# Cascade and Parallel Convolutional Recurrent Neural Networks on EEG-based Intention Recognition for Brain Computer Interface

arXiv: https://arxiv.org/abs/1708.06578 · HTML: https://mlsanity.com/paper/1708.06578/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Aug 2017.

## Built on by (most cited first, 24 of 24)

- [EEG based Emotion Recognition: A Tutorial and Review](https://mlsanity.com/paper/2203.11279/) (2022)
- [GMSS: Graph-Based Multi-Task Self-Supervised Learning for EEG Emotion Recognition](https://mlsanity.com/paper/2205.01030/) (2022)
- [EEGMatch: Learning with Incomplete Labels for Semi-Supervised EEG-based Cross-Subject Emotion Recognition](https://mlsanity.com/paper/2304.06496/) (2023)
- [Semi-Supervised Dual-Stream Self-Attentive Adversarial Graph Contrastive Learning for Cross-Subject EEG-based Emotion Recognition](https://mlsanity.com/paper/2308.11635/) (2023)
- [EEG Decoding for Datasets with Heterogenous Electrode Configurations using Transfer Learning Graph Neural Networks](https://mlsanity.com/paper/2306.13109/) (2023)
- [Deep Learning Approaches for Multimodal Intent Recognition: A Survey](https://mlsanity.com/paper/2507.22934/) (2025)
- [Source-free Subject Adaptation for EEG-based Visual Recognition](https://mlsanity.com/paper/2301.08448/) (2023)
- [See What You See: Self-supervised Cross-modal Retrieval of Visual Stimuli from Brain Activity](https://mlsanity.com/paper/2208.03666/) (2022)
- [Partial Label Learning for Emotion Recognition from EEG](https://mlsanity.com/paper/2302.13170/) (2023)
- [Classification of Alzheimers Disease with Deep Learning on Eye-tracking Data](https://mlsanity.com/paper/2309.12574/) (2023)
- [PL-DCP: A Pairwise Learning framework with Domain and Class Prototypes for EEG emotion recognition under unseen target conditions](https://mlsanity.com/paper/2412.00082/) (2024)
- [CMCRD: Cross-Modal Contrastive Representation Distillation for Emotion Recognition](https://mlsanity.com/paper/2504.09221/) (2025)
- [Analysis of EEG frequency bands for Envisioned Speech Recognition](https://mlsanity.com/paper/2203.15250/) (2022)
- [On The Expressivity of Recurrent Neural Cascades](https://mlsanity.com/paper/2312.09048/) (2023)
- [Subject-Independent Deep Architecture for EEG-based Motor Imagery Classification](https://mlsanity.com/paper/2402.09438/) (2024)
- [Scheduled Knowledge Acquisition on Lightweight Vector Symbolic Architectures for Brain-Computer Interfaces](https://mlsanity.com/paper/2403.13844/) (2024)
- [Dual Stream Graph Transformer Fusion Networks for Enhanced Brain Decoding](https://mlsanity.com/paper/2410.07189/) (2024)
- [Unsupervised Pairwise Learning Optimization Framework for Cross-Corpus EEG-Based Emotion Recognition Based on Prototype Representation](https://mlsanity.com/paper/2508.11663/) (2025)
- [Uncertainty Detection and Reduction in Neural Decoding of EEG Signals](https://mlsanity.com/paper/2201.00627/) (2021)
- [An intertwined neural network model for EEG classification in brain-computer interfaces](https://mlsanity.com/paper/2208.08860/) (2022)
- [EEGSN: Towards Efficient Low-latency Decoding of EEG with Graph Spiking Neural Networks](https://mlsanity.com/paper/2304.07655/) (2023)
- [On the Expressivity of Recurrent Neural Cascades with Identity](https://mlsanity.com/paper/2405.11657/) (2024)
- [SSTAF: Spatial-Spectral-Temporal Attention Fusion Transformer for Motor Imagery Classification](https://mlsanity.com/paper/2504.13220/) (2025)
- [Accelerating Reinforcement Learning via Error-Related Human Brain Signals](https://mlsanity.com/paper/2511.18878/) (2025)

## Abstract (arXiv)

Brain-Computer Interface (BCI) is a system empowering humans to communicate with or control the outside world with exclusively brain intentions. Electroencephalography (EEG) based BCIs are promising solutions due to their convenient and portable instruments. Motor imagery EEG (MI-EEG) is a kind of most widely focused EEG signals, which reveals a subjects movement intentions without actual actions. Despite the extensive research of MI-EEG in recent years, it is still challenging to interpret EEG signals effectively due to the massive noises in EEG signals (e.g., low signal noise ratio and incomplete EEG signals), and difficulties in capturing the inconspicuous relationships between EEG signals and certain brain activities. Most existing works either only consider EEG as chain-like sequences neglecting complex dependencies between adjacent signals or performing simple temporal averaging over EEG sequences. In this paper, we introduce both cascade and parallel convolutional recurrent neural network models for precisely identifying human intended movements by effectively learning compositional spatio-temporal representations of raw EEG streams. The proposed models grasp the spatial correlations between physically neighboring EEG signals by converting the chain like EEG sequences into a 2D mesh like hierarchy. An LSTM based recurrent network is able to extract the subtle temporal dependencies of EEG data streams. Extensive experiments on a large-scale MI-EEG dataset (108 subjects, 3,145,160 EEG records) have demonstrated that both models achieve high accuracy near 98.3% and outperform a set of baseline methods and most recent deep learning based EEG recognition models, yielding a significant accuracy increase of 18% in the cross-subject validation scenario.