The science
behind Pylot
In 2019, the Pylot team set out to track mental performance using consumer-grade sensors with the goal of explaining why some of the team often struggled with deep work in the afternoon.
Pylot's research included over 100 hours of controlled trials, validated on thousands of hours of real world testing.
The sensors
Scientific research has shown the link between electroencephalogram (EEG) and mental fatigue from the 1990s (Okogbaa, 1994). The correlation between heart rate variability (HRV) and mental fatigue was also highlighted in research as early as 1992 (Mascord, 1992).
Pylot uses NeuroSky’s TGAM chip which is a single-channel dry EEG, located at Fp1. This device is valid for consumer use (Rieiro, 2019). Additionally, Morales (2017) concluded that “our results suggest that the TGAM-based chip EEG device is able to detect changes in mental state while performing a complex and dynamic everyday task“ and Rieiro (2019) states it “provides stable recordings even through long periods of time”.
To measure heart rate variability, Pylot uses photoplethysmography (PPG), or an optical sensor, on the forehead. PPG located on the forehead has been shown to be a suitable alternative to ECG and heart rate variability for consumer use (Sun, 2019 and Peralta, 2017).
Sample of HRV from forehead PPG compared to chest strap
Flow
Pylot defines Flow as a state of relaxed awareness. This state has been associated with alpha brain wave activity captured from EEG (Mikicin, 2015). Pylot's flow uses this relationship as the basis for the measurement.
Research has shown increased alpha brain wave activity when subjects are in a flow state (Katahira, 2018 and Knierim, 2019). Further, task performance is optimal when alpha activity is increased (Jann, 2010).
Pylot's research showed a relationship between flow and performance. Response time in a mental challenge showed a moderate difference between low and high flow levels. Trials on gamers also showed a 13% higher win rate when flow was high.
League of Legends win rate by Flow category (n=1634)
Mental Fatigue
Pylot had 41 subjects conduct a one-hour, repeated mental task to identify changes in EEG and HRV based on fatigue. The resulting fatigue model was over 82% accurate in predicting changes in mental fatigue throughout this test.
References and further reading
Green, C.S., Bavelier, D. 2006, “Enumeration versus multiple object tracking: the case of action video game players”, Cognition, 101 (1), 217-245.
Jann, K., Koenig, T., Dierks, T., Boesch, C., and Federspiel, A. 2010, “Association of individual resting state EEG alpha frequency and cerebral blood flow”, NeuroImage, Volume 51, Issue 1.
Katahira, K., Yamazaki, Y., Yamaoka, C., Ozaki, H., Nakagawa, S, Nagata, N., 2018, “EEG Correlates of the Flow State: A Combination of Increased Frontal Theta and Moderate Frontocentral Alpha Rhythm in the Mental Arithmetic Task”, Frontiers in Psychology, 9, 300
Knierim M, Nadj M, and Weinhardt C. 2019, “Flow and Optimal Difficulty in the Portable EEG: On the Potentiality of using Personalized Frequency Ranges for State Detection”. 3rd International Conference on Computer-Human Interaction Research and Applications, 2019.
Mascord DJ, Heath RA., 1992, “Behavioral and physiological indices of fatigue in a visual tracking task”, J. of Safety Research, 23(1), 19–25.
Mikicin, M., Kowalczyk, M., 2015, “Audio-Visual and Autogenic Relaxation Alter Amplitude of Alpha EEG Band, Causing Improvements in Mental Work Performance in Athletes”. Appl Psychophysiol Biofeedback 40, 219–227.
Morales, J., Diaz-Piedra, C. Rieiro, H. Roca-González, J., Romero, S., Catena, A., Fuentes, L.J., and Di Stasi, L. L. 2017. “Monitoring driver fatigue using a single-channel electroencephalographic device: A validation study by gaze-based, driving performance, and subjective data”, Accident Analysis & Prevention, Volume 109, Pages 62-69
Okogbaa, O.G., Shell, R.L., Filipusic, D., 1994, “On the investigation of the neurophysiological correlates of knowledge worker mental fatigue using the EEG signal”, Applied Ergonomics, vol. 25 (6), 355–365.
