Ensembles of adaptive spatial filters increase BCI performance: an online evaluation

Objective: In electroencephalographic (EEG) data, signals from distinct sources within the brain are widely spread by volume conduction and superimposed such that sensors receive mixtures of a multitude of signals. This reduction of spatial information strongly hampers single-trial analysis of EEG data as, for example, required for brain–computer interfacing (BCI) when using features from spontaneous brain rhythms. Spatial filtering techniques are therefore greatly needed to extract meaningful information from EEG. Our goal is to show, in online operation, that common spatial pattern patches (CSPP) are valuable to counteract this problem. Approach: Even though the effect of spatial mixing can be encountered by spatial filters, there is a trade-off between performance and the requirement of calibration data. Laplacian derivations do not require calibration data at all, but their performance for single-trial classification is limited. Conversely, data-driven spatial filters, such as common spatial patterns (CSP), can lead to highly distinctive features; however they require a considerable amount of training data. Recently, we showed in an offline analysis that CSPP can establish a valuable compromise. In this paper, we confirm these results in an online BCI study. In order to demonstrate the paramount feature that CSPP requires little training data, we used them in an adaptive setting with 20 participants and focused on users who did not have success with previous BCI approaches. Main results: The results of the study show that CSPP adapts faster and thereby allows users to achieve better feedback within a shorter time than previous approaches performed with Laplacian derivations and CSP filters. The success of the experiment highlights that CSPP has the potential to further reduce BCI inefficiency. Significance: CSPP are a valuable compromise between CSP and Laplacian filters. They allow users to attain better feedback within a shorter time and thus reduce BCI inefficiency to one-fourth in comparison to previous non-adaptive paradigms.The work of Claudia Sannelli, Carmen Vidaurre and Klaus-Robert Müller was funded by the German Ministry for Education and Research (BMBF) under Grant 01IS14013A-E and Grant 01GQ1115, as well as by the Deutsche Forschungsgesellschaft (DFG) under Grant MU 987/19-1, MU987/14-1 and DFG MU 987/3-2. Additionally, the work of Klaus-Robert Müller was funded by the Brain Korea 21 Plus Program. The work of Benjamin Blankertz was funded by the BMBF contract 01GQ0850

A large scale screening study with a SMR-based BCI: Categorization of BCI users and differences in their SMR activity

Brain-Computer Interfaces (BCIs) are inefficient for a non-negligible part of the population, estimated around 25%. To understand this phenomenon in Sensorimotor Rhythm (SMR) based BCIs, data from a large-scale screening study conducted on 80 novice participants with the Berlin BCI system and its standard machine-learning approach were investigated. Each participant performed one BCI session with resting state Encephalography, Motor Observation, Motor Execution and Motor Imagery recordings and 128 electrodes. A significant portion of the participants (40%) could not achieve BCI control (feedback performance > 70%). Based on the performance of the calibration and feedback runs, BCI users were stratified in three groups. Analyses directed to detect and elucidate the differences in the SMR activity of these groups were performed. Statistics on reactive frequencies, task prevalence and classification results are reported. Based on their SMR activity, also a systematic list of potential reasons leading to performance drops and thus hints for possible improvements of BCI experimental design are given. The categorization of BCI users has several advantages, allowing researchers 1) to select subjects for further analyses as well as for testing new BCI paradigms or algorithms, 2) to adopt a better subject-dependent training strategy and 3) easier comparisons between different studies.This work was supported by German Ministry for Education and Research (BMBF) under Grants 01IS14013A-E, 01GQ1115 and 01GQ0850; Deutsche Forschungsgesellschaft (DFG) under Grant MU 987/19-1, MU987/14-1 and DFG MU 987/3-2; Brain Korea 21 Plus Program and by the Institute for Information & Communications Technology Promotion (IITP) grant funded by the Korea government (No. 2017-0-00451); and Spanish Ministry of Economy RYC-2014-15671

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

Data S1: Data

We present the evaluation of two well-known, low-cost consumer-grade EEG devices: the Emotiv EPOC and the Neurosky MindWave. Problems with using the consumer-grade EEG devices (BCI illiteracy, poor technical characteristics, and adverse EEG artefacts) are discussed. The experimental evaluation of the devices, performed with 10 subjects asked to perform concentration/relaxation and blinking recognition tasks, is given. The results of statistical analysis show that both devices exhibit high variability and non-normality of attention and meditation data, which makes each of them difficult to use as an input to control tasks. BCI illiteracy may be a significant problem, as well as setting up of the proper environment of the experiment. The results of blinking recognition show that using the Neurosky device means recognition accuracy is less than 50%, while the Emotiv device has achieved a recognition accuracy of more than 75%; for tasks that require concentration and relaxation of subjects, the Emotiv EPOC device has performed better (as measured by the recognition accuracy) by ∼9%. Therefore, the Emotiv EPOC device may be more suitable for control tasks using the attention/meditation level or eye blinking than the Neurosky MindWave device

