Obtained 19 lead EEG data from 50 chronic pain patients along with their clinical history and self-reported pain scored. Machine learning was used to analyze the EEG data and derive a chronic pain biomarker.

Inclusion Criteria:

• Male and female chronic pain patients
• Patients between the ages of 18-85 years
• Patients exhibiting the presence of symptoms in excess of 3 months duration
• Patients suffering from neuropathic (e.g., lower back pain), osteoarthritis, or muscular skeletal pain
• Patients with evidence of pathology related to the painful condition on which diagnosis was made (e.g., results of imaging or diagnostic pain code)
• Patients with NRS pain scores across the full range (1-10) at the time of testing

Exclusion Criteria:

• Patients with medically diagnosed psychotic illness
• Patients with medically diagnosed drug or alcohol dependence in the past 12 months
• Patients with a medical history of head injury with loss of consciousness and amnesia (within the last 2 years)
• Patients with skull abnormalities that preclude the proper placement of the electrodes for the EEG data acquisition
• Patients who have a spinal cord stimulator, or other implantable devices
• Patients for whom the source of pain at the time of the evaluation is associated with: neurological disorders (multiple sclerosis, Parkinson, dementia), diabetes, migraines, or those with reflex / sympathetic dystrophy disorder/complex regional pain syndrome, fibromyalgia, or visceral pain
• Patients with cancer
• Patients on workers compensation or disability
• Patient on anticonvulsant medication
• Patients who have a history of seizures

Fifty (50) male and female pain patients with symptoms in excess of 3 months duration (per the IASP definition of Chronic Pain) between the ages of 18-85 years were enrolled. Subjects who met the inclusion/exclusion criteria were asked to volunteer for the study.