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EEG p-adic quantum potential accurately identifies depression, schizophrenia and cognitive decline

Cite this dataset

Benninger, Felix et al. (2022). EEG p-adic quantum potential accurately identifies depression, schizophrenia and cognitive decline [Dataset]. Dryad.


No diagnostic or predictive instruments to help with early diagnosis and timely therapeutic intervention are available as yet for most neuro-psychiatric disorders. A quantum potential mean and variability score (qpmvs), to identify neuropsychiatric and neurocognitive disorders with high accuracy, based on routine EEG recordings, was developed. Information processing in the brain is assumed to involve integration of neuronal activity in various areas of the brain. Thus, the presumed quantum-like structure allows quantification of connectivity as a function of space and time (locality) as well as of instantaneous quantum-like effects in information space (non-locality). EEG signals reflect the holistic (nonseparable) function of the brain, including the highly ordered hierarchy of the brain, expressed by the quantum potential according to Bohmian mechanics, combined with dendrogram representation of data and p-adic numbers. Participants consisted of 230 participants including 28 with major depression, 42 with schizophrenia, 65 with cognitive impairment, and 95 controls. Routine EEG recordings were used for the calculation of qpmvs based on ultrametric analyses, closely coupled with p-adic numbers and quantum theory. Based on area under the curve, high accuracy was obtained in separating healthy controls from those diagnosed with schizophrenia (p<0.0001), depression (p<0.0001), Alzheimer’s disease (AD; p<0.0001), and mild cognitive impairment (MCI; p<0.0001) as well as in differentiating participants with schizophrenia from those with depression (p<0.0001), AD (p<0.0001) or MCI (p<0.0001) and in differentiating people with depression from those with AD (p<0.0001) or MCI (p<0.0001). The novel EEG analytic algorithm (qpmvs) seems to be a useful and sufficiently accurate tool for diagnosis of neuropsychiatric and neurocognitive diseases and may be able to predict disease course and response to treatment.


Online medical health records from two medical centres were used to identify all participants that underwent at least one routine EEG examination between the years 2011 and 2019. The participants were then divided into the following groups:  

  1. Depression: Participants with a diagnosis of major depressive disorder (MDD), hospitalized during the index time. This diagnosis had been established by two senior psychiatrists according to DSM-IV and DSM-V criteria, following a psychiatric interview where the severity of depression was found to be at least moderate. In addition, the participants (range: 33–91 years; average age: 69.7 ±14.8 years; 20 females) had to have had at least one previous major depressive episode, prior to age 30, namely, the index episode was a recurrent one.   
  2. Schizophrenia: Diagnosis of schizophrenia had been established by two senior psychiatrists according to the ICD-10 criteria. In addition, the participant had to be hospitalized during the index time.  
  3. Cognitive impairment: Participants in the study had been diagnosed by two senior neurologists, with either MCI or AD according to the criteria of the National Institute on Aging and the Alzheimer’s Association.
  4. Controls: Participants undergoing routine EEG due to indications unrelated to neuropsychiatric diseases. None of the participants in this group had been diagnosed with any condition defining any of the other groups. In this group, exclusion criteria also included diagnosis of bipolar disorder; substance abuse, psychiatric or general medical conditions requiring hospitalization, history of epilepsy or conditions requiring anticonvulsants, ECT, vagal nerve stimulation, or transcranial magnetic stimulation (TMS), history of traumatic brain injury and history or imaging findings of cerebrovascular diseases including ischaemic and haemorrhagic stroke.

EEG data acquisition and analysis

Routine EEG recordings were retrospectively obtained from the medical records of all patients. EEGs had been performed in a routine clinical setting by an experienced technician. All included participants had undergone EEG between 8 am and 1 pm using a Nihon Koden surface EEG (19-electrode standard according to the international 10-20 electrode placement system) with a sampling frequency of 500 Hz (Nihon Kohden, Japan). Participants had been resting with open and closed eyes. Those who underwent sleep-EEGs were excluded.

Usage notes

Matlab is needed to read the files