Modern UX research increasingly complements classic methods like interviews and usability tests with neuroscientific measures. Among these, electroencephalography (EEG) stands out as a powerful tool for quantifying brain activity and revealing subconscious user reactions (Borawska & Mateja, 2024). Combined with behavioural measures like eye-tracking and other biophysiological signals, e.g., measuring skin conductance, EEG enables a more holistic view of the user experience, capturing subtle shifts in emotion, attention, and mental workload that self-reports often miss.
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.
