Investigate Schizophrenia Classification Based on EEG Electrode Reduction Using Machine Learning Techniques
Bibliographic record
Abstract
Schizophrenia is a mental disorder condition that causes patients to become distracted from reality.Over time, the patient loses his cognitive and social abilities to communicate with the outside world.Due to machine learning's strong ability to analyze complicated brain data, it has become an increasingly important tool in recent years.This study considers the brain's neurologic signals in the resting state in two scenarios to classify schizophrenia disease by electroencephalography (EEG).The performed scenarios were to investigate the impact of selecting electrodes randomly (5 electrodes and 8 electrodes) and comparing it with applying the principal component analysis (PCA), utilizing four algorithms to extract features: Fast Fourier Transform (FFT), Approximate Entropy (ApEn), Log Energy Entropy (LogEn), and Shannon Entropy (ShnEn).We used publicly available datasets with 19 EEG channels consisting of two classes, which are schizophrenia and health control, using a onesecond epoch window size.We applied a band-pass filter to decompose the EEG signals into five sub-bands.Also, the L2-normalization method has been applied to the derived features, which positively impacted the outcomes.The features were applied to three classifiers named K-nearest neighbor (KNN), support vector machine (SVM), and quadratic discriminant analysis (QDA).From all the scenarios, the five-electrode with random selection showed remarkable results of 99% using the SVM classifier in all evaluation metrics with LogEn+ Bandpass features.
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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 | 0.001 | 0.001 |
| 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) | 0.001 | 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 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".