Low Power FIR Filter Bank for EEG Processing Using Frequency-Response Masking Technique
Bibliographic record
Abstract
Different frequency bands in an electroencephalogram (EEG) signal contain different information. It is very helpful to divide an EEG signal by its sub-bands before applying further classification. FIR filter is one of the best choices for processing EEG signal because of its linear phase property. However, the implementation of an FIR filter requires more multipliers compared to its IIR counterpart. With frequency-response masking (FRM) technique, the multipliers needed to implement FIR filter can be reduced dramatically leading to a low power design. This paper proposes a filter bank structure for processing EEG signal based on the FRM technique. The design equations for all the sub-filters are derived and the condition for applying the proposed structure is given. A design example is included to illustrate the effectiveness of the proposed filter. It shows that the filter can fulfill the design objectives with 77% less multipliers comparing to the conventional FIR filter synthesizing technique.
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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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".