Dispositivi medici e cybersecurity: regolamenti, linee guida e loro armonizzazione

L'elaborato presentato ha l'obiettivo di presentare il quadro normativo europeo sul tema della Cybersecurity applicato al settore dei dispositivi medici. Verranno spiegati concetti chiavi dell'argomento e verranno approfondite la linea guida europea MDCG 2019-16 e la norma tecnica IEC 81001-5-1 dedicata ai requisiti di sicurezza informatica nei software sanitari che verrà armonizzata al regolamento europeo sui dispositivi medici. Infine, verranno fornite delle considerazioni generali sulla normativa europea ed un confronto con gli Stati Uniti, che copre il ruolo fondamentale nel mercato internazionale dei dispositivi medici

Optimierung von räumlichen Filtern zur Verringerung der BCI Ineffizienz

Ein Brain-Computer Interface (BCI) stellt einen Kommunikationskanal her zwischen einer Person und einem elektronischen Gerät, wobei der Benutzer das Gerät alleine durch seine Gehirnsignale steuern kann. Während BCI-Techniken erfolgreich für Anwendungen im Spiele- und Unterhaltungsbereich genutzt werden können, bleibt als wichtigstes Ziel, mittels BCI den Alltag von Menschen mit schweren neurologischen Beeinträchtigungen zu erleichtern. Das erfordert eine hohe Verlässlichkeit des BCI Systems, um eine größere Akzeptanz von medizinischer Seite zu erreichen. Tatsächlich gibt es einen nicht vernachlässigbaren Teil der Bevölkerung, für den BCI Systeme nicht richtig funktionieren; Schätzungen gehen von etwa 25% aus. Dieses Phänomen wird BCI Ineffizienz genannt, was sich auf das Unvermögen der BCI Systeme bezieht, erfolgreich mit Gehirnsignalen in ihrer ganzen Bandbreite umzugehen. Diese Arbeit ist der erste Versuch einen Überblick zu bauen, über die Ineffizienz von BCI Systemen, die sich durch Elektroenzephalographie (EEG) auf dem sensomotorischen Rhythmus (SMR) basieren, um sie besser zu verstehen und sich ihr entgegenzusetzen. Das Problem wird von verschiedenen Blickpunkten analysiert, die jeweils einen neuen Einblick erlauben und in ihrer Gänze zu einem neuen Algorithmus führen, der auf einem speziellen räumlichen Filter beruht und dessen Effizienz in einer online-Studie abschließendend demonstriert wird. Im ersten Teil dieser Dissertation wird eine groß angelegte Rasterungs-Studie vorgestellt, anhand derer BCI-Benutzer in drei Kategorieren eingeteilt wurden um die Unterschiede in SMR-Aktivität zwischen diesen Gruppen zu verstehen. Diese ersten Resultate erlauben eine zielorientierte Untersuchung der EEG-Daten mittels neuartiger Methoden des maschinellen Lernens und statistischer Methoden, die im zweiten Teil im Fokus stehen. Die Analysen gehen auf die hohe Dimensionalität, Komplexität und den Informationsgehalt der Daten ein, ebenso wie auf Fragen der praktischen Umsetzung, wie Anzahl und Position der benötigten EEG-Kanäle. Diese Probleme, bilden ein Dilemma, für das ein Kompromiss gefunden werden muss. Dieser muss abwägen zwischen der Quantität der Information und der Empfindlichkeit gegenüber Overfitting von Common Spatial Patterns (CSP), dem gängigsten Algorithmus für SMR-basierte BCI Systeme, basierend auf räumlichen Filtern. Im dritten Teil der Dissertation, wird ein Rückbezug hergestellt zu den vorherigen Analysen, auf deren Basis ein neuer Algorithmus basierend auf räumlichen Filtern entwickelt wird: Common Spatial Pattern Patches (CSPP), eine Erweiterung von CSP. CSPP hat die wertvolle Eigenschaft, robuster gegenüber Overfitting zu sein, was wir in Offline-Vergleichen mit CSP und seiner aktuellen regularisierten Version rCSP zeigen konnten. Um dies weiter unter Beweis zu stellen, wurde eine Online-Studie mit 20 Teilnehmern durchgeführt, welche zuvor Schwierigkeiten hatten, ein BCI zu steuern. Diese Studie beweist, dass CSPP auf Grund seiner Eigenschaften der Lokalität und niedrigen Dimensionalität auch geeignet ist, sich sehr erfolgreich mit neu-entwickelten online Adaptions-Techniken kombinieren lässt, sowohl bei überwachtem als auch bei unüberwachten Ansätzen. Tatsächlich konnten alle Benutzer bis auf drei ihre BCI-Performanz verbessern, was zu einer starken Verringerung der BCI Ineffizienz führte.A Brain-Computer Interface (BCI) establishes a communication between a person and an electronic device, allowing the user to control the device by means of just his/her brain signals. While BCI techniques can be successfully used for game and entertainment applications, the most important BCI aim is to improve the everyday life of people with severe neurodisabilities. Therefore, a high reliability of the BCI system is required to obtain a larger acceptance by the medical community. Indeed, the BCI systems do not work properly for a non negligible part of the population, which is estimated to be around 25%. This phenomenon is named BCI inefficiency, referring to the inability of BCI systems to successfully deal with all typology of brain signals. The BCI inefficiency phenomenon affects all kinds of BCIs. Nevertheless, little effort has been invested so far to understand the causes of BCI inefficiency and to reduce it. This work is the first attempt to build an overview on BCI inefficiency for electroencephalogram (EEG) BCIs based on sensorimotor rhythm (SMR) to better understand and consequently face it. The problem is analyzed from several points of view, each of them offering a new insight and all of them converging in the design of a new spatial filter algorithm whose efficiency is demonstrated in a final online study. In the first part of this thesis, a large scale screening study is presented, with a categorization of BCI users in three groups and grand average EEG data investigations directed towards understanding the differences in the SMR activity among these groups. These preliminary results allow to conduct a goal-oriented investigation of the EEG data by means of novel machine learning and statistical methods, which takes up the second part of the thesis. In particular, several analyses are carried out, focusing on the data dimensionality, complexity and information content, as well as on the practical issue of the number and position of the EEG channels. These problems, together with the experiment's practicability and the offered spatial resolution, constitute the trade-off dilemma between the quantity of the information and the sensibility to overfitting of Common Spatial Patterns (CSP), the most common spatial filter algorithm for SMR-based BCIs. Additionally, the role of pre-stimulus SMR on the user's performance is investigated. In the third part of the thesis, the previous analyses are taken into account, giving rise to the development of a new spatial filter algorithm called Common Spatial Pattern Patches (CSPP), a compromise between Laplacian filters and CSP. Being more robust than CSP against overfitting, as demonstrated in offline comparison to CSP and its recent regularized version R-CSP, CSPP can be employed very early in the experiment and is very suitable for the co-adaptive calibration, a new experimental approach designed to alleviate BCI inefficiency. An online study with 20 volunteers who participated earlier in at most two experiments and had difficulties to reach BCI control, proved that CSPP, given its properties of being local and low dimensional, can be successfully employed to construct subject-independent classifiers and to be combined with supervised and unsupervised adaptation techniques, therefore optimizing the co-adaptive design. Indeed, all users except for three could improve their previous BCI performance resulting in a strong reduction of BCI inefficiency

