Mobile EEG for Brain-Computer Interface (BCI) and Neurofeedback
Brain-computer interfaces (BCIs) translate neural activity into meaningful actions, enabling direct communication between the brain and external devices. As a leader in neurotechnology, Mentalab provides the foundations for BCI research and neurofeedback systems. Our high-precision mobile EEG technology supports scientists and innovators in developing reliable, real-time neurointerfaces.
Our Solution
- Real-time data access: Direct access via open-source Python API or Lab Streaming Layer (LSL).
- High-Resolution Recording: Wired & wireless EEG recording with up to 32 channels and sampling rates up to 8 kHz.
- Interoperability: Seamless integration with leading BCI research platforms (PsychoPy, Psychtoolbox, NeuroPype).
- Precision Timing: High-precision wireless event marking with sub-millisecond latency (< 1 ms).
- Ergonomic Design: Lightweight, wearable EEG design optimized for naturalistic studies and mobile environments.
Choose our Mentalab Explore Pro Wireless setup, combined with our high-precision synchronisation solution Mentalab Hypersync
Resources
Publications
Real-Time Navigation in Google Street View® Using a Motor Imagery-Based BCI
Yang, L., & Van Hulle, M. M. (2023). Real-Time Navigation in Google Street View® Using a Motor Imagery-Based BCI. In Sensors (Vol. 23, Issue 3, p. 1704).
Effect of Ensemble Learning Techniques on RGB-evoked EEG Signal Classification for Locked-in Syndrome BCI Communication
Scheline Urdahl, P., Omsland, V., Molinas, M., Soler, A. Effect of Ensemble Learning Techniques on RGB-evoked EEG Signal Classification for Locked-in Syndrome BCI Communication. Preprint.
EMD–Fuzzy: An Empirical Mode Decomposition Based Fuzzy Model for Cross-Stimulus Transfer Learning of SSVEP
Cao, B., Jiang, X., Leong, D., Tsai, C. L. T., Chang, Y. C., & Do, T. (2025). EMD-Fuzzy: An Empirical Mode Decomposition Based Fuzzy Model for Cross-Stimulus Transfer Learning of SSVEP. arXiv preprint arXiv:2501.17475.
Noninvasive Sensors for Brain–Machine Interfaces Based on Micropatterned Epitaxial Graphene
Faisal, S. N., Do, T.-T. N., Torzo, T., Leong, D., Pradeepkumar, A., Lin, C.-T., & Iacopi, F. (2023). In ACS Applied Nano Materials (Vol. 6, Issue 7, pp. 5440–5447).
Feasibility of a Mobile Electroencephalogram (EEG) Sensor-Based Stress Type Classification for Construction Workers
Lee, G., & Lee, S. (2022). Feasibility of a Mobile Electroencephalogram (EEG) Sensor-Based Stress Type Classification for Construction Workers. In Construction Research Congress 2022. American Society of Civil Engineers.
Psychological stress detection with optimally selected EEG channel using Machine Learning techniques
Marthinsen, A. J., Galtung, I., Cheema, A., Sletten, C., Andreassen, I. M., Sletta, Ø., … & Molinas Cabrera, M. M. (2023). Psychological stress detection with optimally selected EEG channel using Machine Learning techniques. In Italian Workshop on Artificial Intelligence for Human-Machine Interaction (AIxHMI 2023).
EEG source imaging of hand movement-related areas: an evaluation of the reconstruction and classification accuracy with optimized channels
Soler, A., Giraldo, E., & Molinas, M. (2024). EEG source imaging of hand movement-related areas: an evaluation of the reconstruction and classification accuracy with optimized channels. Brain Informatics, 11 (1).