Attendees had the opportunity to experience Bitbrain's neurotechnology portfolio firsthand, including 

 systems. These solutions are designed to support a wide range of 

, from controlled experimental studies to data collection in more naturalistic environments.

Brain monitoring beyond the lab takes the spotlight at CuttingGardens EEG 2026 

Bitbrain makes a growing collection of open-access EEG datasets available to the scientific community, covering sleep monitoring, wearable EEG and recordings acquired with different sensor technologies.

The set contains 128 nights of simultaneous polysomnography (PSG) and wearable EEG recordings acquired using Bitbrain Ikon Sleep, providing 

Other datasets include recordings registered with textile-based EEG technology, as well as data collected using gel, water-based, and dry EEG systems. These resources let researchers explore and compare different EEG acquisition approaches.

Bitbrain is offering open EEG datasets for neuroscience research

Traditional implicit measures rely on reaction times to infer the strength of associations between mental concepts. While these methods are widely used, they provide limited insight into the neural processes underlying decision mechanisms.

EEG technology is increasingly expanding this field by allowing researchers to observe neural dynamics during implicit tasks in real time. This enables a more detailed understanding of attention, information processing, and semantic evaluation.

Advances in implicit cognition research combine behavioral tests with EEG and AI 

The project began as a final-year engineering thesis by Marta Rodríguez Comino, supervised by Dr. Joaquín T. Valderrama and Dr. Iván López Espejo at the 

Participants were asked to focus on a chosen digit while numbers appeared on a screen. When the target appeared, it triggered a stronger P300 response, a brain signal associated with attention. Using 

, signal processing and machine learning, the system could distinguish the attended digit from the others.

P300 brain-computer interface moves from lab to classroom

Sleep staging is essential for assessing sleep and sleep disorders, but conventional approaches often rely on complex laboratory setups and expert analysis. This study investigated whether automatic sleep scoring could be simplified while maintaining reliable performance.

Using a convolutional neural network (CNN), the researchers analyzed four open-access datasets from healthy participants and people with obstructive sleep apnea. The results showed that a single frontal EEG channel can provide reliable sleep-stage classification, with only minimal improvements when additional 

 channels or biosignals such as EOG, EMG and ECG are introduced.

The study also compared offline methods with causal approaches suitable for real-time use. Real-time sleep scoring achieved performance close to offline methods, supporting its potential for applications that need to identify sleep stages as they occur.

New study advances real-time sleep scoring with single-channel EEG

The post underlines its role in aligning neural activity with experimental events and stimuli, enabling reliable analysis of event-related potentials and other time-sensitive measures.

EEG synchronization involves accurately marking when events occur and aligning recordings from multiple devices. For example, combining EEG with 

 requires researchers to match brain activity to the moment a participant fixates on a target.

How EEG synchronization improves the reliability of research data

This year's programme includes a dedicated 

, highlighting innovative tools and methodologies designed to support research and clinical practice.

 will provide insight into new approaches for understanding cognition, brain health, and physiological function through increasingly accessible monitoring solutions.

Sleep Europe 2026: Bitbrain prepares for Europe's leading sleep congress

The certification provided both theoretical and practical training in 

 methods, emphasizing the application of advanced research techniques and transforming data into meaningful insights. During the event, Human Behaviour Research Product Specialist 

 presented the value of combining Eye Tracking with EEG and other biometric signals to support 

 and uncover both conscious and unconscious aspects of human experience. 

Bitbrain participates in the international eye tracking certification in Mexico 

López Valdés studied Mathematics at the University of Zaragoza and later earned a PhD in Computational Engineering, followed by an MBA from IE Business School. In 2010, she founded Bitbrain, a neurotechnology company focused on brain monitoring and brain–computer interfaces. Bitbrain’s products are widely used in neuroscience investigation and practical human-behavior studies, supporting applications in cognitive science, neurotechnology, and human–machine interaction. Its 

 and biosignal systems enable the analysis of brain activity, attention, cognitive workload, and user engagement across both research and practical contexts.

What began as an academic initiative in Zaragoza has evolved into an international reference in neurotechnology, applied in analysis, healthcare, and industry.

María López Valdés Recognized as “Influential Woman of the Year” in Aragón

We use our own and third-party cookies to analyse the use of the website and to show you personalised advertising. You can set your preferences about these cookies <lnk>here</lnk> or use the buttons below.

Bitbrain Highlights