recent transfer learning approaches such as Domain Adaption (DA). In several cases, however, the actual causes of the improvements remain ambiguous. This paper focuses on the impact of data normalisation, or standardisation strategies applied together with DA methods. In particular, using textitSEED, textitDEAP, and textitBCI Competition IV 2a EEG datasets, we experimentally evaluated the impact of different normalization strategies applied with and without several well-known DA methods, comparing the obtained performances. It results that the choice of the normalisation strategy plays a key role on the classifier performances in DA scenarios, and interestingly, in several cases, the use of only an appropriate normalisation schema outperforms the DA technique.