The Berlin Brain–Computer Interface: Non-Medical Uses of BCI Technology

Brain–computer interfacing (BCI) is a steadily growing area of research. While initially BCI research was focused on applications for paralyzed patients, increasingly more alternative applications in healthy human subjects are proposed and investigated. In particular, monitoring of mental states and decoding of covert user states have seen a strong rise of interest. Here, we present some examples of such novel applications which provide evidence for the promising potential of BCI technology for non-medical uses. Furthermore, we discuss distinct methodological improvements required to bring non-medical applications of BCI technology to a diversity of layperson target groups, e.g., ease of use, minimal training, general usability, short control latencies

A biologically-inspired multi-modal evaluation of molecular generative machine learning

While generative models have recently become ubiquitous in many scientific areas, less attention has been paid to their evaluation. For molecular generative models, the state-of-the-art examines their output in isolation or in relation to its input. However, their biological and functional properties, such as ligand-target interaction is not being addressed. In this study, a novel biologically-inspired benchmark for the evaluation of molecular generative models is proposed. Specifically, three diverse reference datasets are designed and a set of metrics are introduced which are directly relevant to the drug discovery process. In particular we propose a recreation metric, apply drug-target affinity prediction and molecular docking as complementary techniques for the evaluation of generative outputs. While all three metrics show consistent results across the tested generative models, a more detailed comparison of drug-target affinity binding and molecular docking scores revealed that unimodal predictiors can lead to erroneous conclusions about target binding on a molecular level and a multi-modal approach is thus preferrable. The key advantage of this framework is that it incorporates prior physico-chemical domain knowledge into the benchmarking process by focusing explicitly on ligand-target interactions and thus creating a highly efficient tool not only for evaluating molecular generative outputs in particular, but also for enriching the drug discovery process in general.Comment: 59 pages, 26 figures Project GitHub repository, https://gitlab.com/cheml.io/abraha

EEG dataset and OpenBMI toolbox for three BCI paradigms: an investigation into BCI illiteracy

https://academic.oup.com/gigascience/article/8/5/giz002/5304369Background Electroencephalography (EEG)-based brain-computer interface (BCI) systems are mainly divided into three major paradigms: motor imagery (MI), event-related potential (ERP), and steady-state visually evoked potential (SSVEP). Here, we present a BCI dataset that includes the three major BCI paradigms with a large number of subjects over multiple sessions. In addition, information about the psychological and physiological conditions of BCI users was obtained using a questionnaire, and task-unrelated parameters such as resting state, artifacts, and electromyography of both arms were also recorded. We evaluated the decoding accuracies for the individual paradigms and determined performance variations across both subjects and sessions. Furthermore, we looked for more general, severe cases of BCI illiteracy than have been previously reported in the literature. Results Average decoding accuracies across all subjects and sessions were 71.1% (± 0.15), 96.7% (± 0.05), and 95.1% (± 0.09), and rates of BCI illiteracy were 53.7%, 11.1%, and 10.2% for MI, ERP, and SSVEP, respectively. Compared to the ERP and SSVEP paradigms, the MI paradigm exhibited large performance variations between both subjects and sessions. Furthermore, we found that 27.8% (15 out of 54) of users were universally BCI literate, i.e., they were able to proficiently perform all three paradigms. Interestingly, we found no universally illiterate BCI user, i.e., all participants were able to control at least one type of BCI system. Conclusions Our EEG dataset can be utilized for a wide range of BCI-related research questions. All methods for the data analysis in this study are supported with fully open-source scripts that can aid in every step of BCI technology. Furthermore, our results support previous but disjointed findings on the phenomenon of BCI illiteracy

Single Trial Classification of Motor Imagination Using 6 Dry EEG Electrodes

BACKGROUND: Brain computer interfaces (BCI) based on electro-encephalography (EEG) have been shown to detect mental states accurately and non-invasively, but the equipment required so far is cumbersome and the resulting signal is difficult to analyze. BCI requires accurate classification of small amplitude brain signal components in single trials from recordings which can be compromised by currents induced by muscle activity. METHODOLOGY/PRINCIPAL FINDINGS: A novel EEG cap based on dry electrodes was developed which does not need time-consuming gel application and uses far fewer electrodes than on a standard EEG cap set-up. After optimizing the placement of the 6 dry electrodes through off-line analysis of standard cap experiments, dry cap performance was tested in the context of a well established BCI cursor control paradigm in 5 healthy subjects using analysis methods which do not necessitate user training. The resulting information transfer rate was on average about 30% slower than the standard cap. The potential contribution of involuntary muscle activity artifact to the BCI control signal was found to be inconsequential, while the detected signal was consistent with brain activity originating near the motor cortex. CONCLUSIONS/SIGNIFICANCE: Our study shows that a surprisingly simple and convenient method of brain activity imaging is possible, and that simple and robust analysis techniques exist which discriminate among mental states in single trials. Within 15 minutes the dry BCI device is set-up, calibrated and ready to use. Peak performance matched reported EEG BCI state of the art in one subject. The results promise a practical non-invasive BCI solution for severely paralyzed patients, without the bottleneck of setup effort and limited recording duration that hampers current EEG recording technique. The presented recording method itself, BCI not considered, could significantly widen the use of EEG for emerging applications requiring long-term brain activity and mental state monitoring

