# Compact Convolutional Neural Networks for Classification of Asynchronous Steady-state Visual Evoked Potentials

arXiv: https://arxiv.org/abs/1803.04566 · HTML: https://mlsanity.com/paper/1803.04566/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Mar 2018.

## Built on by (most cited first, 15 of 15)

- [MAtt: A Manifold Attention Network for EEG Decoding](https://mlsanity.com/paper/2210.01986/) (2022)
- [A Transformer-based deep neural network model for SSVEP classification](https://mlsanity.com/paper/2210.04172/) (2022)
- [Transfer Learning of an Ensemble of DNNs for SSVEP BCI Spellers without User-Specific Training](https://mlsanity.com/paper/2209.01511/) (2022)
- [Boosting Template-based SSVEP Decoding by Cross-domain Transfer Learning](https://mlsanity.com/paper/2102.05194/) (2021)
- [A Hybrid Complex-valued Neural Network Framework with Applications to Electroencephalogram (EEG)](https://mlsanity.com/paper/2207.14799/) (2022)
- [Classification of Raw MEG/EEG Data with Detach-Rocket Ensemble: An Improved ROCKET Algorithm for Multivariate Time Series Analysis](https://mlsanity.com/paper/2408.02760/) (2024)
- [ExBrainable: An Open-Source GUI for CNN-based EEG Decoding and Model Interpretation](https://mlsanity.com/paper/2201.04065/) (2022)
- [SSVEP-DAN: A Data Alignment Network for SSVEP-based Brain Computer Interfaces](https://mlsanity.com/paper/2311.12666/) (2023)
- [Convolutional Neural Networks with A Topographic Representation Module for EEG-Based Brain-Computer Interfaces](https://mlsanity.com/paper/2208.10708/) (2022)
- [Short-length SSVEP data extension by a novel generative adversarial networks based framework](https://mlsanity.com/paper/2301.05599/) (2023)
- [Subject-Independent Classification of Brain Signals using Skip Connections](https://mlsanity.com/paper/2301.07918/) (2023)
- [BRIEDGE: EEG-Adaptive Edge AI for Multi-Brain to Multi-Robot Interaction](https://mlsanity.com/paper/2403.15432/) (2024)
- [SSVEP-BiMA: Bifocal Masking Attention Leveraging Native and Symmetric-Antisymmetric Components for Robust SSVEP Decoding](https://mlsanity.com/paper/2502.10994/) (2025)
- [Interpretable MEG Decoding of Perceived Speech: Cortical Sources and the Stimulus Features That Drive Retrieval](https://mlsanity.com/paper/2608.01481/) (2026)
- [EEG-VID: Task-Guided Latent Predictive Pretraining for EEG Decoding and Assistive Target Selection](https://mlsanity.com/paper/2609.00566/) (2026)

## Abstract (arXiv)

Steady-State Visual Evoked Potentials (SSVEPs) are neural oscillations from the parietal and occipital regions of the brain that are evoked from flickering visual stimuli. SSVEPs are robust signals measurable in the electroencephalogram (EEG) and are commonly used in brain-computer interfaces (BCIs). However, methods for high-accuracy decoding of SSVEPs usually require hand-crafted approaches that leverage domain-specific knowledge of the stimulus signals, such as specific temporal frequencies in the visual stimuli and their relative spatial arrangement. When this knowledge is unavailable, such as when SSVEP signals are acquired asynchronously, such approaches tend to fail. In this paper, we show how a compact convolutional neural network (Compact-CNN), which only requires raw EEG signals for automatic feature extraction, can be used to decode signals from a 12-class SSVEP dataset without the need for any domain-specific knowledge or calibration data. We report across subject mean accuracy of approximately 80% (chance being 8.3%) and show this is substantially better than current state-of-the-art hand-crafted approaches using canonical correlation analysis (CCA) and Combined-CCA. Furthermore, we analyze our Compact-CNN to examine the underlying feature representation, discovering that the deep learner extracts additional phase and amplitude related features associated with the structure of the dataset. We discuss how our Compact-CNN shows promise for BCI applications that allow users to freely gaze/attend to any stimulus at any time (e.g., asynchronous BCI) as well as provides a method for analyzing SSVEP signals in a way that might augment our understanding about the basic processing in the visual cortex.