Motor Imagery Decoding Enhancement Based on Hybrid EEG-fNIRS Signals

This study explores the combination of electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) to enhance the decoding performance of motor imagery (MI) tasks for brain-computer interface (BCI). The experiment involved measuring 64 channels of EEG signals and 20 channels of fNIRS signals simultaneously during a task of the left-right hand MI. By combining these two types of signals, the study aimed to understand how feature fusion affected classification accuracy for MI. The EEG signals were filtered into three bands (
$\theta $
: 4–7 Hz,
$\alpha $
: 8–13 Hz,
$\beta $
: 14–30 Hz), while the fNIRS signals were filtered into 0.02-0.08 Hz to improve signal quality for subsequent analysis. The common spatial patterns (CSP) algorithm was utilized to extract features from both EEG and fNIRS signals. This allowed the researchers to create a fused signal with both EEG and fNIRS features that could then be processed using principal component analysis (PCA). Finally, the processed data was fed into a support vector machine (SVM) classifier, which improved the mean accuracy rate of MI to 92.25%. By comparing the classification accuracies obtained with fused and unfused segments of EEG and fNIRS signals, the study discovered that fusing the signals significantly improved classification accuracy by 5%-10%. Furthermore, analyzing the activated brain regions using fNIRS showed that the auxiliary motor cortex was significantly activated during MI. These results demonstrate that hybrid signals with a fusion strategy can enhance the stability and fault tolerance in BCI systems, making them valuable for practical applications.