# FBCNet: A Multi-view Convolutional Neural Network for Brain-Computer Interface arXiv: https://arxiv.org/abs/2104.01233 ยท HTML: https://mlsanity.com/paper/2104.01233/ ## Key facts - Cited by 57 papers in the map (data through 14 Oct 2026). - Builds on 6 of its 36 references that are in the map. - First posted on arXiv 17 Mar 2021. ## Builds on (most cited first, 6 of 6) - [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014) - [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015) - [Xception: Deep Learning with Depthwise Separable Convolutions](https://mlsanity.com/paper/1610.02357/) (2016) - [Learning Important Features Through Propagating Activation Differences](https://mlsanity.com/paper/1704.02685/) (2017) - [EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfaces](https://mlsanity.com/paper/1611.08024/) (2016) - [Deep learning-based electroencephalography analysis: a systematic review](https://mlsanity.com/paper/1901.05498/) (2019) ## Built on by (most cited first, 30 of 57) - [SPD domain-specific batch normalization to crack interpretable unsupervised domain adaptation in EEG](https://mlsanity.com/paper/2206.01323/) (2022) - [MAtt: A Manifold Attention Network for EEG Decoding](https://mlsanity.com/paper/2210.01986/) (2022) - [Tensor-CSPNet: A Novel Geometric Deep Learning Framework for Motor Imagery Classification](https://mlsanity.com/paper/2202.02472/) (2022) - [Graph Neural Networks on SPD Manifolds for Motor Imagery Classification: A Perspective from the Time-Frequency Analysis](https://mlsanity.com/paper/2211.02641/) (2022) - [Deep Riemannian Networks for End-to-End EEG Decoding](https://mlsanity.com/paper/2212.10426/) (2022) - [DBConformer: Dual-Branch Convolutional Transformer for EEG Decoding](https://mlsanity.com/paper/2506.21140/) (2025) - [Learning to Normalize on the SPD Manifold under Bures-Wasserstein Geometry](https://mlsanity.com/paper/2504.00660/) (2025) - [Dual-TSST: A Dual-Branch Temporal-Spectral-Spatial Transformer Model for EEG Decoding](https://mlsanity.com/paper/2409.03251/) (2024) - [Neural Networks on Symmetric Spaces of Noncompact Type](https://mlsanity.com/paper/2601.01097/) (2026) - [CSP-Net: Common Spatial Pattern Empowered Neural Networks for EEG-Based Motor Imagery Classification](https://mlsanity.com/paper/2411.11879/) (2024) - [MIRepNet: A Pipeline and Foundation Model for EEG-Based Motor Imagery Classification](https://mlsanity.com/paper/2507.20254/) (2025) - [Robust Hyperbolic Learning with Curvature-Aware Optimization](https://mlsanity.com/paper/2405.13979/) (2024) - [Brain-Driven Representation Learning Based on Diffusion Model](https://mlsanity.com/paper/2311.07925/) (2023) - [Are EEG Sequences Time Series? EEG Classification with Time Series Models and Joint Subject Training](https://mlsanity.com/paper/2404.06966/) (2024) - [MVCNet: Multi-View Contrastive Network for Motor Imagery Classification](https://mlsanity.com/paper/2502.17482/) (2025) - [Sheaf Neural Networks on SPD Manifolds: Second-Order Geometric Representation Learning](https://mlsanity.com/paper/2604.20308/) (2026) - [Looking deeper into interpretable deep learning in neuroimaging: a comprehensive survey](https://mlsanity.com/paper/2307.09615/) (2023) - [EEG-EMG FAConformer: Frequency Aware Conv-Transformer for the fusion of EEG and EMG](https://mlsanity.com/paper/2409.18973/) (2024) - [Towards Dynamic Neural Communication and Speech Neuroprosthesis Based on Viseme Decoding](https://mlsanity.com/paper/2501.14790/) (2025) - [HEEGNet: Hyperbolic Embeddings for EEG](https://mlsanity.com/paper/2601.03322/) (2026) - [An embedding for EEG signals learned using a triplet loss](https://mlsanity.com/paper/2304.06495/) (2023) - [Enhancing Motor Imagery Decoding in Brain Computer Interfaces using Riemann Tangent Space Mapping and Cross Frequency Coupling](https://mlsanity.com/paper/2310.19198/) (2023) - [Sparse Multitask Learning for Efficient Neural Representation of Motor Imagery and Execution](https://mlsanity.com/paper/2312.05828/) (2023) - [xEEGNet: Towards Explainable AI in EEG Dementia Classification](https://mlsanity.com/paper/2504.21457/) (2025) - [A Dynamic Domain Adaptation Deep Learning Network for EEG-based Motor Imagery Classification](https://mlsanity.com/paper/2309.11714/) (2023) - [LGL-BCI: A Motor-Imagery-Based Brain-Computer Interface with Geometric Learning](https://mlsanity.com/paper/2310.08051/) (2023) - [Sample Dominance Aware Framework via Non-Parametric Estimation for Spontaneous Brain-Computer Interface](https://mlsanity.com/paper/2311.07079/) (2023) - [Multi-Signal Reconstruction Using Masked Autoencoder From EEG During Polysomnography](https://mlsanity.com/paper/2311.07868/) (2023) - [Neural Speech Embeddings for Speech Synthesis Based on Deep Generative Networks](https://mlsanity.com/paper/2312.05814/) (2023) - [Decoding EEG-based Workload Levels Using Spatio-temporal Features Under Flight Environment](https://mlsanity.com/paper/2312.09423/) (2023) ## Abstract (arXiv) Lack of adequate training samples and noisy high-dimensional features are key challenges faced by Motor Imagery (MI) decoding algorithms for electroencephalogram (EEG) based Brain-Computer Interface (BCI). To address these challenges, inspired from neuro-physiological signatures of MI, this paper proposes a novel Filter-Bank Convolutional Network (FBCNet) for MI classification. FBCNet employs a multi-view data representation followed by spatial filtering to extract spectro-spatially discriminative features. This multistage approach enables efficient training of the network even when limited training data is available. More significantly, in FBCNet, we propose a novel Variance layer that effectively aggregates the EEG time-domain information. With this design, we compare FBCNet with state-of-the-art (SOTA) BCI algorithm on four MI datasets: The BCI competition IV dataset 2a (BCIC-IV-2a), the OpenBMI dataset, and two large datasets from chronic stroke patients. The results show that, by achieving 76.20% 4-class classification accuracy, FBCNet sets a new SOTA for BCIC-IV-2a dataset. On the other three datasets, FBCNet yields up to 8% higher binary classification accuracies. Additionally, using explainable AI techniques we present one of the first reports about the differences in discriminative EEG features between healthy subjects and stroke patients. Also, the FBCNet source code is available at https://github.com/ravikiran-mane/FBCNet.