A dominant modality in current non-invasive neurogaming is Electroencephalography (EEG), specifically utilizing Visual Evoked Potentials (VEPs). A 2025 systematic review highlights Steady-State Visual Evoked Potentials (SSVEP) as a preferred paradigm for high-speed gaming control due to its high information transfer rates and robustness [3]. Despite its efficacy, SSVEP-based systems face significant ergonomic challenges, primarily visual fatigue caused by the flickering stimuli required to evoke neural responses. Researchers are consequently exploring alternative paradigms, such as motion-onset VEPs (m-VEP) and code-modulated VEPs (c-VEP), to balance signal fidelity with user comfort during extended sessions [3].

The clinical application of these technologies has achieved significant regulatory validation, most notably with the U.S. Food and Drug Administration’s authorization of EndeavorRx for the treatment of ADHD. This milestone underscores the shift toward “digital therapeutics,” where game mechanics are engineered to treat specific neural dysregulations. Beyond ADHD, preprint research indicates that neurogaming combined with Virtual Reality (VR) is proving effective for exposure therapy in Post-Traumatic Stress Disorder (PTSD) and anxiety. Furthermore, BCI-driven games are being utilized in motor rehabilitation for stroke and cerebral palsy patients, translating intent directly into digital action to reinforce neural pathways damaged by injury [1].

Future industry growth depends on overcoming the technical limitations of non-invasive sensors. While invasive devices like Neuralink promise high-bandwidth data, the consumer and therapeutic markets currently rely on EEG and Near-Infrared Spectroscopy (NIRS). These technologies must address signal-to-noise ratio issues and the need for lengthy calibration processes that currently hinder widespread adoption. As signal processing algorithms improve to handle individual neural variability, neurogaming is positioned to become a standard modality for both cognitive enhancement and the management of chronic neurological disorders [2].

The Mentalab Explore Pro system offers a comprehensive solution for researchers and developers seeking a versatile and robust platform for EEG/ExG biosignal acquisition in neurogaming applications. Its compact design, wireless & wired streaming capabilities, open software API, and compatibility with various electrode types, including dry electrodes, render it highly adaptable for dynamic development environments, providing the precision and flexibility necessary to advance the field of neurogaming

Selected References

1. Sa, D. P., Bose, U., Gopalakrishnan, S., & Babu, M. G. R. (2024). Emerging Frontiers in Neuroscience: Exploring the Potential of Neurogaming and Brain-Computer Interfaces in Therapeutics. SSRN Electronic Journal.
2. George, A. S., George, A. S. H., & Baskar, T. (2023). Neuro-Gaming: How Video Games Shape the Brain’s Cognitive Landscape. Partners Universal International Research Journal, 2(4), 128-137.
3. Keutayeva, A., Nwachukwu, C. J., Alaran, M., Otarbay, Z., & Abibullaev, B. (2025). Neurotechnology in Gaming: A Systematic Review of Visual Evoked Potential-Based Brain-Computer Interfaces. IEEE Access, 13, 74944-74966.

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Many researchers working with EEG face a similar challenge. Most feedback tasks rely on simple visual cues on a flat screen that do not reflect real environments or natural movement. At the same time, there is growing interest in studying behaviour in situations that feel more immersive. VR can offer this, but it only becomes meaningful when the virtual environment can react to the user in real time.

How can EEG data change what happens in VR in real time without a complicated setup?

We wanted to explore whether Mentalab Explore devices could support this kind of interaction and whether the experience would feel intuitive. The aim was not to create a full application but to build a working example that shows what is possible when EEG and VR are connected.

The solution we built

To make this possible, we processed the EEG in Python, calculated the Alpha band power, and streamed the results through Lab Streaming Layer to Unity. Unity then adjusted the scene based on how stable or variable the Alpha signal was. Instead of focusing on the technical mechanics, we focused on the user experience: would the feedback feel understandable and responsive?

We created a simple virtual space that reacts directly to the user’s Alpha activity. When Alpha rises, the cabin brightens, and certain environmental features, like small birds, begin to appear. When Alpha falls, the scene becomes quieter again. The feedback is meant to feel natural rather than forced.

The result is a small but complete system that illustrates how mental states can be reflected inside a virtual environment. It shows that biofeedback does not have to stay on flat screens. It can be part of an immersive setting where users can explore how their internal state shapes what they see.

What this approach offers

This type of setup can support research on topics such as attention, relaxation training, and interactive tasks in VR. It also gives a basis for experiments where context matters. A quiet forest, a bright room, or a busy environment may influence the user’s mental state, and EEG-driven VR allows these elements to shift in response.

The project does not aim to solve every challenge around VR and EEG. It simply shows a practical path for joining the two. The hope is that it encourages researchers to try their own versions and explore what kinds of questions this combination can help address.

For researchers and developers exploring how EEG can shape immersive VR experiences in real time, the Mentalab Explore Pro system offers a practical and flexible foundation. Explore devices provide high-quality ExG data in a compact wireless form, making it easy to integrate EEG into mobile or headset-based VR setups. Using the open Explore API together with frameworks like Lab Streaming Layer, EEG metrics such as Alpha power can be streamed directly into interactive environments without additional hardware. When combined with Mentalab Hypersync, simultaneous and precisely aligned EEG measurements across multiple devices become possible as well.

The post VR and EEG: Real-Time Neurofeedback in Unity appeared first on Mentalab.

What if we could measure the athlete’s brain in motion, on the field, the track, the court, or even in the pool, without sacrificing data quality? Advances in mobile EEG now make this possible, opening the door to truly ecological, real-world sports neuroscience.

Mobile EEG in Sports Performance

This post explores why mobile EEG matters for sport science, what we can learn when we move beyond the lab.

Despite concerns about movement artifacts, especially in high-acceleration sports, Cheron et al. (2016) highlight promising hardware solutions, including sensor designs that can directly measure and subtract motion noise. As these technologies mature, the feasibility of high-quality EEG in naturalistic settings becomes increasingly well-supported.

