# Transfer Learning of an Ensemble of DNNs for SSVEP BCI Spellers without User-Specific Training

arXiv: https://arxiv.org/abs/2209.01511 · HTML: https://mlsanity.com/paper/2209.01511/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 36 references that are in the map.
- First posted on arXiv 3 Sep 2022.

## Builds on (most cited first, 5 of 5)

- [EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfaces](https://mlsanity.com/paper/1611.08024/) (2016)
- [Transfer Learning for EEG-Based Brain-Computer Interfaces: A Review of Progress Made Since 2016](https://mlsanity.com/paper/2004.06286/) (2020)
- [Compact Convolutional Neural Networks for Classification of Asynchronous Steady-state Visual Evoked Potentials](https://mlsanity.com/paper/1803.04566/) (2018)
- [BETA: A Large Benchmark Database Toward SSVEP-BCI Application](https://mlsanity.com/paper/1911.13045/) (2019)
- [A Deep Neural Network for SSVEP-based Brain-Computer Interfaces](https://mlsanity.com/paper/2011.08562/) (2020)

## Built on by (most cited first, 6 of 6)

- [A Systematic Evaluation of Euclidean Alignment with Deep Learning for EEG Decoding](https://mlsanity.com/paper/2401.10746/) (2024)
- [Source-Free Domain Adaptation for SSVEP-based Brain-Computer Interfaces](https://mlsanity.com/paper/2305.17403/) (2023)
- [iFuzzyTL: Interpretable Fuzzy Transfer Learning for SSVEP BCI System](https://mlsanity.com/paper/2410.12267/) (2024)
- [Short-length SSVEP data extension by a novel generative adversarial networks based framework](https://mlsanity.com/paper/2301.05599/) (2023)
- [Fast and Expressive Gesture Recognition using a Combination-Homomorphic Electromyogram Encoder](https://mlsanity.com/paper/2311.14675/) (2023)
- [Rethinking Self-Training Based Cross-Subject Domain Adaptation for SSVEP Classification](https://mlsanity.com/paper/2601.21203/) (2026)

## Abstract (arXiv)

Objective: Steady-state visually evoked potentials (SSVEPs), measured with EEG (electroencephalogram), yield decent information transfer rates (ITR) in brain-computer interface (BCI) spellers. However, the current high performing SSVEP BCI spellers in the literature require an initial lengthy and tiring user-specific training for each new user for system adaptation, including data collection with EEG experiments, algorithm training and calibration (all are before the actual use of the system). This impedes the widespread use of BCIs. To ensure practicality, we propose a highly novel target identification method based on an ensemble of deep neural networks (DNNs), which does not require any sort of user-specific training. Method: We exploit already-existing literature datasets from participants of previously conducted EEG experiments to train a global target identifier DNN first, which is then fine-tuned to each participant. We transfer this ensemble of fine-tuned DNNs to the new user instance, determine the k most representative DNNs according to the participants' statistical similarities to the new user, and predict the target character through a weighted combination of the ensemble predictions. Results: On two large-scale benchmark and BETA datasets, our method achieves impressive 155.51 bits/min and 114.64 bits/min ITRs. Code is available for reproducibility: https://github.com/osmanberke/Ensemble-of-DNNs Conclusion: The proposed method significantly outperforms all the state-of-the-art alternatives for all stimulation durations in [0.2-1.0] seconds on both datasets. Significance: Our Ensemble-DNN method has the potential to promote the practical widespread deployment of BCI spellers in daily lives as we provide the highest performance while enabling the immediate system use without any user-specific training.