# FBDNN: Filter Banks and Deep Neural Networks for Portable and Fast Brain-Computer Interfaces

arXiv: https://arxiv.org/abs/2109.02165 · HTML: https://mlsanity.com/paper/2109.02165/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 30 references that are in the map.
- First posted on arXiv 5 Sep 2021.

## Builds on (most cited first, 4 of 4)

- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [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)
- [Transfer Learning and SpecAugment applied to SSVEP Based BCI Classification](https://mlsanity.com/paper/2010.06503/) (2020)

## Built on by (most cited first, 1 of 1)

- [SSVEP-DAN: A Data Alignment Network for SSVEP-based Brain Computer Interfaces](https://mlsanity.com/paper/2311.12666/) (2023)

## Abstract (arXiv)

Objective: To propose novel SSVEP classification methodologies using deep neural networks (DNNs) and improve performances in single-channel and user-independent brain-computer interfaces (BCIs) with small data lengths. Approach: We propose the utilization of filter banks (creating sub-band components of the EEG signal) in conjunction with DNNs. In this context, we created three different models: a recurrent neural network (FBRNN) analyzing the time domain, a 2D convolutional neural network (FBCNN-2D) processing complex spectrum features and a 3D convolutional neural network (FBCNN-3D) analyzing complex spectrograms, which we introduce in this study as possible input for SSVEP classification. We tested our neural networks on three open datasets and conceived them so as not to require calibration from the final user, simulating a user-independent BCI. Results: The DNNs with the filter banks surpassed the accuracy of similar networks without this preprocessing step by considerable margins, and they outperformed common SSVEP classification methods (SVM and FBCCA) by even higher margins. Conclusion and significance: Filter banks allow different types of deep neural networks to more efficiently analyze the harmonic components of SSVEP. Complex spectrograms carry more information than complex spectrum features and the magnitude spectrum, allowing the FBCNN-3D to surpass the other CNNs. The performances obtained in the challenging classification problems indicates a strong potential for the construction of portable, economical, fast and low-latency BCIs.