CNN bi-LSTM Hybrid Decodes Motor-Imagery EEG

Summary The preprint was posted to arXiv on August 13, 2026, proposing a hybrid deep-learning architecture that combines a convolutional neural network with a bidirectional long short-term memory network to decode motor-imagery EEG. The author notes that motor-imagery brain-computer interfaces are seen as a promising route to flexible communication between the brain and external devices, particularly for people affected by stroke or neurodegenerative disorders, but that reliable decoding remains difficult because EEG recordings carry substantial noise and relate to underlying brain activity in complex, weakly informative ways. In the proposed architecture, the CNN learns high-level spatial and temporal representations directly from raw MI-EEG recordings, while the bi-LSTM models temporal dependencies among the extracted features. The approach was evaluated on both a publicly available dataset and a privately acquired dataset collected with an EEG acquisition system, with robust performance reported on two- and three-class motor-imagery classification and promising subject-independent decoding across the methods compared. The work is a preprint and has not been peer reviewed.