Brain-computer interfaces

…neuroprosthetics, neuromodulation and brain-computer interfaces. The programme connects multiscale measurement, interpretable models, multimodal data and biomarkers with deployment challenges. Panels consider ethics, standards, clinical needs, careers and funding; posters, live demonstrations, exhibits

Neural Interfaces brings together researchers, clinicians, engineers and other stakeholders working on neurotechnology, brain-computer interfaces, neuroprosthetics and neuromodulation. The programme is developed through community proposals and original research abstracts. Its scope spans clinical applications, neuroscience and neural engineering, data and AI, device technology, translation and outcomes, and ethics, policy and workforce questions. The meeting connects foundational research with implementation and adoption of neural technologies. The speaker-proposal deadline was 12 October 2026

…systems, brain–computer interfaces and artificial intelligence more accurate and robust. Scientific sessions connect biosignal processing, wearable sensing, neuroimaging, human–machine interaction and rehabilitation with intelligent industrial measurement and immersive environments. The programme explores diagnostics using deep learning and hybrid intelligence, motor-behaviour measurements that guide neurological rehabilitation, and the movement of generative AI and language models onto resource-cons

…Brain computer interfaces (BCIs) allow for task improvement to be directly linked to changes in neural activity. Recent experiments that pair BCIs with all-optical circuit mapping allow for the simultaneous tracking of in vivo changes in connectivity while task-relevant activity learning occurs. In silico models of learning allow for th

Brain-computer interfaces (BCIs) have the potential to enable seamless interaction between humans and technology, with particular power for helping patients with paralysis, motor disabilities, or other neurodegenerative diseases move or communicate with the world. BCIs use ``decoders'‘ to relate neural activity to some desired action. Early approaches used highly specific and targeted statistical models, which required careful adaptation to different sessions or days for the same user, and lacked the ability to generalize across users or tasks. Recently, the availability of a large amount of n

Transcranial magnetic stimulation (TMS) is a promising tool for neuromodulatory interventions in research and clinical settings, yet its effects are highly variable. Real-time EEG-TMS seeks to mitigate this variability by analyzing electroencephalography (EEG) signals to determine the optimal timing for stimulation. However, existing EEG-informed TMS methodologies are predominantly open-loop, relying on predefined brain states and disregarding immediate and long-term stimulation effects [1, 2, 3]. Here, we propose an adaptive closed-loop TMS approach that allows dynamically adjusting stimula

Robotic-assisted rehabilitation therapies play a crucial role in improving motor recovery by providing precise, repeatable, and intensive therapeutic interventions that traditional methods often lack [1,2,3]. Nonetheless, their adaptability is restricted due to a lack of comprehensive data on patients' biophysical signals and behavior [4,5,6]. Developing intelligent control mechanisms for rehabilitation devices is an essential step to enable personalized and responsive training for patients. This research project introduces the development of an innovative multimodal decoder for hybrid Brain-

Brain-computer interfaces (BCIs) are a powerful tool in both clinical applications and basic science research. Traditionally, BCIs rely on electrical signals collected by electrode arrays, which usually suffer from instability due to electrode movements and cell loss [1]. Recent approaches to address this issue continuously update BCI decoders [2]. By reliably identifying the same cells over days, using $Ca^{+2}$ imaging as BCI input may circumvent these limitations, and enable simultaneous recording from large neuronal populations over extended periods. Nonetheless, $Ca^{+2}$ imaging data typ

Neuroscientific studies often involve some form of group analysis over multiple participants. This requires alignment of recordings across participants. A naïve and commonly used solution is to assume that participants' recordings can be aligned anatomically in sensor space. However, this assumption is likely violated due to functional-anatomical organization differences between individual brains. In this work, multiple methods were compared to find a common representation of magnetoencephalographic (MEG) activations recorded from different participants performing a grasping task with three d

By causally mapping neural activity to behavior [1], Brain-Computer Interfaces (BCI) offer a means to study the dynamics of sensorimotor learning. Here, we combine computational modeling and data analysis to study the neural learning algorithm [2] monkeys use to adapt to a changed output mapping in a center-out reaching task. We exploit that the mapping from neural space (ca. 100 dimensions) to the 2D cursor position is a credit assignment problem [3] that is underconstrained, because changes along a large number of output-null dimensions do not influence the behavioral output. We hypothesized

Eliciting percepts akin to natural vision using brain computer interfaces is the holy grail of vision prosthetics. However, progress has been slowed by our lack of understanding of how the human visual cortex processes and encodes information as well as how external perturbations, such as electrical stimulation via multi-electrode arrays, might perturb the recurrent cortical dynamics. Furthermore, investigating these questions directly remains difficult as we rarely have the opportunity to probe the human cortex in-vivo. From this limitation and thanks to the current exponential increase in

Understanding how the brain processes motor and tactile imagery is crucial for advancing motor control and rehabilitation.However,there is a knowledge gap in research where EEG recordings are combined with TMS to study the effects of imagery:little is known about tactile imagery (TI) effects.In this study, we aimed to bridge this gap by examining corticospinal excitability during motor imagery (MI) and TI using TMS-EEG.Our previous findings demonstrated that both MI and TI increase corticospinal excitability,with MI having a stronger effect,whereas tactile stimulation (TS) decreases corticospi

Motor imagery (MI) is defined as a mental simulation of movements without any actual motor action. In last decades, several studies had advanced our understanding of this mental process. They notably showed shared neuroanatomical substrates between MI and physical practice. These neuronal networks seem to be dependant of individual expertise. Although the scientific literature seems to agree for a reduced and focused brain activity as expertise increases, a few investigated complex whole-body movements because MI is usually studied through standardized common tasks (e.g., keyboard sequences).