This is strongly supported by a recent PhD thesis from Georgia Alexandrou (2021), who recorded mobile EEG from elite athletes during real sporting behaviors such as pistol shooting and curling. Her work identifies distinct neural signatures that separate successful from unsuccessful performance and shows that these patterns vary significantly between athletes and between sports.

In precision tasks like shooting, unsuccessful attempts consistently showed elevated alpha, theta, and beta power in the final second before trigger pull. These patterns are associated with reduced attentional efficiency and increased cognitive effort. By contrast, successful attempts, showed lower activity across these bands, aligning with the “neural efficiency” hypothesis.

Sport-Specific Brain Patterns

In curling, the picture was entirely different: successful shots were marked by increased right-frontal theta activity during preparation, suggesting enhanced cognitive control and strategic focus.

The key insight is clear: there is no single ‘sport brain pattern.’ Optimal neural dynamics are highly individual, shaped by the athlete, the task, and the sport.

This aligns with broader neuroergonomics findings. Rahman et al. (2019) show that EEG can be reliably captured during running, cycling, weightlifting, yoga, and other full-body activities. Across modalities, alpha, theta, and beta power rise with exercise intensity, but near-maximal effort produces sharp drops in alpha, indicating growing cognitive and physiological load. Even gait which was once considered too noisy to measure, reveals distinct gamma-band fluctuations across the stride cycle. This demonstrates that motor coordination carries a rhythmic cortical signature.

Together, these findings show that mobile EEG does not fall apart during movement. Instead, it provides a powerful window into how the brain and body interact in real athletic environments. Mobile EEG is emerging not just as a scientific tool, but as a foundation for individualized neurofeedback, performance optimization, and future applications across rehabilitation, ergonomics, and health monitoring.

For researchers and developers seeking a flexible ExG biosignal platform, the Mentalab Explore Pro system delivers up to 32 channels of research-grade data in a compact, wireless system. Its mobile app enables real-time data viewing, while an open software API and support for wet and dry electrodes ensure versatile integration. With Mentalab Hypersync, simultaneous inter- and intrapersonal measurements become possible, supporting advanced studies in environments outside the laboratory.

References:

• Alexandrou, G. (2021). Validating the use of mobile EEG to investigate neural markers of real-world successful sporting performance in elite athletes (Doctoral dissertation, University of Stirling). University of Stirling.

• Cheron, G., Petit, G., Cheron, J., Leroy, A., Cebolla, A., Cevallos, C., Petieau, M., Hoellinger, T., Zarka, D., Clarinval, A., & Dan, B. (2016). Brain oscillations in sport: toward EEG Biomarkers of Performance. Frontiers in Psychology, 7, 246. https://doi.org/10.3389/fpsyg.2016.00246

• Park, J. L., Fairweather, M. M., & Donaldson, D. I. (2015). Making the case for mobile cognition: EEG and sports performance. Neuroscience & Biobehavioral Reviews, 52, 117–130. https://doi.org/10.1016/j.neubiorev.2015.02.014

• Rahman, M., Karwowski, W., Fafrowicz, M., & Hancock, P. A. (2019). Neuroergonomics Applications of Electroencephalography in Physical Activities: A Systematic review. Frontiers in Human Neuroscience, 13, 182. https://doi.org/10.3389/fnhum.2019.00182

The post The Brain of Great Athletes: Mobile EEG in Sports appeared first on Mentalab.

In many EEG and ExG experiments, precise synchronization between multiple acquisition devices and the experimental computer is essential. Achieving precise synchronisation is essential not only for Hyperscanning studies, in which brain signals are recorded from several participants at the same time, for instance, to study social interaction. Similarly, users interested in synchronising EEG with peripheral ExG sensors, such as an ECG system worn on the chest, want synchronised data, without involving long cables reaching along the body of the participant. Not least, the same problem arises when conducting fully wireless event-related potential (ERP) studies.

Mentalab Hypersync addresses this challenge by providing sub-millisecond wireless synchronization through a single central host system. Fully compatible with the Lab Streaming Layer (LSL) framework [1], Hypersync enables seamless time alignment across multiple data acquisition devices and experimental computers.

The test setup

To test EEG synchronization accuracy, we used the Lab Streaming Layer (LSL) validation procedure [2], measuring the temporal offset between a hardware-defined ground truth event and a software-generated LSL marker event. Software events occur with irregular timing, while hardware events correspond to voltage transitions on cables connected directly to the Mentalab hardware.

The hardware event is triggered via a wired USB connection, which sends a transistor-transistor-logic (TTL) pulse to the Mentalab ExG amplifiers’ wired trigger inputs. A dedicated computer process initiates both hardware and software events sequentially, ensuring synchronized stimulus delivery.  We designed a dedicated hardware setup for this purpose, which can be expanded modularly to accommodate additional amplifiers as needed. Each amplifier processes the incoming signals and transmits them wirelessly to the Hypersync host for synchronization and recording. All resulting data streams – from both the hardware and software sources – are recorded using LabRecorder for subsequent analysis.

Results: EEG Synchronization

The results are calculated from the XDF files recorded using LabRecorder during the test. For each hardware and software event pair, the error value between the LSL markers and the analog signal was computed. Summary statistics for the error values were computed across approximately 800 trials over one hour. A zoomed-in and zoomed-out view of the error value and frequency distribution plot is shown below. The mean and standard deviation of the error values are in microseconds and suggest a very low jitter well within the sub-millisecond range. In fact, about 90% of the data is within +- 200 microseconds. The results were also validated with CSV file recordings, multiple sampling rates, and an analogue signal-based trigger generator.

Learn more about Mentalab Hypersync and our products.

