devices, software
API, SDK
We develop PSBD devices (Physiological Signals Based Devices) and work with basic physiological signals in the following paradigms: EEG, PPG, motion detection.
EEG
PPG
MEMS
BCI
VR-BCI
I am ready for a long road flight for work with a week- or months-long projects.
METRICS
Thanks to the combination of high-quality hardware, an advanced noise filtering system and various sensors, we operate with a large number of ready-made metrics.
Individual Alpha Peak Frequency iAF/iAPF
The individual frequency of the alpha peak is a feature of the brain that determines a person’s behavioral pattern. It allows you to calculate other metrics individually. A decrease in iAPF may indicate a decrease in performance.
Posthuma et al, «Are Smarter Brains Running Faster? Heritability of Alpha Peak Frequency, IQ, and Their Interrelation», 2001. DOI:10.1023/A:1013345411774
Bazanova, "Comments for Current Interpretation EEG Alpha Activity: A Review and Analysis", 2012.
DOI:10.4236/jbbs.2012.22027
Klimesch, "EEG-alpha rhythms and memory processes", 1997.
DOI:10.1016/s0167-8760(97)00773-3
Zhang et al, "Relationship between individual alpha peak frequency and attentional performance in a multiple object tracking task among ice-hockey players", 2021. DOI:10.1371/journal.pone.0251443
Mierau et al, "State-dependent alpha peak frequency shifts: Experimental evidence, potential mechanisms and functional implications", 2017. DOI:10.1016/j.neuroscience.2017.07.037
Polikanova, "The effect of long-term cognitive load on the EEG parameters", 2014. DOI:10.11621/npj.2014.0109
Posthuma et al, «Are Smarter Brains Running Faster? Heritability of Alpha Peak Frequency, IQ, and Their Interrelation», 2001. DOI:10.1023/A:1013345411774
Bazanova, "Comments for Current Interpretation EEG Alpha Activity: A Review and Analysis", 2012.
DOI:10.4236/jbbs.2012.22027
Klimesch, "EEG-alpha rhythms and memory processes", 1997.
DOI:10.1016/s0167-8760(97)00773-3
Zhang et al, "Relationship between individual alpha peak frequency and attentional performance in a multiple object tracking task among ice-hockey players", 2021. DOI:10.1371/journal.pone.0251443
Mierau et al, "State-dependent alpha peak frequency shifts: Experimental evidence, potential mechanisms and functional implications", 2017. DOI:10.1016/j.neuroscience.2017.07.037
Polikanova, "The effect of long-term cognitive load on the EEG parameters", 2014. DOI:10.11621/npj.2014.0109
Anxiety
State of anxiety. Tension.
Díaz et al, “EEG Beta band frequency domain evaluation for assessing stress and anxiety in resting, eyes closed, basal conditions”, 2019. DOI:10.1016/j.procs.2019.12.075.
Muhammad et al, “Human state anxiety classification framework using EEG signals in response to exposure therapy”, 2022. DOI:10.1371/journal.pone.0265679.
Díaz et al, “EEG Beta band frequency domain evaluation for assessing stress and anxiety in resting, eyes closed, basal conditions”, 2019. DOI:10.1016/j.procs.2019.12.075.
Muhammad et al, “Human state anxiety classification framework using EEG signals in response to exposure therapy”, 2022. DOI:10.1371/journal.pone.0265679.
Stress
State of stress. Strong tension.
Vanhollebeke et al, “The neural correlates of psychosocial stress: A systematic review and meta-analysis of spectral analysis EEG studies”, 2022. DOI:10.1016/j.ynstr.2022.100452.
Saeed et al, “EEG Based Classification of Long-Term Stress Using Psychological Labeling”, 2020. DOI:10.3390/s20071886.
Vanhollebeke et al, “The neural correlates of psychosocial stress: A systematic review and meta-analysis of spectral analysis EEG studies”, 2022. DOI:10.1016/j.ynstr.2022.100452.
Saeed et al, “EEG Based Classification of Long-Term Stress Using Psychological Labeling”, 2020. DOI:10.3390/s20071886.
Fatigue
Fatigue state:
Easy—after a short break, you can return to the task.
Medium—after a break, you can return to the task.
Strong - further completion of tasks will not be productive.
Jap et al, “Using EEG spectral components to assess algorithms for detecting fatigue”, 2009. DOI:10.1016/j.eswa.2007.12.043.
