Robert Leeb

• École Polytechnique Fédérale de Lausanne at Swiss Federal Institute of Technology in Lausanne

This work studies the class of algorithms for learning with side-information that emerges by extending generative models with embedded context-related variables. Using finite mixture models (FMMs) as the prototypical Bayesian network, we show that maximum-likelihood estimation (MLE) of parameters through expectation-maximization (EM) improves over...

Brain-computer interfaces (BCIs) and virtual reality (VR) are two technologic advances that are changing our way of interacting with the world. BCIs can be used to influence and can serve as a control mechanism in navigation tasks, communication, or other assistive functions. VR can create ad hoc interactive scenarios that involve all our senses, s...

Closed-loop or adaptive deep brain stimulation (DBS) for Parkinson's Disease (PD) has shown comparable clinical improvements to continuous stimulation, yet with less stimulation times and side effects. In this form of control, stimulation is driven by pathological beta oscillations recorded from the subthalamic nucleus, which have been shown to cor...

Excessive beta oscillatory activity in the subthalamic nucleus (STN) is linked to Parkinson’s Disease (PD) motor symptoms. However, previous works have been inconsistent regarding the functional role of beta activity in untreated Parkinsonian states, questioning such role. We hypothesized that this inconsistency is due to the influence of electroph...

Hand grasping is a sophisticated motor task that has received much attention by the neuroscientific community, which demonstrated how grasping activates a network involving parietal, pre-motor and motor cortices using fMRI, ECoG, LFPs and spiking activity. Yet, there is a need for a more precise spatio-temporal analysis as it is still unclear how t...

Brain-computer interfaces (BCI) are used in stroke rehabilitation to translate brain signals into intended movements of the paralyzed limb. However, the efficacy and mechanisms of BCI-based therapies remain unclear. Here we show that BCI coupled to functional electrical stimulation (FES) elicits significant, clinically relevant, and lasting motor r...

Background: Excessive beta oscillatory activity in the subthalamic nucleus (STN) is linked to Parkinson's disease and associated motor symptoms. However, the relationship between beta activity and motor symptoms has been inconsistent, which may influence the efficacy of closed-loop deep brain stimulation. Hypothesis: We hypothesized that this varia...

In this paper, we present and analyze an event distribution system for brain-computer interfaces. Events are commonly used to mark and describe incidents during an experiment and are therefore critical for later data analysis or immediate real-time processing. The presented approach, called Tools for brain-computer interaction interface D (TiD), de...

During the last years, several studies have suggested that Brain-Computer Interface (BCI) can play a critical role in the field of motor rehabilitation. In this case report, we aim to investigate the feasibility of a covert visuospatial attention (CVSA) driven BCI in three patients with left spatial neglect (SN). We hypothesize that such a BCI is a...

Introduction: In stroke rehabilitation, one of the key components for motor improvement is brain plasticity and, in particular, the reestablishment of cortical and subcortical networks [1] that can be studied with connectivity analysis. Despite recent advances in brain-computer interface (BCI)-driven stroke therapy [2], it is still unclear what are...

Introduction: Currently, most of invasive brain-machine interfaces (BMI) rely on signals recorded using electrodes implanted intra-cortically or subdural electrocorticography (ECoG) arrays. Barring a few studies [1,2], epidural electrodes–often used for chronic stimulation to alleviate neuropathic pain—are seldom used for this purpose. Here we repo...

Introduction: Performance variation is one of the main challenges that BCIs are confronted with, when being used over extended periods of time. Several methods have been proposed to find correlates of performance variation for sensorimotor rhythms EEG-based BCIs [1]. However, they typically focus on assessing performance variations within the same...

Objective. This work presents a first motor imagery-based, adaptive brain–computer interface (BCI) speller, which is able to exploit application-derived context for improved, simultaneous classifier adaptation and spelling. Online spelling experiments with ten able-bodied users evaluate the ability of our scheme, first, to alleviate non-stationarit...

