A collaborative team from Endicott College and Woosong University presents a hybrid CNN-LSTM deep learning architecture to enhance EEG-based motor imagery classification in BCI systems. By fusing convolutional spatial feature extraction with recurrent temporal modeling and augmenting training data via GANs, the approach achieves over 96% accuracy, paving the way for more reliable assistive technologies.

Key points

• Hybrid CNN-LSTM model combines convolutional layers for spatial feature extraction with LSTM units for temporal modeling, achieving 96.06% accuracy on motor imagery EEG classification.
• GAN-based data augmentation generates synthetic EEG samples to balance training data, reducing overfitting and improving generalization across participants.
• Advanced preprocessing (bandpass and spatial filtering), wavelet transforms, and Riemannian geometry feature extraction across six sensorimotor ROIs yield robust input representations.

Why it matters: This hybrid deep learning approach sets a new benchmark for EEG-based BCI accuracy, unlocking more reliable motor-impaired user control and accelerating neurotechnology applications.