Signal Analysis Techniques for Resource Optimization in Brain-Computer Interfaces and Other Wearables
Bibliographic record
Abstract
Stroke is a serious neurological illness that often leads to motor dysfunction and affects the daily life activities of an individual. Although stroke is a complex medical issue, there is a way to reduce its impact by post-stroke neural rehabilitation. Brain-computer interfaces (BCIs) establishes a real-time interaction between the patients and rehabilitation devices and help stroke patients to restore their lost motor function. However, the existing BCIs have relatively high power consumption, more computational complexity, and large processing time, and therefore, limiting their effectiveness to enhance a patient’s health. The advances in signal processing and machine learning can help us in developing low-power and low-complexity solutions to address the aforementioned problems. Therefore, this dissertation focuses on proposing advanced signal analysis techniques for resource optimization in brain-computer interfaces and other wearables. The first part of this dissertation focuses on developing a patient-specific electroencephalogram (EEG) channel selection methods for motor-imagery (MI) classification using Nonnegative Matrix Factorization (NMF) which is capable of reducing computational complexity and processing time. In addition, variance activated NMF for EEG channel selection is proposed to further reduce the system complexity and effective localization of cognition during a motor task. In the MI classification framework, the theory of Riemannian geometry is utilized for feature extraction from the reduced set of EEG channels, and the neighborhood component-based feature selection algorithm is employed for feature selection. The second part of this dissertation focuses on proposing the idea of reconstruction-free compressive feature learning for wireless BCIs to leverage the resources of current BCIs by reducing the power consumption by extracting the features directly from the compressed measurements. The developed methods for low-power BCIs are validated using three MI datasets. The experimental results in this dissertation show that the developed solutions are capable of reducing the total processing time up to 95 %, the total power required up to 50 %, and the system complexity up to 75 %. Lastly, it is shown that the NMF and Compressive Sensing (CS)-based approaches developed in this dissertation are also useful for any resource constraint wearable for other low-power and real-time applications.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".