Hamann et al, "Investigating mental workload-induced changes in cortical oxygenation and frontal theta activity during simulated flights", 2022.
DOI:/10.1038/s41598-022-10044-y.
Trejo et al, “EEG-Based Estimation and Classification of Mental Fatigue”, 2015. DOI: 10.4236/psych.2015.65055.
Li et al, “A New Method for Human Mental Fatigue Detection with Several EEG Channels”, 2017. DOI: 10.1007/s40846-017-0224-6.
Easy—after a short break, you can return to the task.
Medium—after a break, you can return to the task.
Strong - further completion of tasks will not be productive.
Jap et al, “Using EEG spectral components to assess algorithms for detecting fatigue”, 2009. DOI:10.1016/j.eswa.2007.12.043.
Hamann et al, "Investigating mental workload-induced changes in cortical oxygenation and frontal theta activity during simulated flights", 2022.
DOI:/10.1038/s41598-022-10044-y.
Trejo et al, “EEG-Based Estimation and Classification of Mental Fatigue”, 2015. DOI: 10.4236/psych.2015.65055.
Li et al, “A New Method for Human Mental Fatigue Detection with Several EEG Channels”, 2017. DOI: 10.1007/s40846-017-0224-6.
Involvement
State of Engagement. Best performance.
Katahira et al, “EEG Correlates of the Flow State”, 2018. DOI:10.3389/fpsyg.2018.00300.
Leroy et al, “EEG dynamics and neural generators of psychological flow during one tightrope performance”, 2020. DOI:10.1038/s41598-020-69448-3.
Katahira et al, “EEG Correlates of the Flow State”, 2018. DOI:10.3389/fpsyg.2018.00300.
Leroy et al, “EEG dynamics and neural generators of psychological flow during one tightrope performance”, 2020. DOI:10.1038/s41598-020-69448-3.
Relaxation
State of relaxation. Good performance.
Zemla et al, “Modeling of Brain Cortical Activity during Relaxation and Mental Workload Tasks Based on EEG Signal Collection”, 2023. DOI:10.3390/app13074472.
Petche et al, "The possible meaning of the upper and lower alpha frequency ranges for cognitive and creative tasks", 1997.
DOI:10.1016/S0167-8760(97)00757-5.
Rodriguez-Larios et al, "Visual Working Memory Recruits Two Functionally Distinct Alpha Rhythms in Posterior Cortex", 2022. DOI: DOI:10.1523/ENEURO.0159-22.2022.
Zemla et al, “Modeling of Brain Cortical Activity during Relaxation and Mental Workload Tasks Based on EEG Signal Collection”, 2023. DOI:10.3390/app13074472.
Petche et al, "The possible meaning of the upper and lower alpha frequency ranges for cognitive and creative tasks", 1997.
DOI:10.1016/S0167-8760(97)00757-5.
Rodriguez-Larios et al, "Visual Working Memory Recruits Two Functionally Distinct Alpha Rhythms in Posterior Cortex", 2022. DOI: DOI:10.1523/ENEURO.0159-22.2022.
«Flow»
An indicator of the level of user involvement in the process of any activity. It allows us to draw a conclusion about how interesting the user is in this activity and how involved he is in solving any problems within the framework of this activity.
Petche et al, "The possible meaning of the upper and lower alpha frequency ranges for cognitive and creative tasks", 1997.
DOI:10.1016/S0167-8760(97)00757-5.
Rodriguez-Larios et al, "Visual Working Memory Recruits Two Functionally Distinct Alpha Rhythms in Posterior Cortex", 2022. DOI: DOI:10.1523/ENEURO.0159-22.2022.
Petche et al, "The possible meaning of the upper and lower alpha frequency ranges for cognitive and creative tasks", 1997.
DOI:10.1016/S0167-8760(97)00757-5.
Rodriguez-Larios et al, "Visual Working Memory Recruits Two Functionally Distinct Alpha Rhythms in Posterior Cortex", 2022. DOI: DOI:10.1523/ENEURO.0159-22.2022.
Concentration
An indicator of the user’s concentration level when performing a specific task (for example, watching a video lesson or online lecture). If the index decreases, we can conclude that there is a loss of concentration (switching attention).
"Lim et al, “Comparison between concentration and immersion based on eeg analysis”, 2019. DOI:10.3390/s19071669.
