Real-time feature extraction for multi-channel EEG signals time-frequency analysis
Bibliographic record
Abstract
This paper presents a model-based Field Programmable Gates Array (FPGA) design for real-time feature extraction of Electroencephalogram (EEG) signals, which can be used for brainwaves bands classification to track and detect mental status in Brain Computer Interface (BCI) applications and consciousness studies. An model-based design approach is used to implement Short-time Fourier Transform (STFT) and extract 20 frequency feature components for classification. These 20 features are divided into 5 groups corresponding to 5 different brainwaves bands. Each feature is defined as the average power spectrum of a number of adjacent frequency components. A hardware model is designed using Xilinx System Generator and implemented on FPGA. Fixed-point is used instead of floating-point to increase operating speed for meeting timing requirement of the real-time system. The design is implemented on a Xilinx Zedboard at 50 MHz clock rate, and can be used for up to 128-channel EEG signals feature extraction at 250 Hz sample rate.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".