One of the challenges of using brain-computer interfaces (BCIs) over extended periods of time is the variation of the users' performance from one experimental day to another. The goal of the current study is to propose a performance estimator for an electroencephalography-based motor imagery BCI by assessing the reliability of a command (i.e., pred...

Brain-computer interfaces (BCI) have been shown to be a promising tool in rehabilitation and assistive scenarios. Within these contexts, brain signals can be decoded and used as commands for a robotic device, allowing to translate user's intentions into motor actions in order to support the user's impaired neuro-muscular system. Recently, it has be...

This work introduces algorithms able to exploit contextual information in order to improve maximum-likelihood (ML) parameter estimation in finite mixture models (FMM), demonstrating their benefits and properties in several scenarios. The proposed algorithms are derived in a probabilistic framework with regard to situations where the regular FMM gra...

This study investigated the effect of multimodal (visual and auditory) continuous feedback with information about the uncertainty of the input signal on motor imagery based BCI performance. A liquid floating through a visualization of a funnel (funnel feedback) provided enriched visual or enriched multimodal feedback. In a between subject design 30...

This paper presents an important step forward towards increasing the independence of people with severe motor disabilities, by using brain–computer interfaces to harness the power of the Internet of Things. We analyze the stability of brain signals as end-users with motor disabilities progress from performing simple standard on-screen training task...

In their early days, brain–computer interfaces (BCIs) were only considered as control channel for end users with severe motor impairments such as people in the locked-in state. But, thanks to the multidisciplinary progress achieved over the last decade, the range of BCI applications has been substantially enlarged. Indeed, today BCI technology cann...

This chapter provides an overview of the functionality and the underlying principles of the brain-computer interfaces (BCI) developed by the Chair in Non-Invasive Brain-Machine Interface (CNBI) of the Swiss Federal Institute of Technology (EPFL), as well as exemplary applications where those have been successfully evaluated. Our laboratory mainly d...

Operating a brain-actuated vehicle in real-world environments requires much of our visual attention. However, a typical brain-computer interface (BCI) sends the feedback information about the current status of the user's brain also via the visual channel. As a result, users have to split their visual attention into two: One for the surroundings and...

How movements are generated and controlled by the central nervous system (CNS) is still not well understood. In this work, we tested the hypothesis of a modular organization of the brain activity during the execution of voluntary movements. In particular, we extracted meta-stable topographies as a measure for global brain state, so-called microstat...

Successful operation of motor imagery (MI)-based brain-computer interfaces (BCI) requires mutual adaptation between the human subject and the BCI. Traditional training methods, as well as more recent ones based on co-adaptation, have mainly focused on the machine-learning aspects of BCI training. This work presents a novel co-adaptive training prot...

Assistive technology (AT) supports individuals with motor, sensory, or cognitive disabilities in performing functions that might otherwise be difficult or impossible for them. In particular, individuals with severe motor impairments have a high need for assistive devices supporting access to information technologies, improving mobility, and restori...

Objective. While brain–computer interfaces (BCIs) for communication have reached considerable technical maturity, there is still a great need for state-of-the-art evaluation by the end-users outside laboratory environments. To achieve this primary objective, it is necessary to augment a BCI with a series of components that allow end-users to type t...

In this paper, the results of a pilot interview study with 19 subjects participating in an EEG-based non-invasive brain–computer interface (BCI) research study on stroke rehabilitation and assistive technology and of a survey among 17 BCI professionals are presented and discussed in the light of ethical, legal, and social issues in research with hu...

The Fifth International Brain-Computer Interface (BCI) Meeting met on 3–7 June 2013 at the Asilomar Conference Grounds, Pacific Grove, California, USA. The conference included 19 workshops covering topics in brain-computer interface and brain-machine interface research. Topics included translation of BCIs into clinical use, standardization and cert...

Brain-Computer Interfaces (BCIs) have been successfully used to control assistive mobility devices (like a telepresence robot or an electric wheelchair) using only motor imagery. Importantly, disabled end-users are able to achieve similar performances as healthy participants.

Brain-Computer Interfaces (BCIs) can be extended by other input signals to form a so-called hybrid BCI (hBCI). Such an hBCI allows the processing of several input signals with at least one brain signal for control purposes, i.e. communication and environmental control. This work shows the principle, technology and application of hBCIs and discusses...