Grammer et al, “Effects of context on the neural correlates of attention in a college classroom”, 2021. DOI:10.1038/s41539-021-00094-8.
Freeman et al, “Evaluation of an adaptive automation system using three EEG indices with a visual tracking task”, 1999. DOI:10.1016/s0301-0511(99)00002-2".
"Lim et al, “Comparison between concentration and immersion based on eeg analysis”, 2019. DOI:10.3390/s19071669.
Grammer et al, “Effects of context on the neural correlates of attention in a college classroom”, 2021. DOI:10.1038/s41539-021-00094-8.
Freeman et al, “Evaluation of an adaptive automation system using three EEG indices with a visual tracking task”, 1999. DOI:10.1016/s0301-0511(99)00002-2".
Cognitive load
Cognitive load. How busy the brain is with the current task (the usefulness of the task is not taken into account). The indicator of the user’s cognitive load can be used to assess the number of “mental” resources used by the body to solve a task - indirectly one can judge how easy or difficult a given task is for the user.
Olga Bazanova et al "Individual measures of electroencephalogram alpha activity and non-verbal creativity", 2008.
Irina Polikanova, Alexander Sergeec et al "The effect of long-term cognitive load on the EEG parameters", 2014.
С.И. Новикова et al "Ритмы ЭЭГ и когнитивные процессы", 2015.
Aura Hernández-Sabaté, José Yauri, Pau Folch, Miquel Àngel Piera, Debora Gil et. al "Recognition of the Mental Workloads of Pilots in the Cockpit Using EEG Signals", 2022.
Michał Ociepka , Patrycja Kałamała, Adam Chuderski et al, "High individual alpha frequency brains run fast, but it does not make them smart", 2022.
Olga Bazanova et al "Individual measures of electroencephalogram alpha activity and non-verbal creativity", 2008.
Irina Polikanova, Alexander Sergeec et al "The effect of long-term cognitive load on the EEG parameters", 2014.
С.И. Новикова et al "Ритмы ЭЭГ и когнитивные процессы", 2015.
Aura Hernández-Sabaté, José Yauri, Pau Folch, Miquel Àngel Piera, Debora Gil et. al "Recognition of the Mental Workloads of Pilots in the Cockpit Using EEG Signals", 2022.
Michał Ociepka , Patrycja Kałamała, Adam Chuderski et al, "High individual alpha frequency brains run fast, but it does not make them smart", 2022.
Stress Level Classification Using Heart Rate Variability
Heart rate variability is one of the most effective methods for diagnosing stress.
Amira et al, “Stress Level Classification Using Heart Rate Variability”, 2019. DOI:10.25046/aj04030.
Amira et al, “Stress Level Classification Using Heart Rate Variability”, 2019. DOI:10.25046/aj04030.
Cognitive Fatigue
Fatigue level indicator - allows you to conclude how tired the user is while performing a task. Indirectly, you can judge how many resources the user has left to perform other tasks.
Jap et al, “Using EEG spectral components to assess algorithms for detecting fatigue”, 2009. DOI:10.1016/j.eswa.2007.12.043.
Hamann et al, "Investigating mental workload-induced changes in cortical oxygenation and frontal theta activity during simulated flights", 2022.
DOI:/10.1038/s41598-022-10044-y.
Trejo et al, “EEG-Based Estimation and Classification of Mental Fatigue”, 2015. DOI: 10.4236/psych.2015.65055.
Li et al, “A New Method for Human Mental Fatigue Detection with Several EEG Channels”, 2017. DOI: 10.1007/s40846-017-0224-6.
Jap et al, “Using EEG spectral components to assess algorithms for detecting fatigue”, 2009. DOI:10.1016/j.eswa.2007.12.043.
Hamann et al, "Investigating mental workload-induced changes in cortical oxygenation and frontal theta activity during simulated flights", 2022.
DOI:/10.1038/s41598-022-10044-y.
Trejo et al, “EEG-Based Estimation and Classification of Mental Fatigue”, 2015. DOI: 10.4236/psych.2015.65055.
Li et al, “A New Method for Human Mental Fatigue Detection with Several EEG Channels”, 2017. DOI: 10.1007/s40846-017-0224-6.
Pulse
Determination of heart rate and related parameters.
Relaxation
An indicator of the level of relaxation (relaxation) of the user during the relaxation exercise. If the index increases from the base level, we can conclude that the user is relaxing in the right way, has disconnected from extraneous thoughts and is in a state conducive to proper rest and relaxation.
