An Internet-Based Quantum Biofeedback and Neurotechnology Cybertherapy System for the Support of Transpersonal Psychology
Bibliographic record
Abstract
Transpersonal psychology is the study of human nature and proceeds on the assumption that human beings possess potentials that exceed the limits of their ego and integrate the spiritual experience within a broader understanding of the human psyche and consciousness. Altered states of consciousness have been used as psychotherapy in transpersonal psychology for decades. A Cyberpsychotherapy system is proposed to support transpersonal psychotherapy. The system can be used to induce non-ordinary state of consciousness that can be used by transpersonal psychologists as a healing tool to cure his patients with psychological problems such as psychosis at a distance location with the help of Internet technologies. The Cyberpsychotherapy system uses quantum signal generator for the induction of altered states of consciousness based on the God's helmet of Persinger (1983). The Cyberpsychotherapy has integrated an EEG that serves as a biofeedback device in order to assess if the patient has reached the desired level of consciousness and can be used it to adjust the frequencies of the signal generator in order to improve the psychotherapy experience of the patient if necessary. A sample of 10 patients was used to test the Cybertherapy system based on Neurotechnology and quantum biofeedback, data was collected and analyzed. Although the results show that the patients were not able to reach the desired level of consciousness for the psychotherapy, there is statistical significant evidence that the proposed system can help to raise the level of consciousness; this can help with future designs that will eventually assist to reach the desired level for the psychotherapy.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".