Motor Imagery (MI) based Brain-Computer Interfaces (BCI) can facilitate very early motor rehabilitation during the acute post-stroke period that is characterised by both greater impairment and, paradoxically, greater plasticity. Current Electroencephalography (EEG) based approaches require substantial time and effort to learn to use. We suggest that a multimodal and multiphase approach may allow stroke survivors to learn to use the BCI more rapidly, enabling early bedside intervention.In phase one, for two sessions, 17 participants learned to perform MI with the help of Transcranial Magnetic S

Aims: nowadays, commercial lightweight electroencephalography (EEG) headsets such as Emotiv EPOC and Muse are gaining popularity in the neuroscience community, mostly in research that monitors natural behaviour outside traditional laboratory settings. These devices often utilize only a few dry or active electrodes in specific locations and signal quality is often inferior compared to their traditional counterparts equipped with electrode caps that require conductive paste for proper functioning. In this study, we wanted to assess the feasibility of a portable, dry electrode-based EEG headset m

…acts as a common entry point in brain-computer interfaces, making it possible to plug in downstream third-party applications for real-time analysis of the incoming signal. NeuriGUI is 100% free and open source.

Brain-computer interfaces (BCIs) offer a unique method to study learning dynamics by defining a causal mapping between neural activity and movement. Examples include studying the acquisition of an arbitrary sensorimotor mapping or perturbing a learned mapping to study adaptation. Alternatively, the sensorimotor mapping (“decoder”) can be purposefully adapted in closed loop, as the subject controls the BCI, to improve performance. Beyond improving performance for applications, closed-loop decoder adaptation (CLDA) also allows the study of learning mechanisms under changing motor demands. Here,

Brain-computer interfaces (BCI) have important applications both in medicine and as a research tool. In recent years the field of calcium imaging based BCIs has been accelerating1–4. Calcium imaging has the advantages of recording hundreds of cells simultaneously, and tracking the same neurons over many days, allowing a longitudinal analysis of neural code development and dynamics5. The collected data usually requires post experiment offline processing to extract single cells’ activity. Yet, emerging techniques for real-time processing of imaging data offer the potential for novel BCI experim

Proprioception is fundamental for the control of movement, its loss producing profound motor deficits. Yet basic questions of how pose and movement are represented, as well as how these representations are arranged across the somatosensory cortex, are unclear. To this end, we adopt a task-driven modelling approach, using a spiking variational autoencoder to approximate a population of cortical neurons. We optimize the model to encode natural movement stimuli derived from recordings of human kinematics and impose biological constraints which we hypothesise to be important for reproducing charac

Learning in neural circuits manifests via slow changes in activity driving behaviour changes. Uncovering how the coordination of plasticity mechanisms found in the brain leads to such changes is a difficult problem, chiefly because we do not have access to the synaptic parameters underlying neural computation. Nevertheless, as neural population recording techniques improve and experiments track changes in neural dynamics over long durations, finer details about plastic changes could be inferred by observing and characterizing changes in neural activity. In this work, we present a step towards

Neuroscientific studies often involve some form of group analysis over multiple participants. This requires alignment of recordings across participants. A naive solution is to assume that participants’ recordings can be aligned anatomically in sensor space. However, this assumption is likely violated due to anatomical and functional differences between individual brains. In magnetoencephalography (MEG) recordings the problem of inter-individual alignment is exacerbated by the susceptibility of MEG to individual cortical folding patterns as well as the inter-individual variability of sensor loc

…neurotechnology and the promise of brain-computer interfaces (BCI) to improve the lives of those suffering from incurable neurological disorders. In the last decade, investigational devices capable of recording and streaming neural activity from chronically implanted therapeutic electrodes has supercharged research into clinical applications of BCI, enabling in-human studies investigating the

…motivates considerable research on brain-computer interfaces (BCIs), which bypass damaged neural pathways to control paralyzed or prosthetic limbs. Here, we demonstrate the BCI control of a prosthetic hand using intracortical recordings from the posterior parietal cortex (PPC). As part of an ongoing clinical trial, two participants with cervical spinal cord injury were each implanted with a 96-channel array in the left PPC. Across four sessions each, we recorded neural activity while they attempted to pr

Brain-computer interfaces (BCIs) have afforded paralysed users “mental control” of computer cursors and robots, and even of electrical stimulators that reanimate their own limbs. Most existing BCIs map the activity of hundreds of motor cortical neurons recorded with implanted electrodes into control signals to drive these devices. Despite these impressive advances, the field is facing a number of challenges that need to be overcome in order for BCIs to become widely used during daily living. In this talk, I will focus on two such challenges: 1) having BCIs that allow performing a broad range o