Brain-Computer Interfaces (BCIs) for Neurorehabilitation in Ghana

Abstract

Brain-Computer Interfaces (BCIs) hold immense promise for restoring function in patients with neurological disorders. However, the high cost, extensive calibration, and fragility of current systems make them largely inaccessible in resource-constrained settings like Ghana. Stroke, a leading cause of long-term disability in Ghana, requires effective and affordable rehabilitation solutions. This Masters in Computer Science proposal outlines a research agenda to develop a low-cost, robust, non-invasive BCI system for post-stroke motor rehabilitation, centered on a novel neural decoding paradigm that is resilient to the noise and data-shift inherent in real-world clinical applications in Ghana.

Key Research Questions for BCIs in Ghana

1. Cross-Subject Generalization: How can we develop a neural decoding model that generalizes effectively across different individuals, minimizing the need for lengthy, expert-led calibration for each new patient? This is critical for scalability in Ghanaian hospitals where neurologist-to-patient ratios are low.
2. Robustness to Artifacts: How can BCI algorithms be designed to be robust to environmental and physiological artifacts (e.g., power-line noise, muscle movements) that are common in non-ideal settings?
3. Data Scarcity: Neural data from African populations is severely underrepresented in global datasets. How can we use self-supervised and unsupervised learning techniques to build effective models from limited, unlabeled EEG data collected locally in Ghana?
4. Clinical Viability and Usability: How can we design a BCI system that is not just technically functional but also usable and motivating for patients and therapists in a typical Ghanaian physiotherapy department?

Proposed Masters in Computer Science Research: Self-Supervised Learning for a Zero-Calibration Motor Imagery BCI

The core of this thesis is to develop and validate a self-supervised learning framework for pre-training a deep neural network that can extract robust, subject-invariant features from raw EEG signals. This pre-trained model would serve as a universal feature extractor, allowing a new BCI user to achieve high performance with zero or minimal calibration.

Methods:

• Data Collection: In partnership with a Ghanaian hospital (e.g., Korle-Bu Teaching Hospital), a dataset of multi-channel EEG data will be collected from a cohort of healthy Ghanaian volunteers during motor imagery tasks. This will form the basis for a foundational Ghanaian EEG dataset.
• Self-Supervised Pre-Training: A contrastive learning approach will be used. The model (e.g., a hybrid convolutional and recurrent neural network) will be trained to learn representations that are invariant to subject-specific idiosyncrasies but sensitive to the underlying neural task (e.g., distinguishing "imagine left hand movement" from "right hand movement").
• Fine-Tuning and Evaluation: The pre-trained model will be evaluated in a "zero-shot" or "few-shot" setting on unseen subjects, including stroke patients. Its performance (decoding accuracy) will be benchmarked against traditional BCI methods that require full calibration.
• System Integration: The final model will be integrated into a simple, tablet-based neurofeedback game, providing patients with real-time visual feedback to guide their motor rehabilitation exercises. The entire system will be built using open-source hardware (e.g., OpenBCI) and software to ensure accessibility.

Expected Outcomes and Impact

This research is expected to produce a novel, robust decoding algorithm that significantly lowers the barrier to entry for clinical BCI use. A successful project would demonstrate a cost-effective and scalable neurorehabilitation tool that is specifically validated for a Ghanaian population. By open-sourcing the models, dataset, and system design, this work will empower other researchers in Africa to build upon this foundation, fostering a local ecosystem of BCI innovation. Ethically, the project will adhere to strict protocols for patient consent and data privacy, ensuring that this sensitive neural data is used for the benefit of the community from which it was gathered.