TL;DR
Results indicate that TSMD-Net provides effective and generalizable cross-subject motor decoding across the three evaluated EEG datasets.
Motor imagery (MI) is a core paradigm in Brain-computer interface (BCI) research, but its practical application remains limited by intersubject variability and the scarcity of labeled target-domain data. Existing methods usually focus on a single adaptation level, such as domain alignment, feature interaction, or model...
We propose TSMCA-Net, a lightweight cross-session EEG-fNIRS decoder that achieves state-of-the-art accuracy with <1M parameters-70.6% fewer than the strongest baseline-while maintaining sub-8ms inference latency. TSMCA-Net decomposes multimodal interactions into three complementary views-temporal-spatial (TS), temporal...
Objective. Motor-imagery (MI) electroencephalography (EEG) offers a non-invasive route to brain–computer interaction, yet reliable single-trial decoding remains difficult because task-related patterns are weak, nonstationary, and distributed over multiple temporal scales. This study investigates whether a staged local-...
A comprehensive taxonomy of MI EEG cross-variability decoding studies from 2020 to 2025 is presented, systematically organizing advances in deep learning and transfer learning and critically evaluate core algorithmic approaches, including Convolutional Neural Networks, transformers, feature alignment, domain adaptation...
The integration of structured frequency-domain priors and attention mechanisms improves the fidelity of generated EEG samples, which in turn enhances downstream classification performance.
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