Zemla et al, “Modeling of Brain Cortical Activity during Relaxation and Mental Workload Tasks Based on EEG Signal Collection”, 2023. DOI:10.3390/app13074472.
Zemla et al, “Modeling of Brain Cortical Activity during Relaxation and Mental Workload Tasks Based on EEG Signal Collection”, 2023. DOI:10.3390/app13074472.
Anger
A rapid emotional state of dissatisfaction/annoyance.
Harmon-Jones et al, “Anger and frontal brain activity: EEG asymmetry consistent with approach motivation despite negative affective valence”, 1998. DOI:10.1037/0022-3514.74.5.1310.
Zhao et al, “Frontal EEG Asymmetry and Middle Line Power Difference in Discrete Emotions”, 2018. DOI:10.3389/fnbeh.2018.00225.
Harmon-Jones et al, “Anger and frontal brain activity: EEG asymmetry consistent with approach motivation despite negative affective valence”, 1998. DOI:10.1037/0022-3514.74.5.1310.
Zhao et al, “Frontal EEG Asymmetry and Middle Line Power Difference in Discrete Emotions”, 2018. DOI:10.3389/fnbeh.2018.00225.
Self-control
Rapid emotional state - the ability to suppress or change the prevailing tendency to react to a situation.
Schmeichel et al, “Exercising self-control increases relative left frontal cortical activation”, 2016. DOI:10.1093/scan/nsv112.
Hofstee et al, “Self-regulation and frontal EEG alpha activity during infancy and early childhood: A multilevel meta-analysis”, 2022. DOI: 10.1111/desc.13298.
Schmeichel et al, “Exercising self-control increases relative left frontal cortical activation”, 2016. DOI:10.1093/scan/nsv112.
Hofstee et al, “Self-regulation and frontal EEG alpha activity during infancy and early childhood: A multilevel meta-analysis”, 2022. DOI: 10.1111/desc.13298.
Focus
The rapid emotional state of focus.
Lim et al, “Comparison between concentration and immersion based on eeg analysis”, 2019. DOI:10.3390/s19071669.
Grammer et al, “Effects of context on the neural correlates of attention in a college classroom”, 2021. DOI:10.1038/s41539-021-00094-8.
Freeman et al, “Evaluation of an adaptive automation system using three EEG indices with a visual tracking task”, 1999. DOI:10.1016/s0301-0511(99)00002-2.
Lim et al, “Comparison between concentration and immersion based on eeg analysis”, 2019. DOI:10.3390/s19071669.
Grammer et al, “Effects of context on the neural correlates of attention in a college classroom”, 2021. DOI:10.1038/s41539-021-00094-8.
Freeman et al, “Evaluation of an adaptive automation system using three EEG indices with a visual tracking task”, 1999. DOI:10.1016/s0301-0511(99)00002-2.
Long-Term Stress
Rapid emotional state of tension.
Saeed et al, “EEG Based Classification of Long-Term Stress Using Psychological Labeling”, 2020. DOI:10.3390/s20071886.
Vanhollebeke et al, “The neural correlates of psychosocial stress: A systematic review and meta-analysis of spectral analysis EEG studies”, 2022. DOI:10.1016/j.ynstr.2022.100452.
Saeed et al, “EEG Based Classification of Long-Term Stress Using Psychological Labeling”, 2020. DOI:10.3390/s20071886.
Vanhollebeke et al, “The neural correlates of psychosocial stress: A systematic review and meta-analysis of spectral analysis EEG studies”, 2022. DOI:10.1016/j.ynstr.2022.100452.
Calmness
A quick emotional state of relaxation/calmness.
Zemla et al, “Modeling of Brain Cortical Activity during Relaxation and Mental Workload Tasks Based on EEG Signal Collection”, 2023. DOI:10.3390/app13074472.
Petche et al, "The possible meaning of the upper and lower alpha frequency ranges for cognitive and creative tasks", 1997.
DOI:10.1016/S0167-8760(97)00757-5.
Rodriguez-Larios et al, "Visual Working Memory Recruits Two Functionally Distinct Alpha Rhythms in Posterior Cortex", 2022. DOI: DOI:10.1523/ENEURO.0159-22.2022.