References

• Kothe, C., Shirazi, S. Y., Stenner, T., Medine, D., Boulay, C., Grivich, M. I., Artoni, F., Mullen, T., Delorme, A., & Makeig, S. (2025). The lab streaming layer for synchronized multimodal recording. Imaging Neuroscience, 3, IMAG.a.136. https://doi.org/10.1162/IMAG.a.136
• https://sccn.ucsd.edu/download/mgrivich/LSL_Validation.html
• Ayrolles, A., Brun, F., Chen, P., Djalovski, A., Beauxis, Y., Delorme, R., Bourgeron, T., Dikker, S., & Dumas, G. (2021). HyPyP: A Hyperscanning Python Pipeline for inter-brain connectivity analysis. Social Cognitive and Affective Neuroscience, 16(1–2), 72–83. https://doi.org/10.1093/scan/nsaa141
• Barraza, P., Dumas, G., Liu, H., Blanco-Gomez, G., van den Heuvel, M. I., Baart, M., & Pérez, A. (2019). Implementing EEG hyperscanning setups. MethodsX, 6, 428–436. https://doi.org/10.1016/j.mex.2019.02.021
• Czeszumski, A., Eustergerling, S., Lang, A., Menrath, D., Gerstenberger, M., Schuberth, S., Schreiber, F., Rendon, Z. Z., & König, P. (2020). Hyperscanning: A Valid Method to Study Neural Inter-brain Underpinnings of Social Interaction. Frontiers in Human Neuroscience, 14. https://doi.org/10.3389/fnhum.2020.00039
• Turk, E., Vroomen, J., Fonken, Y., Levy, J., & van den Heuvel, M. I. (2022). In sync with your child: The potential of parent–child electroencephalography in developmental research. Developmental Psychobiology, 64(3), e22221. https://doi.org/10.1002/dev.22221
• Zamm, A., Loehr, J. D., Vesper, C., Konvalinka, I., Kappel, S. L., Heggli, O. A., Vuust, P., & Keller, P. E. (2024). A practical guide to EEG hyperscanning in joint action research: From motivation to implementation. Social Cognitive and Affective Neuroscience, 19(1), nsae026. https://doi.org/10.1093/scan/nsae026
• https://github.com/amisepa/BrainBeats
• Cannard, C., Wahbeh, H., & Delorme, A. (2024). BrainBeats, an Open-Source EEGLAB Plugin to Jointly Analyze EEG and Cardiovascular Signals (p. 2023.06.01.543272). bioRxiv. https://doi.org/10.1101/2023.06.01.543272

FAQ

How do you synchronize two or more EEG devices for hyperscanning experiments?
Mentalab Hypersync synchronizes multiple EEG systems by connecting all Explore amplifiers and experimental computers to a single wireless timing host. Each device streams its data through the Lab Streaming Layer (LSL), and Hypersync aligns the timestamps so all EEG data share the same clock with sub-millisecond precision, even when participants are physically separated.

Can I synchronize EEG with ECG/EMG/EOG (multimodal ExG)?
Yes. Hypersync can time-align the EEG with other physiological signals, such as ECG, EMG, or EOG, in real time, without using long trigger cables.

Can you run wireless ERP experiments (e.g., P300, flanker task) without losing timing precision?
Yes. Hypersync maintains sub-millisecond timing accuracy in wireless event-related potential (ERP) studies. This means you can reliably capture latency-sensitive components such as the P300 or N200 – even during movement.

How do you validate synchronization accuracy in LSL recordings (e.g., using XDF timing error analysis)?
You can validate synchronization by comparing hardware trigger timestamps to LSL markers in the XDF file and computing timing error distributions to confirm sub-millisecond latency and jitter.

What research applications use synchronized multi-device EEG?
EEG hyperscanning (social interaction, joint action), multimodal EEG-ECG “brainbeats,” mobile/wireless ERP (P300, flanker), and any LSL-based multi-device experiment that needs precise timing.

How accurate is Mentalab Hypersync (latency and jitter)?
In ~800 trials over ~1 hour, ~90% of event-pair errors were within ±200 µs after subtracting a fixed 3.6 ms latency; overall jitter is well within the sub-millisecond range.

The post Inside Mentalab Hypersync: How We Test Our Hyperscanning Technology appeared first on Mentalab.

EEG excels at capturing real-time brain activity in user research, thanks to its millisecond-level precision (see Zhu & Lv, 2023). Combined with its non-invasive nature and versatile data processing options, EEG enables research on novel and innovative products, applications, and services. At Mentalab, for example, we explored a side project combining our EEG amplifier with a programmable EEG Lamp for neurofeedback. The system monitors the increase of alpha-band activity, which can indicate a state of relaxation (Klimesch, 1999). Based on these signals, it adjusts the brightness and color of a spherical lamp. This creates immediate visual feedback during meditation sessions. This playful yet functional design illustrates the broader potential of EEG beyond research, into interactive user experiences. EEG to lamp brightness | Wiki

Capturing Emotional Reactions with EEG

Users’ emotions play a major role in the experience of a product, yet they are difficult to measure objectively. EEG addresses this challenge, as certain patterns of brain activity correlate with emotional states. For example, the left and right frontal lobes of the brain respond differently to positive versus negative emotions. By analyzing these frontal EEG asymmetries, one can infer the strength of positive or negative feelings (Borawska & Mateja, 2024). One study showed that EEG frontal asymmetry could predict how users perceive an IT application’s usefulness and enjoyment (Moridis et al., 2018). EEG thus provides indicators of user emotions during an interaction in real time, without relying solely on potentially biased self-reports.

In UX research, EEG data are often used to quantify the valence (pleasant vs. unpleasant) and intensity(arousal) of user experiences. Gannouni et al. (2023) present a framework for EEG-based emotion recognition in usability testing that yields much more precise insights into user satisfaction than questionnaires alone. Using machine learning on EEG features, they were able to classify participants’ emotions along the dimensions of valence, arousal, and dominance with over 92% accuracy (Gannouni et al., 2023). Such approaches demonstrate that EEG can make users’ feelings and satisfaction measurable in real time.

