Deep Convolutional Architecture with discriminative feature visualization for analysis of EEG data
Bibliographic record
Abstract
Analysis of Electroencephalography (EEG) data to improve understanding of underlying neural activity is typically hypothesis-driven and requires the investigator to quantify certain features of the EEG time-series. However, this could lead to sub-optimal feature selection. Data-driven approaches like deep learning (DL) allow discovery of the optimal feature set from available data. Convolutional Neural Networks (CNNs) have been recently used for classification tasks in neuroscience[1]. To visualize discriminative features that guide the network's decision, we use a method called cue-combination for Class Activation Map (ccCAM) which is inspired by existing methods in literature[2] and adapted for neuroscience applications. Specifically, a deep CNN architecture, combined with ccCAM is applied on EEG data collected from human subjects to study the effect of exercise on motor learning. Our results reveal discriminative features within specific frequency band (19-31 Hz) that is a subset of the beta-band, which has been found to be significantly modulated by exercise in previous studies[3]. They also reveal that activity in this frequency band propagates across different regions of the cortex while performing a fixed force hand-grip task. Collectively, our results demonstrate the potential of DL frameworks for identifying a more informative feature space in neuroimaging data in a completely data-driven manner, which can in turn yield a better understanding of brain function.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".