Neuroscience Software way of thinking

Problem background

Depression is a serious mental disorder characterized by persistent feelings of sadness and hopelessness, loss of interest in activities once enjoyed, lack of energy, and diminished ability to focus [1]. An estimated 17.3 million adults in the United States have had at least one major expressive episode in their life. This number represents 7.1% of all U.S. adults [2]. This disorder may result in social problems, such as family issues or difficulties at work, or physical health problems [3]; most importantly, depression is the most commonly occurring psychiatric disorder among people who die by suicide [4]. According to the World Health Organization, 800,000 people die by suicide globally every year, and this number continues to increase [5]. 

Currently, in the majority of mental health clinics, psychiatrists diagnose depression based on Diagnostic and Statistical Manual of Mental Disorders (DSM-5) diagnostic criteria or by using other self-reported measures that are prone to the subjectivity of both professionals and patients. This diagnosis method, as well as an absence of methods based on analysis of depression biomarkers, often leads to misdiagnosis and inadequate treatment; it also makes it impossible to accurately monitor pharmacological treatment outcomes [6].  

Depression screening technology

Electroencephalography (EEG) is an effective, non-invasive method of studying a patient’s brain activity. Recent studies have demonstrated that different EEG signals (linear band power, network-based features, evoked potential) and their combinations can be used as biomarkers for depression with up to 90% percent accuracy [7, 8, 9, 10, 11, 12]. However, psychiatrists are rarely trained in how to run an EEG test or analyze the collected data. 

Our goal is to provide a technical solution that will help psychiatrists understand the EEG reports and help diagnose, treat, and control the treatment outcome of a patient’s depression, thereby facilitating the work of clinicians and lowering the risk of patients’ death by suicide. 

How it all works

Our program works with most of the known clinical EEG devices. According to the number of channels of the EEG device, our AI platform analyzes available data from your device and checks for different biomarkers. Software will produce a report with a list of all identified biomarkers, based on both linear and nonlinear signal analysis, rated according to current patient condition and their probability of depression, from 1 to 10 (low probability to high). Additionally, our model will calculate the Depression Diagnosis Index (DDI) based on all provided information and all identified biomarkers.  

How it helps

Neuroscience Software technology is powerful AI/ML software that helps to do EEG-based depression screening faster and in more reliable way.

To the doctors it may help to diagnose the disorder in a more objective way, since it is often diagnosed through the use of questionnaires which are prone to professional’s and patient’s subjectivity.


  1. First, M. B. (2013). DSM-5 handbook of differential diagnosis. American Psychiatric Pub. 
  2. National Institute of Mental Health (2019). Major Depression Statistics Report. Retrieved February 09, 2021, from https://www.nimh.nih.gov/health/statistics/major-depression.shtml
  3. Braam, A. W., Prince, M. J., Beekman, A. T., Delespaul, P., Dewey, M. E., Geerlings, S. W., … & Copelan, J. R. M. (2005). Physical health and depressive symptoms in older Europeans: Results from EURODEP. The British Journal of Psychiatry, 187(1), 35-42. 
  4. Hawton, K., I Comabella, C. C., Haw, C., & Saunders, K. (2013). Risk factors for suicide in individuals with depression: A systematic review. Journal of Affective Disorders, 147(1-3), 17-28.
  5. World Health Organization. Suicide. (2019, September 02). Retrieved February 09, 2021, from https://www.who.int/news-room/fact-sheets/detail/suicide
  6. Koukopoulos, A., Sani, G., & Ghaemi, S. (2013). Mixed features of depression: Why DSM-5 is wrong (and so was DSM-IV). British Journal of Psychiatry, 203(1), 3-5. doi:10.1192/bjp.bp.112.124404 
  7. Bachmann, M., Päeske, L., Kalev, K., Aarma, K., Lehtmets, A., Ööpik, P., … & Hinrikus, H. (2018). Methods for classifying depression in single channel EEG using linear and nonlinear signal analysis. Computer Methods and Programs in Biomedicine, 155, 11-17. 
  8. Hosseinifard, B., Moradi, M. H., & Rostami, R. (2013). Classifying depression patients and normal subjects using machine learning techniques and nonlinear features from EEG signal. Computer Methods and Programs in Biomedicine, 109(3), 339-345. 
  9. Lee, P. F., Kan, D. P. X., Croarkin, P., Phang, C. K., & Doruk, D. (2018). Neurophysiological correlates of depressive symptoms in young adults: A quantitative EEG study. Journal of Clinical Neuroscience, 47, 315-322. 
  10. Mohammadi, M., Al-Azab, F., Raahemi, B., Richards, G., Jaworska, N., Smith, D., … & Knott, V. (2015). Data mining EEG signals in depression for their diagnostic value. BMC Medical Informatics and Decision Making, 15(1), 1-14. 
  11. Mahato, S., & Paul, S. (2019). Electroencephalogram (EEG) signal analysis for diagnosis of major depressive disorder (MDD): A review. Nanoelectronics, Circuits and Communication Systems, 323-335.
Back to blog list

Last posts