What is Attention?

Our brainwave changes according to what we are doing. When you focus your mind, studies have shown that the high-frequency energy of brain waves increases, and the low-frequency energy decreases.
 
Flowtime collects real-time brainwaves through the headband and measures the user's attention level. Based on extensive data analysis, the Attention algorithm calculates attention by analyzing the spectral characteristics of brainwaves. If the value turns higher, it means your brain is more active.
 
Many types of meditation need you to be focused on things such as your breath or an object. If you realize you are distracted, you need to come back and focus your mind on it again.

Brainwave frequency varies from person to person. The algorithm needs to set a baseline for you at the beginning of meditation. Please minimize your face movements like blinking, gritting teeth, or moving eyes within the 30s after you start to meditate to have an accurate baseline.


How to understand the Attention graph?

Many types of meditation need you to be focused on things such as your breath or an object. If you realize you are distracted, you need to come back and focus your mind on it again. The Attention index shows how much you are focused and how it changes. Without long-term practice, the Attention index is hard to keep high, generally up and down. By practicing meditation, you can keep focused at a high level and stay concentrated longer.

Example: Meditation beginner's attention (midlevel, up and down).

After long-term practice, the veteran's attention (increasing soon and keeping on at a high level).

 

References 

1. Wang, Yu-Kai & Jung, Tzyy-Ping & Lin, Chin-Teng. (2015). EEG-Based Attention Tracking During Distracted Driving. IEEE transactions on neural systems and rehabilitation engineering: a publication of the IEEE Engineering in Medicine and Biology Society. 23. 10.1109/TNSRE.2015.2415520.
2. Xu, Lu-Qiang & Liu, Jing-Xia & Xiao, Guang-Can & Jin, Wei-Dong. (2013). Characterization and classification of EEG attention level. Journal of Computer Applications. 32. 3268-3270. 10.3724/SP.J.1087.2012.03268.
3. Lutsyuk, N. & Éismont, E. & Pavlenko, Vladimir. (2006). Modulation of attention in healthy children using a course of EEG-feedback sessions. Neurophysiology. 38. 389-395. 10.1007/s11062-006-0076-0.
4. Lansbergen, Marieke & Arns, Martijn & Dongen-Boomsma, Martine & Spronk, Desirée & Buitelaar, Jan. (2011). The increase in theta/beta ratio on resting-state EEG in boys with attention-deficit/hyperactivity disorder is mediated by slow alpha peak frequency. Progress in neuro-psychopharmacology & biological psychiatry. 35. 47-52. 10.1016/j.pnpbp.2010.08.004.
5. Putman, Peter & Verkuil, Bart & Arias-Garcia, Elsa & Pantazi, Ioanna & van Schie, Charlotte. (2013). Erratum to: EEG theta/beta ratio as a potential biomarker for attentional control and resilience against deleterious effects of stress on attention. Cognitive, affective & behavioral neuroscience. 14. 10.3758/s13415-013-0238-7.
September 05, 2022 — Flowtime Team
Tags: Glossary