To explore the exciting new domain of brain informatics, we invited several well-known experts to discuss the state of the art, the challenges, the opportunities, and the trends. In "Creating Human-Level AI by Educating a Child Machine," Raj Reddy proposes an architecture for a "child machine" that can learn and is teachable. In "Cyborg Intelligenc...

Objective. In this work we present—for the first time—the online operation of an electroencephalogram (EEG) brain–computer interface (BCI) system based on covert visuospatial attention (CVSA), without relying on any evoked responses. Electrophysiological correlates of pure top-down CVSA have only recently been proposed as a control signal for BCI....

Controlling a brain-actuated device requires the participant to look at and to split his attention between the interaction of the device with its environment and the status information of the Brain-Computer Interface (BCI). Such parallel visual tasks are partly contradictory, with the goal of achieving a good and natural device control. Is there a...

Motor-disabled end users have successfully driven a telepresence robot in a complex environment using a Brain-Computer Interface (BCI). However, to facilitate the interaction aspect that underpins the notion of telepresence, users must be able to voluntarily and reliably stop the robot at any moment, not just drive from point to point. In this work...

In this paper, we describe a multimodal brain–computer interface (BCI) experiment, situated in a highly immersive CAVE. A subject sitting in the virtual environment controls the main character of a virtual reality game: a penguin that slides down a snowy mountain slope. While the subject can trigger a jump action via the BCI, additional steering wi...

OBJECTIVE: We studied the activation of cortical motor and parietal areas during the observation of object related grasping movements. By manipulating the type of an object (realistic versus abstract) and the type of grasping (correct versus incorrect), we addressed the question how observing such object related movements influences cortical rhythm...

When BCI based devices are operated, users are often desired to interact with environment. However, conventional visual BCI feedback disturbs continuous and smooth interactions. Therefore, a new tactile stimulation system suitable for delivering BCI feedback to user is developed. The system employs tactile illusion of movement to produce a continuo...

Brain–Computer Interface (BCI) research has developed in the last decade so that BCIs are ready to be used with users outside the research labs. Although a wide range of assistive devices (ADs) exist, the additional usage of a BCI could improve the overall performance or applicability of such a combined system and is called hybrid BCI (hBCI). In th...

Transcranial Direct Current Stimulation (tDCS) induces selective modulation of cortical excitability. This technique, as well as Brain–Computer Interfaces (BCIs), has been proposed as a supporting tool for neurorehabilitation. Here we show evidence that tDCS in SCI patients and control subjects modulates spectral features related to motor-imagery,...

In this paper we propose a method to modulate the level of assistance provided by a shared controller, not only given the environmental context, but also according to the context of the user's current behaviour. We show that the enhanced situational context can be adequately captured by using online performance metrics (such as those more usually f...

The prospect of controlling devices merely by the power of one's thoughts is compelling, especially for assistive technology applications. In the accompanying video, we show how we have strived to push brain-computer interface (BCI) technology out of the lab and into the real world, while simultaneously moving away from testing solely with healthy...

Brain-computer interfaces (BCIs) are devices that enable people to communicate via thought alone. Brain signals can be directly translated into messages or commands. Until recently, these devices were used primarily to help people who could not move. However, BCIs are now becoming practical tools for a wide variety of people, in many different situ...

Recently, several studies have started to explore covert visuospatial attention as a control signal for brain–computer interfaces (BCIs). Covert visuospatial attention represents the ability to change the focus of attention from one point in the space without overt eye movements. Nevertheless, the full potential and possible applications of this pa...

The BCI competition IV stands in the tradition of prior BCI competitions that aim to provide high quality neuroscientific data for open access to the scientific community. As experienced already in prior competitions not only scientists from the narrow field of BCI compete, but scholars with a broad variety of backgrounds and nationalities. They in...

Brain–Computer Interfaces (BCI) are communication systems which can convey messages through brain activity alone. Recently BCIs were gaining interest among the virtual reality (VR) community since they have appeared as promising interaction devices for virtual environments (VEs). Especially these implicit interaction techniques are of great interes...