Zemla et al, “Modeling of Brain Cortical Activity during Relaxation and Mental Workload Tasks Based on EEG Signal Collection”, 2023. DOI:10.3390/app13074472.
Petche et al, "The possible meaning of the upper and lower alpha frequency ranges for cognitive and creative tasks", 1997.
DOI:10.1016/S0167-8760(97)00757-5.
Rodriguez-Larios et al, "Visual Working Memory Recruits Two Functionally Distinct Alpha Rhythms in Posterior Cortex", 2022. DOI: DOI:10.1523/ENEURO.0159-22.2022.
Software and API
UNIVERSAL SOFTWARE FOR WORKING
WITH DATA FROM GELO DEVICES AND ALL THE CAPABILITIES OF GELO DEVICES
Display data from all sensors in real time (EEG, PPG, MEMS)
Full access to all Gelo metrics: measuring instant productivity, determining psycho-emotional states, measuring iAPF
Output of spectral power Theta, Alpha, SMR, Beta rhythms in the range of 4 - 35 Hz
Recording RAW DATA in HDF5 format with the ability to markup
Write values to XLSX for quick analysis
The software works with all Gelo devices
Versions available for Windows and MacOS
Capsule software is included free with the purchase of any Gelo device
A tool to integrate Gelo capabilities
into your project
Create your own applications for Windows, MacOS, iOS
Implement a full set of metrics and states into your products or work with raw streaming data
Use ready-made dynamic libraries for Windows, MacOS, iOS
Documentation in Doxygen format
API language - C
API compatible with Gelo Headband and Headphones
Access to the Capsule API with the purchase of any device
DEVICES
gelo devices are available in TWO main form factors
Вот уже более 5 лет мы каждый
день упорно работаем над преобразованием мировых научных исследований в продукты ежедневного использования.
Наша команда уже преодолела
большое количество сложных научных, инженерных и продуктовых челленджей, но это только начало.
Headband for working with brain signals
and body indicators. It has 4 dry EEG channels, a PPG sensor and a gyroscope + accelerometer (MEMS)
4 EEG channels - O1, O2, T3, T4
Works with brain rhythms: Theta, Alpha, SMR, Beta
Sampling frequency 250 Hz
The headband is soft and flexible, with adjustable clasp to fit all head sizes
Working with physiological signals: EEG, PPG, acceleration, angular velocity
The Headband has 4 EEG channels, as well as a reference and grounding on the forehead. Each channel contains 25 pins, which allows you to create a larger contact area and more stable signal reception
Measured voltage range ± 0.4 V
The pins are spring-loaded: they will not break, crack or rub. Does not put pressure on the head and works even through the hair
Supported software: Capsule, Capsule API, Mind Tracker BCI
The electrodes are coated with gold, which is the best way to collect EEG among dry methods
Characteristics
Convenience
Dry spring-loaded electrodes
Supported platforms: Windows, MacOS, iOS
Operating time on one charge 8 hours
The electrode design is patented and provides the best signal-to-noise ratio among dry electrodes
Headband with adjustable clasp
Equipment
Magnetic USB charging
Headband for working with brain signals
Software
and body indicators. It has 4 dry EEG channels, a PPG sensor and a gyroscope + accelerometer (MEMS)
Sampling frequency of PPG signal measurement: 100 Hz
Angular velocity is measured along three orthogonal axes
Acceleration is measured along three orthogonal axes
Range of measured angular velocities: ±250; ±500; ±1000; ±1200; ±2000 deg/s
Range of measured accelerations: ±2; ±4; ±8; ±16 g
Headphones for working with brain signals
and body indicators 4 dry EEG channels, PPG sensor and gyroscope + accelerometer (MEMS)
4 EEG channels (A1, A2, C3, C4, 3Cz - ref.)
Reproducible frequencies (dynamic range) 20-20000 Hz
Sampling frequency 250 Hz
Working with physiological signals: EEG
Bluetooth 4.2.
Supported software: Capsule, Capsule API, Mind Tracker BCI
Characteristics
Dry spring-loaded electrodes
Supported platforms: Windows, MacOS, iOS
Operating time 8 hours
Headphones
Equipment
MicroUSB charging
Headphones for working with brain signals
Software
and body indicators 4 dry EEG channels, PPG sensor and gyroscope + accelerometer (MEMS)
Bluetooth profiles HFP/HSP/A2DP/VCRCP
Dimensions and number of speakers 2 x 40mm
Wireless
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