Peralta, E., Lázaro, J., Gil, E., Bailón R., and Marozas, V., 2017, "Robust pulse rate variability analysis from reflection and transmission photoplethysmographic signals," Computing in Cardiology (CinC), Rennes, 1-4.
Rieiro, H., Diaz-Piedra, C., Morales, J.M., Catena, A. et al., 2019, “Validation of Electroencephalographic Recordings Obtained with a Consumer-Grade, Single Dry Electrode, Low-Cost Device: A Comparative Study”, Sensors, 19 (12), 2808
Shevelev, I.A., Kostelianetz, N.B., Kamenkovich, V.M., Sharaev, G.A., 1991, “EEG alpha-wave in the visual cortex: check of the hypothesis of the scanning process”, Int J. of Psychophysiology, vol. 11 (2), 195-201.
Sun, S., Peeters, W.H., Bezemer, R. et al. 2019, “Finger and forehead photoplethysmography-derived pulse-pressure variation and the benefits of baseline correction”. J Clin Monit Comput 33, 65–75.
The science
behind Pylot
In 2019, the Pylot team set out to track mental performance using consumer-grade sensors with the goal of explaining why some of the team often struggled with deep work in the afternoon.
Pylot's research included over 100 hours of controlled trials, validated on thousands of hours of real world testing.
The sensors
Scientific research has shown the link between electroencephalogram (EEG) and mental fatigue from the 1990s (Okogbaa, 1994). The correlation between heart rate variability (HRV) and mental fatigue was also highlighted in research as early as 1992 (Mascord, 1992).
Pylot uses NeuroSky’s TGAM chip which is a single-channel dry EEG, located at Fp1. This device is valid for consumer use (Rieiro, 2019). Additionally, Morales (2017) concluded that “our results suggest that the TGAM-based chip EEG device is able to detect changes in mental state while performing a complex and dynamic everyday task“ and Rieiro (2019) states it “provides stable recordings even through long periods of time”.
To measure heart rate variability, Pylot uses photoplethysmography (PPG), or an optical sensor, on the forehead. PPG located on the forehead has been shown to be a suitable alternative to ECG and heart rate variability for consumer use (Sun, 2019 and Peralta, 2017).
Sample of HRV from forehead PPG compared to chest strap
Flow
Pylot defines Flow as a state of relaxed awareness. This state has been associated with alpha brain wave activity captured from EEG (Mikicin, 2015). Pylot's flow uses this relationship as the basis for the measurement.
Research has shown increased alpha brain wave activity when subjects are in a flow state (Katahira, 2018 and Knierim, 2019). Further, task performance is optimal when alpha activity is increased (Jann, 2010).
Pylot's research showed a relationship between flow and performance. Response time in a mental challenge showed a moderate difference between low and high flow levels. Trials on gamers also showed a 13% higher win rate when flow was high.
League of Legends win rate by Flow category (n=1634)
Mental Fatigue
Pylot had 41 subjects conduct a one-hour, repeated mental task to identify changes in EEG and HRV based on fatigue. The resulting fatigue model was over 82% accurate in predicting changes in mental fatigue throughout this test.
References and further reading
Green, C.S., Bavelier, D. 2006, “Enumeration versus multiple object tracking: the case of action video game players”, Cognition, 101 (1), 217-245.
Jann, K., Koenig, T., Dierks, T., Boesch, C., and Federspiel, A. 2010, “Association of individual resting state EEG alpha frequency and cerebral blood flow”, NeuroImage, Volume 51, Issue 1.