Measuring Attention and Visual Perception with EEG

EEG can also be used to objectively monitor users’ attention. Whether an interface effectively directs attention to important elements or causes distraction is crucial. EEG can help answer this, since specific brain signatures are linked to attentional processes. Early event-related potentials (ERPs) like the P1 and N1 waves indicate when a visual stimulus is initially processed. The P300 signal, occurring about 300 ms after a significant stimulus, increases in amplitude with attention and is delayed under cognitive load (Zhu & Lv, 2023).

EEG can also detect lapses in attention. When users become tired, slower theta waves increase, indicating reduced vigilance. Slanzi et al. (2017) demonstrated that combining pupil size measurement and EEG could predict click intention. This predictive use of EEG could inform adaptive interfaces that respond to attention in real time.

Measuring Cognitive Load and Mental Workload

Cognitive load refers to the extent to which working memory and mental resources are taxed by a task such as interacting with a user interface. EEG offers a direct way to measure this. High mental demand often corresponds to decreased alpha waves and increased theta waves (Lal & Craig, 2001). Caldiroli et al. (2023) found that smartphone-based web tasks induced significantly more cognitive load than the same tasks on desktop, as indicated by these EEG changes.

This has practical implications. Mobile interfaces may require simplification to prevent mental overload. EEG can also aid in identifying usability problems in interfaces.

What Mentalab can offer

For researchers and developers requiring a versatile and robust platform for ExG biosignal acquisition for UX research, the Mentalab Explore Pro system offers a comprehensive solution. With up to 32 channels and research grade EEG data quality, it is ideally suited for demanding research environments. Its compact design, wireless & wired streaming capabilities, open software API, and compatibility with various electrode types, including dry electrodes, render it highly adaptable for both laboratory and mobile studies, providing the precision and flexibility necessary to advance the field of UX research.

References

• Borawska, A., & Mateja, A. (2024). Unveiling the User Experience: A Synthesis of Cognitive Neuroscience Methods in Digital Product Design. In A. R. da Silva et al. (Eds.), Advances in Information Systems Development. Springer.
• Caldiroli, C. L., et al. (2023). Comparing online cognitive load on mobile versus PC-based devices. Personal and Ubiquitous Computing, 27(2), 495–505.
• Cano, S., et al. (2020). Low-Cost Assessment of User Experience through EEG Signals. IEEE Access, 8, 158475–158487.
• Gannouni, S., et al. (2023). Software Usability Testing Using EEG-Based Emotion Detection and Deep Learning. Sensors, 23(11), 5147.
• Lal, S. K. L., & Craig, A. (2001). A critical review of the psychophysiology of driver fatigue. Biological Psychology, 55(3), 173–194.
• Moridis, C. N., et al. (2018). Using EEG frontal asymmetry to predict IT users’ perceptions. Applied Psychophysiology and Biofeedback, 43(2), 1–11.
• Slanzi, G., et al. (2017). Combining eye tracking, pupil dilation and EEG analysis for predicting web users’ click intention. Information Fusion, 35, 51–57.
• Stancin, I., et al. (2021). A review of EEG signal features and their application in driver drowsiness detection systems. Sensors, 21(11), 3786.
• Zhu, L., & Lv, J. (2023). Review of studies on user research based on EEG and eye tracking. Applied Sciences, 13(11), 6502.
• Klimesch, W. (1999). EEG alpha and theta oscillations reflect cognitive and memory performance: a review and analysis. Brain Research Reviews, 29(2-3), 169–195.

FAQ

What is EEG and how is it used in UX research?
EEG measures brain activity in real time. In UX research, it helps detect users’ emotional states, attention levels, and mental workload during interactions.

How does EEG measure cognitive load?
EEG detects shifts in brainwave frequencies. A drop in alpha and rise in theta waves typically indicates increased mental workload.

Can EEG detect user frustration?
Yes. EEG patterns can reveal frustration before users express it consciously, making it a powerful tool for usability testing.

What is the benefit of combining EEG with eye tracking in UX?
This combination shows both where users look and how deeply they cognitively process what they see, offering a full picture of attention.

The post EEG in UX Research: Measuring Emotions, Attention & Cognitive Load appeared first on Mentalab.

Electroencephalography (EEG) for Cognitive State Monitoring

EEG, a measure of the brain’s electrical activity, offers significant utility in understanding and mitigating cognitive factors in driving. Driver fatigue and inattention contribute substantially to global road accidents (National Highway Traffic Safety Administration, 2025; National Sleep Foundation, 2024). Early and foundational research, spanning the late 20th and early 21st centuries, established the relationship between changes in EEG frequency bands (e.g., increased theta and alpha power, decreased beta power) and increasing levels of drowsiness and reduced alertness in driving scenarios. Comprehensive reviews of this work highlight the consistent physiological markers of fatigue derived from EEG (Lal & Craig, 2001; Stancin et al., 2021). EEG enables the detection of subtle alterations in brainwave patterns indicative of drowsiness, often preceding overt behavioral manifestations.

Beyond fatigue detection, EEG facilitates the assessment of cognitive workload and attentional allocation, critical parameters for effective HMI design in increasingly complex vehicular environments. For instance, EEG has been employed to quantify the cognitive load experienced by drivers in partially automated vehicles, comparing it to manual driving scenarios (Figalová et al., 2024). The capacity for real-time cognitive state monitoring allows for the development of adaptive vehicle systems that can adjust warnings, information presentation, or even automation levels in accordance with the driver’s cognitive readiness.

Electromyography (EMG) for Musculoskeletal Analysis and Control

Electromyography (EMG) involves recording the electrical activity generated by muscle contractions. In the automotive context, EMG provides insights into driver comfort, physical workload, and offers a potential pathway for novel control interfaces. Research has explored the application of surface EMG (sEMG) to assess muscle activity related to driver posture and comfort, particularly in response to vehicle dynamics such as lateral acceleration during cornering. This information can inform ergonomic seat design and suspension tuning, aiming to minimize driver fatigue and discomfort during extended journeys (Katsis et al., 2004).