Metabolites_310K_pH6.zip

Dataset for "Rapid Probing of Glucose Influx into Cancer Cell Metabolism: Using Adjuvant and a pH-Dependent Collection of Central Metabolites to Improve In-Cell D-DNP NMR". NMR spectroscopy spectra for the assignment of common metabolites from whole-cell catalysts. HSQC and HMBC chemical shifts at different pH and temperature. Raw NMR data generated on a Bruker 800MHz NMR spectrometer. Can be opened with TopSpin software. 1H and 13C 2D correlation experiments.    This item is part of the collection: NMR Metabolites spectra for "Rapid Probing of Glucose Influx into Cancer Cell Metabolism: Using Adjuvant and a pH-Dependent Collection of Central Metabolites to Improve In-Cell D-DNP NMR". https://doi.org/10.11583/DTU.c.6382923 </p

Stimolazione del gusto tramite impulsi elettrici

Questo elaborato finale si pone come obiettivo l'esaminazione di articoli scientifici nei quali viene spiegata la stimolazione elettrica del gusto tramite degli impulsi elettrici adeguatamente regolati. Infine si cerca di riprodurre la stimolazione elettrica del gusto

Metabolites_298K_pH6.zip

Dataset for "Rapid Probing of Glucose Influx into Cancer Cell Metabolism: Using Adjuvant and a pH-Dependent Collection of Central Metabolites to Improve In-Cell D-DNP NMR". NMR spectroscopy spectra for the assignment of common metabolites from whole-cell catalysts. HSQC and HMBC chemical shifts at different pH and temperature. Raw NMR data generated on a Bruker 800MHz NMR spectrometer. Can be opened with TopSpin software. 1H and 13C 2D correlation experiments.    This item is part of the collection: NMR Metabolites spectra for "Rapid Probing of Glucose Influx into Cancer Cell Metabolism: Using Adjuvant and a pH-Dependent Collection of Central Metabolites to Improve In-Cell D-DNP NMR". https://doi.org/10.11583/DTU.c.6382923 </p