Katahira, K., Yamazaki, Y., Yamaoka, C., Ozaki, H., Nakagawa, S, Nagata, N., 2018, “EEG Correlates of the Flow State: A Combination of Increased Frontal Theta and Moderate Frontocentral Alpha Rhythm in the Mental Arithmetic Task”, Frontiers in Psychology, 9, 300
Knierim M, Nadj M, and Weinhardt C. 2019, “Flow and Optimal Difficulty in the Portable EEG: On the Potentiality of using Personalized Frequency Ranges for State Detection”. 3rd International Conference on Computer-Human Interaction Research and Applications, 2019.
Mascord DJ, Heath RA., 1992, “Behavioral and physiological indices of fatigue in a visual tracking task”, J. of Safety Research, 23(1), 19–25.
Mikicin, M., Kowalczyk, M., 2015, “Audio-Visual and Autogenic Relaxation Alter Amplitude of Alpha EEG Band, Causing Improvements in Mental Work Performance in Athletes”. Appl Psychophysiol Biofeedback 40, 219–227.
Morales, J., Diaz-Piedra, C. Rieiro, H. Roca-González, J., Romero, S., Catena, A., Fuentes, L.J., and Di Stasi, L. L. 2017. “Monitoring driver fatigue using a single-channel electroencephalographic device: A validation study by gaze-based, driving performance, and subjective data”, Accident Analysis & Prevention, Volume 109, Pages 62-69
Okogbaa, O.G., Shell, R.L., Filipusic, D., 1994, “On the investigation of the neurophysiological correlates of knowledge worker mental fatigue using the EEG signal”, Applied Ergonomics, vol. 25 (6), 355–365.
Peralta, E., Lázaro, J., Gil, E., Bailón R., and Marozas, V., 2017, "Robust pulse rate variability analysis from reflection and transmission photoplethysmographic signals," Computing in Cardiology (CinC), Rennes, 1-4.
Rieiro, H., Diaz-Piedra, C., Morales, J.M., Catena, A. et al., 2019, “Validation of Electroencephalographic Recordings Obtained with a Consumer-Grade, Single Dry Electrode, Low-Cost Device: A Comparative Study”, Sensors, 19 (12), 2808
Shevelev, I.A., Kostelianetz, N.B., Kamenkovich, V.M., Sharaev, G.A., 1991, “EEG alpha-wave in the visual cortex: check of the hypothesis of the scanning process”, Int J. of Psychophysiology, vol. 11 (2), 195-201.
Sun, S., Peeters, W.H., Bezemer, R. et al. 2019, “Finger and forehead photoplethysmography-derived pulse-pressure variation and the benefits of baseline correction”. J Clin Monit Comput 33, 65–75.
The science
behind Pylot
In 2019, the Pylot team set out to track mental performance using consumer-grade sensors with the goal of explaining why some of the team often struggled with deep work in the afternoon.
Pylot's research included over 100 hours of controlled trials, validated on thousands of hours of real world testing.
The sensors
Scientific research has shown the link between electroencephalogram (EEG) and mental fatigue from the 1990s (Okogbaa, 1994). The correlation between heart rate variability (HRV) and mental fatigue was also highlighted in research as early as 1992 (Mascord, 1992).
Pylot uses NeuroSky’s TGAM chip which is a single-channel dry EEG, located at Fp1. This device is valid for consumer use (Rieiro, 2019). Additionally, Morales (2017) concluded that “our results suggest that the TGAM-based chip EEG device is able to detect changes in mental state while performing a complex and dynamic everyday task“ and Rieiro (2019) states it “provides stable recordings even through long periods of time”.
To measure heart rate variability, Pylot uses photoplethysmography (PPG), or an optical sensor, on the forehead. PPG located on the forehead has been shown to be a suitable alternative to ECG and heart rate variability for consumer use (Sun, 2019 and Peralta, 2017).
