Quantum enhanced EEG classifier towards brain-controlled wheelchair navigation

Prabhat Kumar Upadhyay ¹, Kumar Avinash Chandra ¹

⁰Department of Electrical & Electronics Engineering, Birla Institute of Technology, Mesra, Ranchi, Jharkhand, India.

Neuroscience +

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November 9, 2025

View abstract on PubMed

Summary

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.

Keywords:

Brain-computer interface (BCI) Convolutional neural network (CNN) Electroencephalography (EEG) Long short term memory (LSTM) Motor imagery (MI) Quantum computing