# Learning Representations from EEG with Deep Recurrent-Convolutional Neural Networks

arXiv: https://arxiv.org/abs/1511.06448 · HTML: https://mlsanity.com/paper/1511.06448/

## Key facts

- Cited by 45 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Nov 2015.

## Built on by (most cited first, 30 of 45)

- [Toward cross-subject and cross-session generalization in EEG-based emotion recognition: Systematic review, taxonomy, and methods](https://mlsanity.com/paper/2212.08744/) (2022)
- [EEG2Vec: Learning Affective EEG Representations via Variational Autoencoders](https://mlsanity.com/paper/2207.08002/) (2022)
- [Robust learning from corrupted EEG with dynamic spatial filtering](https://mlsanity.com/paper/2105.12916/) (2021)
- [A Compact and Interpretable Convolutional Neural Network for Cross-Subject Driver Drowsiness Detection from Single-Channel EEG](https://mlsanity.com/paper/2106.00613/) (2021)
- [Unveiling Thoughts: A Review of Advancements in EEG Brain Signal Decoding into Text](https://mlsanity.com/paper/2405.00726/) (2024)
- [Time Majority Voting, a PC-based EEG Classifier for Non-expert Users](https://mlsanity.com/paper/2207.12662/) (2022)
- [Decoding ECoG signal into 3D hand translation using deep learning](https://mlsanity.com/paper/2110.03528/) (2021)
- [Towards trustworthy seizure onset detection using workflow notes](https://mlsanity.com/paper/2306.08728/) (2023)
- [BrainGPT: Unleashing the Potential of EEG Generalist Foundation Model by Autoregressive Pre-training](https://mlsanity.com/paper/2410.19779/) (2024)
- [Positional-Spectral-Temporal Attention in 3D Convolutional Neural Networks for EEG Emotion Recognition](https://mlsanity.com/paper/2110.09955/) (2021)
- [Impact of dataset size and long-term ECoG-based BCI usage on deep learning decoders performance](https://mlsanity.com/paper/2209.03789/) (2022)
- [Review of Deep Representation Learning Techniques for Brain-Computer Interfaces and Recommendations](https://mlsanity.com/paper/2405.19345/) (2024)
- [An EEG-based approach for Parkinson's disease diagnosis using Capsule network](https://mlsanity.com/paper/2201.00628/) (2021)
- [Comparative Analysis of Deep Learning Approaches for Harmful Brain Activity Detection Using EEG](https://mlsanity.com/paper/2412.07878/) (2024)
- [Investigating the Impact of Rational Dilated Wavelet Transform on Motor Imagery EEG Decoding with Deep Learning Models](https://mlsanity.com/paper/2510.09242/) (2025)
- [EEG-FM-Audit: A Systematic Evaluation and Analysis Pipeline for EEG Foundation Models](https://mlsanity.com/paper/2605.26910/) (2026)
- [BRAIN2DEPTH: Lightweight CNN Model for Classification of Cognitive States from EEG Recordings](https://mlsanity.com/paper/2106.06688/) (2021)
- [EEG-ConvTransformer for Single-Trial EEG based Visual Stimuli Classification](https://mlsanity.com/paper/2107.03983/) (2021)
- [Automated Parkinson's Disease Detection and Affective Analysis from Emotional EEG Signals](https://mlsanity.com/paper/2202.12936/) (2022)
- [Spatio-Temporal Analysis of Transformer based Architecture for Attention Estimation from EEG](https://mlsanity.com/paper/2204.07162/) (2022)
- [Two Heads are Better than One: A Bio-inspired Method for Improving Classification on EEG-ET Data](https://mlsanity.com/paper/2304.06471/) (2023)
- [Subject-Independent Deep Architecture for EEG-based Motor Imagery Classification](https://mlsanity.com/paper/2402.09438/) (2024)
- [Towards Decoding Brain Activity During Passive Listening of Speech](https://mlsanity.com/paper/2402.16996/) (2024)
- [Spatiotemporal Pooling on Appropriate Topological Maps Represented as Two-Dimensional Images for EEG Classification](https://mlsanity.com/paper/2403.04353/) (2024)
- [Perception-Guided EEG Analysis: A Deep Learning Approach Inspired by Level of Detail (LOD) Theory](https://mlsanity.com/paper/2501.10428/) (2025)
- [A novel Fourier Adjacency Transformer for advanced EEG emotion recognition](https://mlsanity.com/paper/2503.13465/) (2025)
- [EEG-based Texture Roughness Classification in Active Tactile Exploration with Invariant Representation Learning Networks](https://mlsanity.com/paper/2102.08976/) (2021)
- [Coherence of Working Memory Study Between Deep Neural Network and Neurophysiology](https://mlsanity.com/paper/2102.10994/) (2021)
- [Cross-Subject Domain Adaptation for Classifying Working Memory Load with Multi-Frame EEG Images](https://mlsanity.com/paper/2106.06769/) (2021)
- [A Saliency based Feature Fusion Model for EEG Emotion Estimation](https://mlsanity.com/paper/2201.03891/) (2022)

## Abstract (arXiv)

One of the challenges in modeling cognitive events from electroencephalogram (EEG) data is finding representations that are invariant to inter- and intra-subject differences, as well as to inherent noise associated with such data. Herein, we propose a novel approach for learning such representations from multi-channel EEG time-series, and demonstrate its advantages in the context of mental load classification task. First, we transform EEG activities into a sequence of topology-preserving multi-spectral images, as opposed to standard EEG analysis techniques that ignore such spatial information. Next, we train a deep recurrent-convolutional network inspired by state-of-the-art video classification to learn robust representations from the sequence of images. The proposed approach is designed to preserve the spatial, spectral, and temporal structure of EEG which leads to finding features that are less sensitive to variations and distortions within each dimension. Empirical evaluation on the cognitive load classification task demonstrated significant improvements in classification accuracy over current state-of-the-art approaches in this field.