Sample of HRV from forehead PPG compared to chest strap
Flow
Pylot defines Flow as a state of relaxed awareness. This state has been associated with alpha brain wave activity captured from EEG (Mikicin, 2015). Pylot's flow uses this relationship as the basis for the measurement.
Research has shown increased alpha brain wave activity when subjects are in a flow state (Katahira, 2018 and Knierim, 2019). Further, task performance is optimal when alpha activity is increased (Jann, 2010).
Pylot's research showed a relationship between flow and performance. Response time in a mental challenge showed a moderate difference between low and high flow levels. Trials on gamers also showed a 13% higher win rate when flow was high.
League of Legends win rate by Flow category (n=1634)
Mental Fatigue
Pylot had 41 subjects conduct a one-hour, repeated mental task to identify changes in EEG and HRV based on fatigue. The resulting fatigue model was over 82% accurate in predicting changes in mental fatigue throughout this test.
References and further reading
Green, C.S., Bavelier, D. 2006, “Enumeration versus multiple object tracking: the case of action video game players”, Cognition, 101 (1), 217-245.
Jann, K., Koenig, T., Dierks, T., Boesch, C., and Federspiel, A. 2010, “Association of individual resting state EEG alpha frequency and cerebral blood flow”, NeuroImage, Volume 51, Issue 1.
Katahira, K., Yamazaki, Y., Yamaoka, C., Ozaki, H., Nakagawa, S, Nagata, N., 2018, “EEG Correlates of the Flow State: A Combination of Increased Frontal Theta and Moderate Frontocentral Alpha Rhythm in the Mental Arithmetic Task”, Frontiers in Psychology, 9, 300
Knierim M, Nadj M, and Weinhardt C. 2019, “Flow and Optimal Difficulty in the Portable EEG: On the Potentiality of using Personalized Frequency Ranges for State Detection”. 3rd International Conference on Computer-Human Interaction Research and Applications, 2019.
Mascord DJ, Heath RA., 1992, “Behavioral and physiological indices of fatigue in a visual tracking task”, J. of Safety Research, 23(1), 19–25.
Mikicin, M., Kowalczyk, M., 2015, “Audio-Visual and Autogenic Relaxation Alter Amplitude of Alpha EEG Band, Causing Improvements in Mental Work Performance in Athletes”. Appl Psychophysiol Biofeedback 40, 219–227.
Morales, J., Diaz-Piedra, C. Rieiro, H. Roca-González, J., Romero, S., Catena, A., Fuentes, L.J., and Di Stasi, L. L. 2017. “Monitoring driver fatigue using a single-channel electroencephalographic device: A validation study by gaze-based, driving performance, and subjective data”, Accident Analysis & Prevention, Volume 109, Pages 62-69
Okogbaa, O.G., Shell, R.L., Filipusic, D., 1994, “On the investigation of the neurophysiological correlates of knowledge worker mental fatigue using the EEG signal”, Applied Ergonomics, vol. 25 (6), 355–365.
Peralta, E., Lázaro, J., Gil, E., Bailón R., and Marozas, V., 2017, "Robust pulse rate variability analysis from reflection and transmission photoplethysmographic signals," Computing in Cardiology (CinC), Rennes, 1-4.
Rieiro, H., Diaz-Piedra, C., Morales, J.M., Catena, A. et al., 2019, “Validation of Electroencephalographic Recordings Obtained with a Consumer-Grade, Single Dry Electrode, Low-Cost Device: A Comparative Study”, Sensors, 19 (12), 2808
Shevelev, I.A., Kostelianetz, N.B., Kamenkovich, V.M., Sharaev, G.A., 1991, “EEG alpha-wave in the visual cortex: check of the hypothesis of the scanning process”, Int J. of Psychophysiology, vol. 11 (2), 195-201.
Sun, S., Peeters, W.H., Bezemer, R. et al. 2019, “Finger and forehead photoplethysmography-derived pulse-pressure variation and the benefits of baseline correction”. J Clin Monit Comput 33, 65–75.
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