Northwestern Polytechnical University Diffusion Network Cleans EEG Artifacts in 17 ms
EEG is easily contaminated by blinks, muscle activity and heartbeats, which can drag down how accurately a brain-computer interface reads intent. Researchers from the School of Automation and the School of Software at Northwestern Polytechnical University in Xi'an, northwestern China, built Diff-ADN, a single-channel denoiser that first reconstructs the signal according to contamination severity, then applies one diffusion-guided residual correction. It handles ocular, muscular, cardiac and mixed artifacts, reaching mean Pearson correlations with clean EEG of 0.839 to 0.936 at contamination levels from -7 to +2 dB. Across 4 public motor-imagery datasets, removing cardiac artifacts recovered 4.88 to 5.59 percentage points of decoding accuracy, up to 21.90 points in a single case, and a 2-second segment took 12.52 to 17.08 ms on the tested CPU. Better waveform reconstruction did not always translate into equal gains in decoding accuracy.