Scientific Background of Mind Tracker

To get more information on electroencephalography and brain alpha activity, we recommend reading the following articles:

  1. Electroencephalogram alpha activity: modern interpretations, O. Bazanova
  2. EEG-alpha rhythms and memory processes, W. Klimesch
  3. EEG alpha and theta oscillations reflect cognitive and memory performance: a review and analysis, W. Klimesch
  4. Alpha-band oscillations, attention, and controlled access to stored information, W. Klimesch

In the Mind Tracker app, users can determine their intrinsic inclination to various activities (based on individual Alpha Wave Frequency, iAF, and individual Alpha Peak Frequency, iAPF). The algorithm for this function is based on the research by Olga Bazanova, moreover, it was developed with her direct participation.

To get more information on Alpha frequencies, check out our post or read the following research:

  1. Comments for Current Interpretation EEG Alpha Activity: A Review and Analysis, O. Bazanova
  2. EEG-alpha rhythms and memory processes, W Klimesch
  3. Relationship between individual alpha peak frequency and attentional performance in a multiple object tracking task among ice-hockey players. Yanhui Zhang, Yingzhi Lu, Dandan Wang, Chenglin Zhou, Chang Xu
  4. Current interpretation of electroencephalogram alpha activity, O. Bazanova
  5. Individual alpha frequency eeg as neurophysiological endophenotype of affective predispositions, Aftanas L.I., Tumyalis A.V.
  6. individual frequency of Alpha activity and the experience of positive and negative emotions, А. Tumyalis, V. Korenek, I. Brak, V. Makhnev, N. Reva, L. Aftans

The algorithm for diagnosing psychophysiological states (Involvement, Relaxation, Fatigue, Anxiety, Stress) is based on the following research:

  1. EEG Correlates of the Flow State: A Combination of Increased Frontal Theta and Moderate Frontocentral Alpha Rhythm in the Mental Arithmetic Task, Kenji Katahira, Yoichi Yamazaki, Chiaki Yamaoka, Hiroaki Ozaki, Sayaka Nakagawa, Noriko Nagata
  2. EEG dynamics and neural generators of psychological flow during one tightrope performance, A. Leroy & G. Cheron
  3. Modeling of Brain Cortical Activity during Relaxation and Mental Workload Tasks Based on EEG Signal Collection, Katarzyna Zemla, Grzegorz M. Wojcik, Filip Postepski, Krzysztof Wróbel, Andrzej Kawiak, Grzegorz Sedek
  4. The possible meaning of the upper and lower alpha frequency ranges for cognitive and creative tasks, H Petsche, S Kaplan, A von Stein, O Filz
  5. Using EEG spectral components to assess algorithms for detecting fatigue, Budi Thomas Jap, Sara Lal, Peter Fischer, Evangelos Bekiaris
  6. EEG-Based Estimation and Classification of Mental Fatigue, Leonard J. Trejo
  7. A New Method for Human Mental Fatigue Detection with Several EEG Channels, G. Li
  8. EEG Beta band frequency domain evaluation for assessing stress and anxiety in resting, eyes closed, basal conditions, H. Diaz
  9. Human state anxiety classification framework using EEG signals in response to exposure therapy, F. Muhammad
  10. The neural correlates of psychosocial stress: A systematic review and meta-analysis of spectral analysis EEG studies. G.Vanhollebeke

The Cognitive score metric is a complex index, measured differently in people with different iAPF. Developed in collaboration with scientific consultant O. Bazanova.

Read more on the topic:

  1. EEG rhythms and cognitive processes, S. I. Novikova
  2. The effect of long-term cognitive load on the EEG parameters, Irina S. Polikanova
  3. Recognition of the Mental Workloads of Pilots in the Cockpit Using EEG Signals, Aura Hernández-Sabaté, José Yauri, Pau Folch, Miquel Àngel Piera, Debora Gil

Establishing emotional state is based on the following research:

  1. Visual working memory recruits two functionally distinct alpha rhythms in posterior cortex, J. Rodriguez-Larios
  2. Modeling of Brain Cortical Activity during Relaxation and Mental Workload Tasks Based on EEG Signal Collection, Katarzyna Zemla, Grzegorz M. Wojcik, Filip Postepski, Krzysztof Wróbel, Andrzej Kawiak
  3. Comparison between concentration and immersion based on eeg analysis, S. Lim
  4. Anger and frontal brain activity: EEG asymmetry consistent with approach motivation despite negative affective valence, H. Harmon-Jones
  5. Frontal EEG Asymmetry and Middle Line Power Difference in Discrete Emotions, G.Zhao
  6. Exercising self-control increases relative left frontal cortical activation, Schmeichel
  7. Self-regulation and frontal EEG alpha activity during infancy and early childhood: A multilevel meta-analysis, M. Hofstee

Biofeedback alpha training is an in-house development of Waverox overseen by scientific consultants O. Bazanova and M. Lebedev.

More information on biofeedback alpha training:

  1. Intensive Neurofeedback Protocol: An Alpha Training to Improve Sleep Quality and Stress Modulation in Health Care Professionals During the Covid-19 Pandemic. A Pilot Study, Beatrice Benatti
  2. Neurofeedback training of EEG alpha rhythm enhances episodic and working memory, Jen-Jui Hsueh
  3. Neurofeedback training of the upper alpha frequency band in EEG improves cognitive performance, Benedikt Zoefel, René J. Huster, Christoph S. Herrmann
  4. EEG-based Upper-Alpha neurofeedback for cognitive enhancement in major depressive disorder: a preliminary, uncontrolled study, C Escolano
  5. Neurofeedback training improves anxiety trait and depressive symptom in GAD,Yue Hou
  6. Neurofeedback training in major depressive disorder: A systematic review of clinical efficacy, study quality and reporting practices, Lucas R Trambaiolli
  7. Comparative efficacy of targeted structural patterns of electroencephalography neurofeedback in children with inattentive or combined attention deficit hyperactivity disorder, Feng-Hua Wang
  8. Is alpha wave neurofeedback effective with randomized clinical trials in depression? A pilot study, Sung Won Choi
  9. Creativity Increases in Scientists through Alpha EEG Feedback Training, James V. Hardt
  10. Alpha EEG Feedback: Closer Parallel with Zen Than with Yoga

The Productivity function methodology is built on the study Understanding effort regulation: Comparing 'Pomodoro' breaks and self-regulated breaks, Felicitas Biwer, as well as in-house research of the Waverox company.