Additionally, EMG is under investigation for direct vehicle control in specialized applications. By interpreting muscle signals, for example, from the forearm, researchers are exploring alternative input methods that could augment or replace conventional steering mechanisms (Wang et al., 2021, Nacpil et al., 2018). This area of research underscores EMG’s potential to contribute to more inclusive and adaptable vehicle designs.

Electrocardiography (ECG) for Physiological State and Health Monitoring

Electrocardiography (ECG) measures the heart’s electrical activity, yielding comprehensive information regarding a driver’s physiological state, including stress, emotional arousal, and cardiovascular health. Acute medical events, particularly cardiovascular incidents, represent a significant concern for road safety. Continuous, unobtrusive ECG monitoring within vehicles holds promise for detecting early indicators of cardiac distress, potentially enabling timely intervention and improved outcomes (Koh & Lee, 2019).

Current research focuses on integrating ECG sensors into various vehicle components, such as steering wheels (Koh & Lee, 2019) or car seats (Sakai et al., 2013), to enable non-contact or minimally intrusive measurements. These systems aim to derive heart rate (HR) and heart rate variability (HRV) metrics, which are established indicators of stress, cognitive load, and autonomic nervous system activity. By analyzing these physiological responses, automotive engineers can design systems that mitigate driver stress in challenging scenarios or even predict and avert situations that might induce anxiety or panic. Beyond safety, ECG data can also contribute to personalized in-cabin experiences, adapting environmental parameters such as lighting or temperature based on the driver’s physiological state.

Conclusion

The integration of ExG biosignals represents a transformative advancement in automotive R&D. By providing direct, objective measures of human physiological and cognitive states, EEG, EMG, and ECG enable a more profound understanding of human-vehicle interaction. This understanding is critical for developing advanced driver-assistance systems, intuitive HMIs, and features that prioritize occupant safety, comfort, and well-being. As the automotive landscape continues its trajectory toward increasing automation and connectivity, the role of biosignal integration will become increasingly prominent, fostering the development of a new generation of human-centric vehicles.

For researchers and developers requiring a versatile and robust platform for ExG biosignal acquisition in automotive applications, the Mentalab Explore Pro system offers a comprehensive solution. With up to 32 channels and research grade ExG data quality, it is ideally suited for demanding research environments. Its compact design, wireless & wired streaming capabilities, open software API, and compatibility with various electrode types, including dry electrodes, render it highly adaptable for both laboratory and in-vehicle studies, providing the precision and flexibility necessary to advance the field of automotive biosignal research.

References

• National Highway Traffic Safety Administration. (2025). Traffic Fatalities Decreased in the First Quarter of 2025. Retrieved from https://www.nhtsa.gov/press-releases/traffic-fatalities-decreased-first-quarter-2025
• National Sleep Foundation. (2024). 2024 Drowsy Driving Survey. Retrieved from https://www.thensf.org/wp-content/uploads/2024/10/Drowsy-Driving-Survey_2024.pdf
• Lal, S. K., & Craig, A. (2001). A critical review of the psychophysiology of driver fatigue. Biological Psychology, 55(3), 173-194.
• Stancin, I., Cifrek, M., & Jovic, A. (2021). A review of EEG signal features and their application in driver drowsiness detection systems. Sensors, 21(11), 3786.
• Figalová, N., Bieg, H. J., Reiser, J. E., Liu, Y. C., Baumann, M., Chuang, L., & Pollatos, O. (2024). From driver to supervisor: Comparing cognitive load and EEG-based attentional resource allocation across automation levels. International Journal of Human-Computer Studies, 182, 103169.
• Katsis, C. D., Ntouvas, N. E., Bafas, C. G., & Fotiadis, D. I. (2004, February). Assessment of muscle fatigue during driving using surface EMG. In Proceedings of the IASTED international conference on biomedical engineering (Vol. 262).
• Wang, Z., Suga, S., Nacpil, E. J. C., Yan, Z., & Nakano, K. (2020). Adaptive driver-automation shared steering control via forearm surface electromyography measurement. IEEE Sensors Journal, 21(4), 5444-5453.
• Nacpil, E. J., Zheng, R., Kaizuka, T., & Nakano, K. (2017, June). Implementation of a sEMG-machine interface for steering a virtual car in a driving simulator. In International Conference on Applied Human Factors and Ergonomics (pp. 274-282). Cham: Springer International Publishing.
• Koh, D. W., & Lee, S. G. (2019). An evaluation method of safe driving for senior adults using ECG signals. Sensors, 19(12), 2828.
• Sakai, K., Yanai, K., Okada, S., & Nishii, K. (2013). Design of seat mounted ECG sensor system for vehicle application. SAE International journal of passenger cars-electronic and electrical systems, 6(2013-01-1339), 342-348.

The post Driving to the future: ExG biosignals in the automotive industry appeared first on Mentalab.

Why wireless high-precision synchronisation matters

Achieving precise, research-grade wireless synchronization presents several complex technical challenges that must be carefully addressed to ensure high temporal fidelity in experimental data. Some challenges include:

• Clock Drift: Wireless devices operate with internal clocks that inevitably diverge over time. Without active correction, this drift can severely compromise temporal alignment across data streams, especially for longer recording periods.
• Network Latency and Jitter: Common wireless communication protocols such as Bluetooth and Wi-Fi exhibit variable transmission delays (latency) and unpredictable fluctuations in timing (jitter). These inconsistencies raise challenges for applications requiring sub-millisecond synchronization.
• Multi-Device Coordination: Synchronizing multiple data acquisition and stimulus delivery devices to a single, stable time source is inherently complex in wireless settings and demands careful system-level design.
• Seamless Ecosystem Integration: Any viable solution must integrate smoothly into existing research environments, including compatibility with established standards like the Lab Streaming Layer (LSL) for synchronized data handling and real-time acquisition. This allows for accurate synchronisation with other equipment such as Eye-trackers, SpO2 Sensors, and even cameras and microphones.