Fortschritte der Neurotechnologie für Gehirn Computer Schnittstellen

Gehirn Computer Schnittstellen haben in den letzten 10 Jahren ein enormes wissenschaftliches Interesse hervorgerufen. Allerdings offenbart diese spannende Technology bei näherer Betrachtung noch einige Hürden, welche bisher die Entwicklung von massentauglichen Anwendungen verhindert haben. Unter Anderem eine lange Vorbereitungszeit eines BCI Systems, die fehlende Steuermöglichkeiten für manche Benutzer, sowie die nicht Stationaritäten innerhalb einer Aufnahme. Diese Dissertation führt eine Reihe von neurotechnologischen Entwicklungen ein, welche diese Probleme addressieren. Dadurch wird BCI zu einer kompakteren, robusteren und praktikableren Technologie. Eine patentierte EEG Kappe mit sechs trockenen Elektroden wird vorgestellt und ihre Funktion innerhalb der BCI Umgebung demonstriert. Während diese Entwicklung für BCI von Nutzen ist, wird auch zukünftige EEG Forschung von dieser Technologie profitieren. Zur weiteren Reduzierungs der Vorbereitungszeit, wurde ein Ensemble Framework entwickelt, welches aus einer grossen Menge von BCI Daten besteht. Mit Hilfe der Methoden des maschinellen Lernens erlaubt dieses Framework damit ein instantanes Feedback. Weiterhin wurde eine multi-modale Studie durchgeführt, welche die Inoperabilität des Systems für einige Benutzer reduzieren konnte, und desweiteren zu neurophysiologischen Erkenntnissen geführt hat.Brain Computer Interfacing has witnessed a tremendous growth of scientific interest during the last 10 years. However, some downfalls have prevented this exciting technology to produce mainstream applications for the general public. Among those are long setup time, illiteracy of some subjects as well as non-stationarities within recording sessions. This thesis introduces a number of hardware as well as software related neurotechnological developments, which address and alleviate these issues, thus making BCI a more compact, robust and ready-to-use technology. A patented dry electrode EEG cap with 6 channels is introduced and its capabilities demonstrated within a BCI environment. While this development certainly enhances BCI usability, also future EEG research will benefit from dry electrode technology. To further reduce setup time to essentially zero, an ensemble framework, consisting of a large number of BCI datasets, was developed and gated by a number of machine learning methods, to enable instantaneous feedback for users. In addition, a multimodal neuroimaging study was conducted and shown to reduce illiteracy among subjects as well as enabling basic neuroscientific insight

An EEG Dataset of Neural Signatures in a Competitive Two-Player Game Encouraging Deceptive Behavior

Abstract Studying deception is vital for understanding decision-making and social dynamics. Recent EEG research has deepened insights into the brain mechanisms behind deception. Standard methods in this field often rely on memory, are vulnerable to countermeasures, yield false positives, and lack real-world relevance. Here, we present a comprehensive dataset from an EEG-monitored competitive, two-player card game designed to elicit authentic deception behavior. Our extensive dataset contains EEG data from 12 pairs (N = 24 participants with role switching), controlled for age, gender, and risk-taking, with detailed labels and annotations. The dataset combines standard event-related potential and microstate analyses with state-of-the-art decoding approaches of four scenarios: spontaneous/instructed truth-telling and lying. This demonstrates game-based methods’ efficacy in studying deception and sets a benchmark for future research. Overall, our dataset represents a unique resource with applications in cognitive neuroscience and related fields for studying deception, competitive behavior, decision-making, inter-brain synchrony, and benchmarking of decoding frameworks in a difficult, high-level cognitive task

Transmol: Repurposing Language Model for Molecular Generation

Recent advances in convolutional neural networks have inspired the application of deep learning to other disciplines. Even though image processing and natural language processing have turned out to be the most successful, there are many other areas that have benefited, like computational chemistry in general and drug design in particular. From 2018 the scientific community has seen a surge of methodologies related to the generation of diverse molecular libraries using machine learning. However, no algorithm used an attention mechanisms for de novo molecular generation. Here we employ a variant of transformers, a recent NLP architecture, for this purpose. We have achieved a statistically significant increase in some of the core metrics of the MOSES benchmark. Furthermore, a novel way of generating libraries fusing two molecules as seeds has been described

EEG-BASED PREDICTION OF SUCCESSFUL MEMORY FORMATION DURING VOCABULARY LEARNING

Previous Electroencephalography (EEG) and neuroimaging studies have found differences between brain signals for subsequently remembered and forgotten items during learning of items - it has even been shown that single trial prediction of memorization success is possible with a few target items. There has been little attempt, however, in validating the findings in an application-oriented context involving longer test spans with realistic learning materials encompassing more items. Hence, the present study investigates subsequent memory prediction within the application context of foreign-vocabulary learning. We employed an off-line, EEG-based paradigm in which Korean participants without prior German language experience learned 900 German words in paired-associate form. Our results using convolutional neural networks optimized for EEG-signal analysis show that above-chance classification is possible in this context allowing us to predict during learning which of the words would be successfully remembered later