One of the most important scientific and technological frontiers of our time is the interfacing of electronics with the human brain. The convergence between engineering and neurobiology brings about new discoveries in the brain, as well as innovations for future brain-technologies-interfaces. NBT Berlin provides an interdisciplinary dialogue platform for engineers, industry, and academic researchers. Focus areas cover from the latest techniques of neural data acquisition, signal analysis and interpretation, up to solutions for neuroelectronic interfaces and control of assistive devices with electrophysiological signals.
Sessions include:
Recording neuronal activity triggered by electrical impulses is a powerful tool in neuroscience research and neural engineering. It is often applied in acute electrophysiological experimental settings to record compound nerve action potentials. However, the elicited neural response is often distorted by electrical stimulus artifacts, complicating subsequent analysis. In this talk, we present a model to better understand the effect of the selected recording amplifier configuration and the location of the ground electrode in a practical electrophysiological nerve setup. We present simulations results that show that the stimulus artifact can be reduced by more than an order of magnitude if the placement of the ground electrode, its impedance, and the amplifier configuration is optimized. We also present experimentally results which demonstrate the effects in three different settings, in-vivo and in-vitro.
Up to this day, most experiments and data analysis techniques focus on data recorded inside laboratory environments or simulations. However, life does not happen inside a laboratory but instead occurs mostly outside of it. There exists a gap between well-established data analysis methods and their applicability to data recorded in daily life. We conducted a user-centered study to provide methods for researchers to analyze physiological data from multi-modal EEG, PPG, and EDA sensors when participants are under mental workload and stress in daily life. While using laboratory measurements as reference data, we discuss some challenges encountered and the first insights we gained in working with data from uncontrolled environments.
Brain computer interfaces (BCI) offer high precision for treating brain disorders, but there is a lack of standards for integrating data and estimating features using machine learning. To address this issue, we present a software framework called py_neuromodulation that combines adaptive deep brain stimulation and connectomics to facilitate brain circuit discovery for brain implants. We tested this platform on data from 73 patients with movement disorders, depression, and epilepsy. We identified Network targets for emotion decoding in depression patients, performed generalized movement decoding across cohorts with Parkinson’s disease from the US, Europe, and China, and improved seizure detection in responsive neurostimulation for epilepsy. The platform's capabilities aid precision medicine by enabling personalized therapy for patients with brain disorders.
Neural probes are crucial for diagnosis and treatment, as well as in prosthetic devices. However, the durability of these probes is limited due to scarring and material deterioration, which can lead to a decrease in signal quality. While materials, coatings, flexibility, and surgical techniques are explored to overcome these challenges, a systematic and industry-compatible process for long-term testing of new probe technologies is still lacking. The development of objective quality criteria for intracortical implant testing could reduce the risk for developers and improve treatment of patients suffering from neural diseases. Therefore, we are developing quality criteria, ranging from device characterization to data analysis, to enable reliable assessment of long-term functionality and further improve intracortical longevity.
This talk will focus on the validation of in-ear EEG using full scalp EEG biomarkers. Over a series of experiments, we have used our in-ear EEG sensor to reproduce standard EEG biomarkers such as such as ASSR, SSVEO, SSEP, HEOG, Alpha etc. as well as sleep-based biomarkers such as K-complexes, sleep spindles, slow waves, arousal and REM. Our results showed that in-ear EEG compares favourably to the gold standard scalp EEG in the detection of these biomarkers. This talk will explain in detail the types of biomarkers studied and to what extent they were reproduced, as well as protocols used to elicit them and analysis methods.
Prior research indicates that resting state theta power predicts response to hypnosis treatment, suggesting the possibility that neurofeedback to enhance theta power might enhance response to clinical hypnosis. To examine this possibility in a pilot study, 22 individuals with multiple sclerosis and pain or fatigue were randomly assigned to five sessions of hypnosis treatment alone or five sessions of hypnosis combined with 10 sessions of neurofeedback. The results indicated larger improvements in pain intensity, sleep disturbance, and depression in participants who received the combined treatment than who received HYP alone. No between-group differences in fatigue were found. The results are consistent with the possibility that NF could be used to enhance response to hypnosis interventions.
We demonstrate the use of epitaxial graphene on silicon carbide on silicon as dry EEG sensors for a wearable brain-machine interface (BMI) system which is reliable upon long-term usage. The produced sensors are wafer- thin, highly biocompatible, and show a lower contact impedance as compared to their bulkier commercial sensor counterparts and are are remarkably resilient to corrosion in saline environments. The BMI evaluation used a steady -state visual evoked potential paradigm (SSVEP), presenting the individual with up to 6 command options to control hands-free the movement of a robotic quadruped. We have demonstrated up to 94% accuracy in the lab using 6 graphene sensors mounted on a rudimental helmet in the presence of 5mm length hair.
Non-invasive brain stimulation (NIBS) is widely used for neuromodulatory purposes. When applied in sleep medicine, the main hypothesis explaining its effects concerns the induction of neural plasticity and the modulation of the strength of synaptic connections between the brain areas involved in sleep disorders. Recently, a multi-database-based search converges on the evidence that NIBS is safe and effective in insomnia, restless legs syndrome, and sleep deprivation-related cognitive deficits; limited data are available for sleep bruxism and REM sleep behavior disorder, whereas no relevant effect was observed in obstructive sleep apnea syndrome and narcolepsy. Some limitations, especially regarding the population studied and the stimulation protocols adopted, need to be considered, although the development of individually tailored neuromodulatory techniques is promising.
We will present a new binocular laser pupillometer, aimed to study the pupil light reflex with unprecedented precision. The laser pupillometer provides for the first time precise spatially-selective illumination of the eye and stimulation of the retina. It combines visible and infrared laser light, the former used for stimulus, and the latter for providing pointing information. The spatial selectivity provides order-of-magnitude increase in the neural information that can be extracted from measurements of the pupil diameter dynamics, and thus can lead to novel methodologies for addressing brain function and developing new medical diagnostic tools.
