Bibliographic record
Abstract
Electroencephalography is an electrophysiological monitoring process to capture electrical activity on the scalp that has been shown to represent the macroscopic activity of the surface layer of the brain underneath. It is typically non-invasive, with the electrodes placed along the scalp. Computer programs in different programming language such as MATLAB, Python are used to simulate and study brain signals. This thesis focuses on utilizing Python, an open-source programming language to understand the impact of alcohol on one’s memory and attention and comparing them with non-alcoholic brain. To carry out this research, we are using open-source EEG data collected from alcoholic and non-alcoholic subjects subjected to visual stimuli. Experiments are carried out to observe spatial patterns related to both groups' brain activity and their association with different region of brain such as memory, attention, somatosensory, and emotional regulation regions. Besides the spatial pattern, we are also focusing to find source signals and their association with respect to attention region to understand the impact of alcohol on one’s attention function. Finally, the optimal sources based on optimal alpha and gamma rhythms are estimated. For these optimal source channels, we estimated time-frequency based spectrogram to understand the association of other band powers for both groups. Beta power activities from these spectrograms are analyzed for both groups to understand attention-deficit caused by alcohol consumption. By analyzing the results from the experiments can help us understand the impact of alcohol on one's brain's activity.
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 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 | 0.001 | 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) | 0.006 | 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".