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

• URL: http://arxiv.org/abs/2305.17403v2
• Date: Sun, 19 Nov 2023 11:07:59 GMT
• Title: Source-Free Domain Adaptation for SSVEP-based Brain-Computer Interfaces
• Authors: Osman Berke Guney, Deniz Kucukahmetler and Huseyin Ozkan
• Abstract summary: This paper presents a source free domain adaptation method for steady-state visually evoked potentials (SSVEP) based brain-computer interface (BCI) spellers. achieving a high information transfer rate (ITR) in most prominent methods requires an extensive calibration period before using the system. We propose a novel method that adapts a powerful deep neural network (DNN) pre-trained on data from source domains to the new user.
• Score: 1.4364491422470593
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper presents a source free domain adaptation method for steady-state visually evoked potentials (SSVEP) based brain-computer interface (BCI) spellers. 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), based only on the unlabeled target data. This adaptation is achieved 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. The proposed method priorities user comfort by removing the burden of calibration while maintaining an excellent character identification accuracy and ITR. In particular, our method achieves striking 201.15 bits/min and 145.02 bits/min ITRs 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

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