CRRL: Learning Channel-invariant Neural Representations for High-performance Cross-day Decoding

Brain-computer interfaces have shown great potential in motor and speech rehabilitation, but still suffer from low performance stability across days, mostly due to the instabilities in neural signals. These instabilities, partially caused by neuron deaths and electrode shifts, leading to channel-level variabilities among different recording days. Previous studies mostly focused on aligning multi-day neural signals of onto a low-dimensional latent manifold to reduce the variabilities, while faced with difficulties when neural signals exhibit significant drift. Here, we propose to learn a channel-level invariant neural representation to address the variabilities in channels across days. It contains a channel-rearrangement module to learn stable representations against electrode shifts, and a channel reconstruction module to handle the missing neurons. The proposed method achieved the state-of-the-art performance with cross-day decoding tasks over two months, on multiple benchmark BCI datasets. The proposed approach showed good generalization ability that can be incorporated to different neural networks.

Authors

• Xianhan Tan The College of Computer Science and Technology, Zhejiang University, China MOE Frontier Science Center for Brain Science and Brain-machine Integration, Zhejiang University, China Affiliated Mental Health Center \& Hangzhou Seventh People's Hospital, Zhejiang University, China
• Binli Luo
• Yu Qi MOE Frontier Science Center for Brain Science and Brain-machine Integration, Zhejiang University Affiliated Mental Health Center & Hangzhou Seventh People’s Hospital, Zhejiang University The College of Computer Science and Technology, Zhejiang University State Key Lab of Brain-Machine Intelligence, Zhejiang University
• Yueming Wang The College of Computer Science and Technology, Zhejiang University

Keywords

No keywords are indexed for this paper.