These challenges cannot be resolved through basic wireless transmission alone. Instead, they require a purpose-built, robust synchronization framework that is engineered to meet the stringent demands of (neuro)physiological research.

Our Solution: Mobility, Simplicity, Precision

Accurate synchronization of physiological data streams is crucial for understanding dynamic biological processes and their interactions. While traditional wired methods offer precision, they impose significant limitations, particularly in complex experimental setups. Hypersync effectively addresses these challenges, enabling enhanced flexibility and facilitating more comprehensive data analysis. Below, we highlight three key advantages of using our wireless solution:

1. Mobility: Conduct Naturalistic Hyperscanning Studies
   • Studying real-time brain-to-brain coupling (e.g., inter-brain synchrony) during social interaction requires that participants engage in natural movement and behaviour. Traditional wired setups often hinder this, limiting the authenticity of the interaction.
   • Our solution: By wirelessly synchronizing EEG systems across multiple participants (e.g., between parents and children, collaborating teams, therapist-client dyads, performers and audience members), researchers can explore neural correlates of shared attention, empathy, cooperation, and communication without any physical constraints. Capture the dynamics of interaction as they truly happen!

2. Simplicity: Capture Comprehensive Multimodal Recordings
   • To gain a comprehensive understanding of brain-mind-body interactions, it is desirable to record multiple physiological signals, such as EEG, ECG, EMG, EOG, and EGG, simultaneously. However, managing the cables from multiple amplifiers on a single participant can be complex and uncomfortable, potentially affecting both data quality and participant comfort.
   • Our solution: Effortlessly integrate and synchronize data from multiple Mentalab devices on a single individual. For instance, accurately align cortical activity (EEG) with heart rate variability (ECG) to investigate the heart-brain axis during emotional regulation, or correlate brain waves (EEG) with muscle tension (EMG) during motor tasks or stress responses. Additionally, explore complex interactions, such as the brain-gut connection (EEG+EGG), with ultimate ease and precision.

3. Precision: Achieve High-Fidelity Wireless Event Marking
   • Event-Related Potential (ERP) studies require precise timing between stimulus presentation and the corresponding neural response. Trigger cables, however, can introduce noise, restrict setup flexibility, and increase complexity, potentially compromising the accuracy and efficiency of your experiment.
   • Our Solution: Wirelessly set event markers directly from your stimulus presentation computer, ensuring precise timing synchronization between the computer and the Mentalab Explore Pro ExG amplifier. Conduct robust ERP research without the physical limitations of trigger cables, streamlining the setup process and enhancing overall data quality.

Mentalab’s high-precision wireless synchronization system includes a range of advanced features designed to enhance both usability and performance, including:

• Plug & Play Simplicity: Developed with researchers in mind, the system requires minimal setup and configuration. This allows users to concentrate on experimental design and data collection rather than troubleshooting complex hardware configurations.
• Compact & Mobile: The synchronization modules are lightweight and unobtrusive, preserving the portability of the Mentalab Explore system. This enables data acquisition in a wide range of environments, from controlled laboratory settings to naturalistic field studies.
• High Temporal Accuracy: The system ensures sub-millisecond synchronization accuracy between all connected devices and event markers, consistently maintained over extended recording sessions. This level of precision is critical for time-sensitive analyses, such as event-related potentials or cross-modal integration studies.
• Scalability for Multi-Device Setups: The architecture supports the simultaneous synchronization of multiple Mentalab Explore amplifiers, facilitating complex experimental paradigms such as hyperscanning or multimodal data acquisition.
• Lab Streaming Layer (LSL) Compatibility: Mentalab Explore devices support LSL, enabling real-time data acquisition through standard tools such as LabRecorder. This ensures seamless integration into existing analysis pipelines and allows for immediate compatibility with offline processing tools, including EEGLAB, FieldTrip, and MNE-Python.

Ready To Go Wireless?

Mentalab Hypersync system removes critical barriers, empowering you to design experiments previously deemed too complex or technically challenging. Study social interaction in ecological settings, capture comprehensive brain-body dynamics, and achieve cleaner, more reliable ERP data – all with unprecedented wireless freedom and precision.

• Schedule a personalized demo with our team to discuss how Mentalab can help get your research project off the ground.
• Use the contact form to get an in-depth look at Mentalab Hyperync.

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Perhaps the most important paper in neuroscience is called: “A quantitative description of membrane current and its application to conduction and excitation in nerve”. At the time of writing, the article has over 30,000 citations.

Written in the Journal of Physiology in 1952 by Alan Hodgkin and Andrew Huxley, the paper presents a mathematical model of action potentials in an axon.

It, along with their wider work in neurophysiology, earned the authors the Nobel Prize in Physiology and Medicine in 1963. But what does it say and what’s the history? Here, we explore one of the most famous papers in neuroscience.

Squids in 1939

In 1939, Alan Hodgkin, who had spent the previous summer studying squid giant axons in the USA, invited Andrew Huxley, a recent graduate in physiology from Cambridge, to join him in Plymouth to investigate nerve conduction.

With Hodgkin’s prior experience with squid axons, and Huxley’s fresh perspective, the two neuroscientists tried using mercury droplets to measure viscosity of cytoplasm within the axon of a squid. However, they were unsuccessful.

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Nonetheless, their setback was not fruitless. Inspired, they came up with the idea of inserting a fine capillary electrode into a nerve fibre to record membrane potential. This yielded mankind’s first intracellular action potential recordings.

Despite this promising start, the outbreak of World War II halted their research; the researchers had to contribute to the war effort, of course. As such, their findings, published in Nature in October 1939, marked the end of their initial, pre-war collaboration. These findings were already groundbreaking.

Voltage clamping in 1947

After seven years of war, during which the two scientists gained valuable insights from their non-academic endeavours, Hodgkin and Huxley reconvened. What they were about to do was measure, more precisely than ever before, the voltage potential of a squid axon.

