Quantum enhanced EEG classifier towards brain-controlled wheelchair navigation
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Summary
This summary is machine-generated.This study introduces a Quantum Enhanced CNN-LSTM model for classifying electroencephalography signals, achieving high accuracy for brain-computer interfaces. The novel approach enhances motor imagery decoding for assistive technologies like wheelchairs.
Area Of Science
- Neuroscience
- Computer Science
- Quantum Computing
Background
- Brain-computer interfaces (BCIs) enable assistive technologies but face challenges in accurate electroencephalography (EEG) signal classification due to noise and variability.
- Motor imagery (MI) classification is crucial for controlling devices like brain-controlled wheelchairs.
Purpose Of The Study
- To develop a hybrid Quantum Enhanced CNN-LSTM model (HQeCL) for improved EEG-based MI classification.
- To integrate diverse EEG features (frequency, spatial, non-linear) and quantum-inspired techniques for robust classification.
Main Methods
- Proposed a hybrid model (HQeCL) combining Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) with a simulated quantum pooling layer.
- Integrated Power Spectral Density (PSD), Common Spatial Patterns (CSP), and quantum entropy for comprehensive feature extraction.
- Evaluated using leave-one-subject-out (LOSO) cross-validation on an 8-channel MI dataset.
Main Results
- Achieved high performance metrics: 92.1%±5.9 accuracy, 93.1%±6.2 precision, 91.9%±1.3 recall, 92.5%±1.3 F1-score, and Cohen's κ=0.89±0.02.
- Outperformed existing methods like CSP-LDA, ShallowConvNet, and CNN-LSTM, demonstrating competitive results with QuEEGNet.
- Ablation studies confirmed the benefits of quantum pooling (+0.7% accuracy) and UMAP for feature reduction.
- Demonstrated computational efficiency with 0.12M parameters, 270.2M FLOPs, and 77.6ms inference latency.
Conclusions
- The HQeCL model offers a quantum-inspired, efficient, and high-performing solution for EEG-based motor imagery decoding.
- The approach shows near real-time feasibility in simulation, advancing the potential for advanced brain-controlled assistive technologies.
- Further research is needed for hardware implementation to fully realize the potential of this BCI advancement.

