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

TL;DR

This paper introduces novel deep neural network models combined with filter banks for improved SSVEP classification in portable, user-independent brain-computer interfaces, achieving higher accuracy with small data and no calibration.

Contribution

The study develops three new DNN models utilizing filter banks for SSVEP classification, demonstrating superior performance over existing methods and no need for user calibration.

Findings

DNNs with filter banks outperform those without in accuracy.

FBCNN-3D surpasses other CNN models by analyzing spectrograms.

Results show potential for portable, low-latency BCIs.

Abstract

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.