Characterizing the stimulation interference in electroencephalographic signals during brain–computer interface–controlled functional electrical stimulation therapy
Bibliographic record
Abstract
Abstract Introduction The integration of brain–computer interface (BCI) and functional electrical stimulation (FES) has brought about a new rehabilitation strategy: BCI‐controlled FES therapy or BCI‐FEST. During BCI‐FEST, the stimulation is triggered by the patient’s brain activity, often monitored using electroencephalography (EEG). Several studies have demonstrated that BCI‐FEST can improve voluntary arm and hand function after an injury, but few studies have investigated the FES interference in EEG signals during BCI‐FEST. In this study, we evaluated the effectiveness of band‐pass filters, used to extract the BCI‐relevant EEG components, in simultaneously reducing stimulation interference. Methods We used EEG data from eight participants recorded during BCI‐FEST. Additionally, we separately recorded the FES signal generated by the stimulator to estimate the spectral components of the FES interference, and extract the noise in time domain. Finally, we calculated signal‐to‐noise ratio (SNR) values before and after band‐pass filtering, for two types of movements practiced during BCI‐FEST: reaching and grasping. Results The SNR values were greater after filtering across all participants for both movement types. For reaching movements, mean SNR values increased between 1.31 dB and 36.3 dB. Similarly, for grasping movements, mean SNR values increased between 2.82 dB and 40.16 dB, after filtering. Conclusions Band‐pass filters, used to isolate EEG frequency bands for BCI application, were also effective in reducing stimulation interference. In addition, we provide a general algorithm that can be used in future studies to estimate the frequencies of FES interference as a function of the selected stimulation pulse frequency, FSTIM, and the EEG sampling rate, FS.
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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 | 0.002 |
| 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) | 0.001 | 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".