Although it is called a “giant axon” it is, at its largest, 1.5mm in diameter. Typically, squid axons are half a millimeter in diameter. Giant for an axon; small in reality. The axon is associated with a squid’s propulsion system.

To measure this voltage potential, Hodgkin and Huxley would need to adopt voltage clamping.

What is voltage clamping?

Voltage clamping allows scientists to control the voltage across an axon membrane. The reason neuroscientists want to do this is that during an action potential, the voltage across the cell membrane is changing in time and space. This makes measuring the voltage across an axon incredibly difficult.

Voltage clamping solves this problem, by measuring the voltage potential across the cell membrane using one electrode, and then passing a current through the axon using another electrode. This current is designed to change with the changing potential so that the voltage is clamped at a certain value. Voltage clamping stops the voltage from changing.

In this way, what was really measured by early adopters of voltage clamping was the amount of current required to maintain the desired voltage potential.

Hodgkin and Huxley were not the first

Hodgkin and Huxley were certainly not the first to use voltage clamping on a squid’s giant axon. They are not even cited as its inventors!

However, they had discussed the idea prior to the war’s end, and they contributed significantly to the implementation of voltage-clamping by the end of 1949. Specifically, they introduced a second electrode, which solved problems associated with electrode polarization.

This dual electrode approach enabled Hodgkin and Huxley to directly record the ionic currents across a squid’s axon without altering its voltage potential. As such, they could investigate the voltage sensitivity and kinetics of the underlying ion channels.

We should note that, post-war, Hodgkin and Huxley were aided in some experiments by Bernard Katz.

The Hodgkin-Huxley model in 1952

After all of this, then, what is the famous paper all about?

The article was in fact a summary of four previous papers where the authors used voltage clamping, and other tools, to investigate the electrical activity of action potentials in a squid giant axon.

The paper is split into three parts. In part 1, the authors discuss their previous experimental results. In part 2, the authors present a mathematical description of the current in an axon during a voltage clamp. In the final part, the authors describe how they use their mathematical descriptions to predict the behaviour of the squid giant axon.

What is the Hodgkin-Huxley model

One of the reasons the paper is so popular, is that it presents, in part 2, what is known as the Hodgkin-Huxley model. The model describes the mechanisms underlying the generation and propagation of action potentials in excitable cells, particularly in neurons.

The model consists of a system of ordinary differential equations that describe how ion currents change across a neuron’s membrane. Specifically, it considers how the currents of sodium (Na+), potassium (K+), capacitance and leak contribute to changes in membrane potential.

Without going into took much detail, by summing each of these currents iteratively, one can derive the time course of an action potential with high precision. This is remarkable given the number of factors the Hodgkin-Huxley model omits.

Despite its simplicity, the model provides a robust framework for understanding the electrical behaviour of neurons and has served as the foundation for subsequent computational models of neuronal activity. Most importantly, it has advanced our understanding of the principles governing electrical signalling in the nervous system.

In fact, without such solid research into neuronal firing dynamics it is questionable whether we would have had such great progress in the artificial neural networks that now make news headlines almost daily. Such counterfactuals we will never know.

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The techniques employed by Hodgkin and Huxley and many other early neuroscientists were highly invasive procedures. Still today, amazing technological breakthroughs allow scientists to measure molecular changes in vitro.

However, we can measure neuronal firing patterns non-invasively too using EEG. Small, mobile EEG amplifiers, like Mentalab Explore+, make this easier than every. They allow participants to move around and relax without obstructive wires that tether them to a stationary amplifier.

Much of what is written in this article was taken from the wonderful perspective by Schwiening (2012). Do take a look for more!

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The world of science is a world of publication counts and citation statistics. And in neuroscience, things are no different. It seems now, more than ever, that having your work published determines the trajectory of your career (although thankfully, this isn’t always true).

To get more insight, we were interested in what gets published, and, perhaps more importantly, what gets cited.

Fortunately, we weren’t the first to do this. In 2017, Yeung and colleagues analysed papers from the Web of Science to distil who was doing the most cited research in neuroscience, and what their topics were. We thought you might like to know too, so here’s a summary.

The Study

With the overwhelming volume of neuroscience publications, identifying key research topics can be challenging for clinicians and scientists. To address this gap, Yeung et al. performed a bibliometric analysis to identify and characterize the 100 most-cited articles in neuroscience.

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The study utilized data from the Web of Science, focusing on publication year, journal, impact factors, citation counts, reference lists, authorship, and article types.

Results

The total citation count for the top 100 articles ranged from 2,138 to 7,326, with a mean of 3,087. Most articles were research-oriented (67%) and published between 1996 and 2000 (30%).

Stephen M. Smith and Science emerged as the leading author and journal, with six and thirteen contributions, respectively.

Thirty-seven articles formed an interlinked citation network, categorised into five major topics: neurological disorders, prefrontal cortex/emotion/reward, brain network, brain mapping, and methodology.

Intriguingly, 41 of the remaining non-interlinked articles also aligned with these five topics.

Detailed Analysis

Bradford’s law essentially suggests that a few journals and articles are cited the most, with exponentially diminishing returns outside this group. The distribution of citations among articles Yeung et al. reviewed did not adhere to Bradford’s law. This suggests a more even distribution of attention.

Major contributing journals included Science, NeuroImage, Neurology, Nature, and Proceedings of the National Academy of Sciences, and the peak in publications occurred during 1996–2000.

In terms of authorship, 533 contributors were identified, 55 of which were listed in the Highly Cited Researchers 2016 list.

Noteworthy authors contributing to three or more articles included Stephen M. Smith, John Ashburner, and Mark Jenkinson. The citation network revealed five distinct topics, with the majority of contributions focusing on neurological disorders and methodology.

Despite the lack of a significant correlation between adjusted impact factor and citation count, a two-step clustering analysis highlighted a distinct cluster of articles from high-impact journals. However, normalized citation counts did not significantly differ between clusters.

