The Use of SPOD and Spherical Harmonics for the Analysis of EEG Data
Bibliographic record
Abstract
The assessment of mental workload from electroencephalogram (EEG) data for brain-computer interfaces (BCI) poses some challenges due to the interaction of spatial and temporal features within the data. Similar challenges are well known in the analysis of turbulent flows, which exhibit complex space-time correlations resulting from large-scale structures. The similarities motivated us to conduct this feasibility work of applying analytic methods of fluid dynamics – i.e., a scheme combining spectral proper orthogonal decomposition (SPOD) and spherical harmonics as basis functions – to EEG data for identifying features representing mental states. Based on EEG data of an existing BCI Hackathon, the scheme yielded some relevant features across subjects and sessions by relying only on a Fourier transform in time and the basis functions in space. The features were then classified by employing a conventional support vector machine algorithm to produce an accuracy comparable to those reported in a previous study on the same EEG data. This performance comparability indicates the scheme's potential for analyzing EEG data in BCI applications. Nevertheless, future work is needed to select specific features as general indicators of mental workload.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".