Examining Brainwave Patterns in Response to Familiar Music: An EEG and Machine Learning Approach
Bibliographic record
Abstract
Detecting familiar music using brainwaves through machine learning can be pivotal for innovative therapeutic devices benefiting dementia patients' memory and communication. We applied various machine learning algorithms, including Random Forest (RF), Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), K-Nearest Neighbor (KNN), and Deep Learning (DL), to EEG data from a mobile headset's Fp2 channel. EEG data from 20 participants assessing familiarity with 20 Christmas carols was collected. For ML methods (excluding DL), specific frequency bands were extracted (theta, alpha, low beta, and high beta), and six statistical features served as input for training ML classifiers. DL method used spectrograms and 2D convolutional neural networks. SVM with only kurtosis features reached the highest accuracy at 66.9%. Since there are variations among the participants, individualized training and testing resulted in an average accuracy of 72.3%. These results suggest promising avenues for therapeutic strategies in dementia care.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 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".