Why this work is in the frame
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Bibliographic record
Abstract
Emotion states greatly influence many areas in our daily lives, such as: learning, decision making and interaction with others. Therefore, the ability to detect and recognize one's emotional states is essential in intelligence Human Machine Interaction (HMI). The aim of this study was to develop a new system that can sense and communicate emotion changes expressed by the Central Nervous System (CNS) through the use of EEG signals. More specifically, this study was carried out to develop an EEG-based subject-dependent affect recognition system to quantitatively measure and categorize three affect states: Positively excited, neutral and negatively excited. In this paper, we discussed implementation issues associated with each key stage of a fully automated affect recognition system: emotion elicitation protocol, feature extraction and classification. EEG recordings from 5 subjects with IAPS images as stimuli from the eNTERFACE06 database were used for simulation purposes. Discriminating features were extracted in both time and frequency domains (statistical, narrow-band, HOC, and wavelet entropy) to better understand the oscillatory nature of the brain waves. Through the use of k Nearest Neighbor classifier (kNN), we obtained mean correct classification rates of 90.77% on the three emotion classes when K equals 5. This demonstrated the feasibility of brain waves as a mean to categorize a user's emotion state. Secondly, we also assessed the suitability of commercially available EEG headsets such as Emotive Epoc for emotion recognition applications. This study was carried out by comparing the sensor location, signal integrity with those of Biosemi Active II. A new set of recognition performance was presented with reduced number of channels.
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.
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 | 0.000 | 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) | 0.031 | 0.005 |
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 it