Interestingly, a positive correlation was observed between years since publication and normalized citation count, indicating that newer articles had lower normalized citation counts, possibly due to the exponential growth in neuroscience publications.

Something Is Missing

Although the authors appear to have carried out an objective, data-driven analysis of neuroscience papers, there seem to be one glaring omission!

The classic Hodgkin and Huxley papers that define the neuroscientific community are not mentioned in the report. This, despite one of their most famous papers “A quantitative description of membrane current and its application to conduction and excitation in nerve” having, at the time of writing (admittedly seven years later), almost 30,000 citations.

Perhaps most of those citations came in the last seven years. Perhaps not. In either case, we delve into this paper in another blog post. So do take a look!

Conclusion

The study’s findings contribute a comprehensive overview of the 100 most-cited articles in neuroscience, aiding the identification and recognition of pivotal research topics. While acknowledging the limitations of citation analysis, the study provides valuable insights into the evolving landscape of neuroscience literature and highlights the diverse niches within the field.

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Neural oscillations, also known as brain waves, are rhythmic patterns of neural activity that occur at various levels of the central nervous system. But how do they work? And what are they for? Here, we explore neural oscillations and their significance.

What are the different brain wave bands?

Neural oscillations can be generated by individual neurons or groups of neurons interacting with one other. They occur at various frequencies, and each is associated with specific cognitive functions or states of consciousness. For instance:

• Delta waves (0.5-4 Hz) are slow waves predominant during deep sleep. It is argued that they facilitate restorative processes and memory consolidation.
• Theta waves (4-8 Hz) are prominent during REM sleep and deep relaxation. They are implicated in creative thinking and meditation.

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• Alpha waves (8-12 Hz) are observed during relaxed wakefulness with eyes closed. They tend to indicate a state of calm alertness and are, again, associated with enhanced creativity and mindfulness.
• Beta waves (12-30 Hz) are often present during active thinking, problem-solving, and focused attention. They reflect heightened cognitive engagement and are typical during wakeful consciousness.
• Gamma waves (>30 Hz) are the highest frequency brain waves. They tend to be studied in relation to perception, learning, and memory encoding. They represent synchronized firing of neuronal assemblies and are thought to facilitate neural integration and information processing.

Where do neural oscillations come from?

Neural oscillations occur at different levels of brain function. They can be observed as alterations in the electrical charge across the cell membrane of single neurons (known as changes in membrane potential).

They also emerge at the meso-scale as coordinated patterns of activity across many neurons. In fact, these synchronized groups of neurons are often responsible for the kind of electrical activity described by electroencephalography (EEG).

Finally, at the macro-scale, different brain regions can interact with each other and generate large-scale oscillations.

Various mechanisms contribute to the generation of neural oscillations across these different scales. These include the intrinsic properties of neurons and network properties arising from synaptic interactions. Additionally, neuromodulators regulate oscillatory activity over longer time scales.

In fact, external stimuli can also modulate neural oscillations. For instance, environmental factors like sound and light can influence the amplitude and frequency of brainwaves.

As an example of this, consider steady state visually evoked potentials (SSVEPs), which describe how brain oscillations are entrained by a flickering light. That is, if you look at a flickering light long enough, neurons in the visual cortex will fire at the same frequency as the flickering light. This is detectable by EEG.

We explored SSVEPs in BCIs and SSVEPs as a way to increase signal-to-noise ratio in previous blog posts.

In fact, in recent work, Hainke and colleagues managed to induce gamma-range neural oscillations during sleep by creating a sleep mask that flickers light gently during the night. This provides a promising technique to promote gamma oscillations in patients with Alzheimer’s.

How can I measure brain waves?

To identify brain waves, researchers have to select a method that is appropriate to the scale they are investigating. For instance, at the lowest level, researchers may conduct so-called single-unit recordings, where they measure individual neurons using techniques like patch-clamping.

Patch clamping was developed by Erwin Neher and Bert Sakmann some 50 years ago. It allows researchers to study the electrical current across individual living cells or cell membranes. This is, of course, particularly useful for brain research.

Patch clamping employs either a voltage clamp or current clamp to control membrane voltage or current, respectively. A micropipette filled with electrolyte solution and connected to an amplifier is used to form an electrical circuit with an isolated cell membrane. Researchers can then measure the ionic currents and channels in real time.

Of course, patch clamping, and other single-unit recordings using microelectrodes are invasive procedures. However, this is not the only way to research brain waves.

Anyone who has used EEG will have come into contact with synchronized firing patterns. This is because neural oscillations are so prevalent that if you place an electrode on a participant’s head, you are bound to identify at least one (although more commonly multiple) bands of oscillations.

Importantly, small, mobile EEG amplifiers, like Mentalab Explore Pro, are capable of detecting these frequency bands and associating them particular brain function. They allow participants to engage in ecologically valid experiments without obstructive wires that are tethered to a stationary amplifier.

What are brain waves for?

Scientists have studied neural oscillations since the early 20th century. Significant advancements in brain imaging technologies greatly contributed to their study.

Researchers believe neural oscillations have diverse functions in cognition. For instance, they appear to be implicated in cortical information transfer. That is, by synchronizing their firing rates, neurons that are far apart from one another, but are responsive to the same stimulus, generate a so-called “relational code” that allows them to jointly process information.

Neural oscillations are also thought to be implicated in feature binding. The feature binding problem describes how we attribute features to objects. For instance, Zhang and colleagues used EEG to argue that alpha oscillations have a causal role in feature binding.

In any case, a unified understanding is still evolving. What we can say is that abnormal oscillations are associated with neurological disorders like epilepsy and Parkinson’s disease.

Therefore, understanding the mechanisms and roles of brain waves is crucial for advancing our knowledge of brain function and developing potential therapeutic interventions.

Conclusion

In summary, neural oscillations are the rhythmic patterns of electrical activity in the brain that coordinate communication between different brain regions. By studying brainwaves, we may gain insights into the mechanisms underlying consciousness and cognitive functions.

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