CCA-Based Compressive Sensing for SSVEP-Based Brain-Computer Interfaces to Command a Robotic Wheelchair
Bibliographic record
Abstract
People with severe physical disabilities are not able of using standard robotic wheelchairs, which generally demand some motor skills, and therefore total usage of associate muscles. Robotic wheelchairs commanded by brain-computer interfaces (BCIs) based on electroencephalography have demonstrated to be an alternative for these end-users. In general, existing robotic wheelchairs commanded by BCIs require special platforms adapted to the EEG-BCI, and end-users need to attend a long training process to safely drive these devices. But many times these potential users do not have access to training sessions; due to mobility problems or technology access restrictions. This study proposes an EEG-based BCI with a customizable configuration to be used in cloud architectures for the remote control of robotic wheelchairs. This research explores two types of steady-state visual evoked potential (SSVEP)-based BCI by applying canonical correlation analysis (CCA) and compressive sensing (CS) as a novelty, adopting one free calibration, and the other including a calibration stage. The free-calibrated SSVEP recognition approach (CS-ncCCA) using compression ratio (CR) at 60% obtained accuracy (ACC) of 85% and information transfer rate (ITR) of 102 bits per minute (b/min), whereas the calibrated BCI (CS-wcCCA) applying also CR at 62% achieved ACC of 85% and ITR of 195 b/min. As a highlight, the proposed BCI allows a significant reduction of the transmitted file size (TFS) and improves the communication latency that may be useful in remote and cloud robotics applications, such as for users with severe motor disabilities, to train driving safely robotic wheelchairs via Internet of Things (IoT).
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".