Topic: brain-computer interfaces

Researchers from neuroscience, engineering and clinical practice will examine technologies for understanding brain function and treating disorders. Three tracks cover emerging neural recording and imaging methods, machine learning and brain-inspired computation, and clinical translation through 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

Portable biosensors become more popular every year. In this context, I propose NeuriGUI, a modular and cross-platform graphical interface that connects to those biosensors for real-time processing, exploring and storing of electrophysiological signals. The NeuriGUI 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.

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

Over the last few decades, the use of deep brain stimulation (DBS) to improve the treatment of those with neurological movement disorders represents a critical success story in the development of invasive 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

Restoring hand function is a top priority for individuals with tetraplegia. This challenge 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

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

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