A Quantum Biofeedback and Neurotechnology Cybertherapy System for the Support of Transpersonal Psychotherapy
Bibliographic record
Abstract
Transpersonal psychology is the study of human nature and rests 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 to aid psychotherapy by transpersonalists for decades. A cyberpsychotherapy system is proposed to support transpersonal psychotherapy. The system can be used to induce a non-ordinary state of consciousness that can be used by transpersonal psychologists as a healing tool to treat patients with psychological problems such as psychosis. With the help of internet technology, these treatment sessions can occur over great distances. The cyberpsychotherapy system uses a quantum signal generator for the induction of altered states of consciousness, based on the so-called Koren Helmet of Persinger’s (1983). The cyberpsychotherapy has integrated EEG which serves as a biofeedback device in order to assess if the patient has reached the desired level of consciousness. Additionally, this EEG measurement can be used to inform the adjustment of the signal generator frequency 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 analysed to confirm the system’s efficacy. Although the results show that the patients were not able to reach the desired level of consciousness for the psychotherapy, there was statistically significant evidence that the proposed system can alter an individual’s level of consciousness, which may help inform future designs intended to induce a state of consciousness most conducive to 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.010 | 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".