Source-Free Domain Adaptation for SSVEP-based Brain-Computer Interfaces

TL;DR

This paper introduces a source-free domain adaptation method for SSVEP-based BCI spellers that eliminates the need for calibration data from new users, achieving high information transfer rates and improving user comfort.

Contribution

A novel source-free deep neural network adaptation technique for SSVEP BCI that uses unlabeled target data with a custom loss function, enhancing usability and performance.

Findings

Achieved ITRs of 201.15 and 145.02 bits/min on benchmark datasets.

Outperformed state-of-the-art methods in accuracy and speed.

Removed calibration requirement, improving user comfort.

Abstract

Objective: SSVEP-based BCI spellers assist individuals experiencing speech difficulties by enabling them to communicate at a fast rate. However, achieving a high information transfer rate (ITR) in most prominent methods requires an extensive calibration period before using the system, leading to discomfort for new users. We address this issue by proposing a novel method that adapts a powerful deep neural network (DNN) pre-trained on data from source domains (data from former users or participants of previous experiments), to the new user (target domain) using only unlabeled target data. Approach: Our method adapts the pre-trained DNN to the new user by minimizing our proposed custom loss function composed of self-adaptation and local-regularity terms. The self-adaptation term uses the pseudo-label strategy, while the novel local-regularity term exploits the data structure and forces the DNN to assign similar labels to adjacent instances. Main results: Our method achieves excellent ITRs of 201.15 bits/min and 145.02 bits/min on the benchmark and BETA datasets, respectively, and outperforms the state-of-the-art alternatives. Our code is available at https://github.com/osmanberke/SFDA-SSVEP-BCI Significance: The proposed method prioritizes user comfort by removing the burden of calibration while maintaining an excellent character identification accuracy and ITR. Because of these attributes, our approach could significantly accelerate the adoption of BCI systems into everyday life.