Brain–Computer Interface (BCI) research is a currently very active and fast growing field, in particular in bringing the BCI out of the lab and moving from prototypes to real world applications such as brain-controlled writing applications, wheelchairs, and games. The research focus has been widened and BCIs are no longer only useful for patients,...

Brain–computer interfaces (BCIs) are finally moving out of the laboratory and beginning to gain acceptance in real-world situations. As BCIs gain attention with broader groups of users, including persons with different disabilities and healthy users, numerous practical questions gain importance. What are the most practical ways to detect and analyz...

The BCI competition IV stands in the tradition of prior BCI competitions that aim to provide high quality neuroscientific data for open access to the scientific community. As experienced already in prior competitions not only scientists from the narrow field of BCI compete, but scholars with a broad variety of backgrounds and nationalities. They in...

This paper summarizes two novel ways to extend brain-computer interface (BCI) systems. One way involves hybrid BCIs. A hybrid BCI is a system that combines a BCI with another device to help people send information. Different types of hybrid BCIs are discussed, along with challenges and issues. BCIs are also being extended through intelligent system...

The aim of this work is to present the development of a hybrid Brain-Computer Interface (hBCI) which combines existing input devices with a BCI. Thereby, the BCI should be available if the user wishes to extend the types of inputs available to an assistive technology system, but the user can also choose not to use the BCI at all; the BCI is active...

Motor imagery (MI) brain-computer interfaces (BCIs) translate a subject's motor intention to a command signal. Most MI BCIs use power features in the mu or beta rhythms, while several results have been reported using a measure of phase synchrony, the phase-locking value (PLV). In this study, we investigated the performance of various phase-based fe...

In this paper we present the first results of users with disabilities in mentally controlling a telepresence robot, a rather complex task as the robot is continuously moving and the user must control it for a long period of time (over 6 minutes) to go along the whole path. These two users drove the telepresence robot from their clinic more than 100...

Non-Invasive Brain-Computer Interfaces (BCI) convey a great potential in the field of stroke rehabilitation, where the continuous monitoring of mental tasks execution could support the positive effects of standard therapies. In this paper we combine time-frequency analysis of EEG with the topographic analysis to identify and track task-related patt...

Hybrid brain-computer interfaces (BCIs) are representing a recent approach to develop practical BCIs. In such a system disabled users are able to use all their remaining functionalities as control possibilities in parallel with the BCI. Sometimes these people have residual activity of their muscles. Therefore, in the presented hybrid BCI framework...

Brain-Computer Interfaces convey a great potential in the field of stroke rehabilitation, where the continuous monitoring of the execution of mental tasks could support the positive effects of the therapy by reinforcing specific mental patterns. We propose the use of EEG Microstates as the building blocks of a novel BCI for Operators, and we show t...

The non-invasive Brain-Computer Interface (BCI) developed in our lab targets asynchronous operation of devices by monitoring electroencephalographic (EEG) activity and identifying oscillatory patterns that the user can voluntary modulate through the execution of motor imagery (MI) tasks. Successful self-paced interaction under this framework requir...

One important source of performance degradation in BCIs is bias towards one of the men-tal classes. Recent literature has focused on the general problem of classification accuracy drop, identifying non-stationarity as the generating factor, thus leading to several classi-fier adaptation approaches suggested as of today. In this work, we explicitly...

Hybrid Brain-Computer Interfaces (BCI) are representing a recent approach to develop practical BCIs. In such a system disabled users are able to use all their remaining functionalities as control possibilities in parallel with the BCI. Sometimes these people have residual activity of their muscles. Therefore, in the presented hybrid BCI framework w...

Non–Invasive Brain–Computer Interfaces (BCI) convey a great potential in the field of stroke rehabilitation, where the continuous monitoring of mental tasks execution could support the positive effects of standard therapies. In this paper we combine time-frequency analysis of EEG with the topographic analysis to identify and track task–related patt...

This paper discusses and evaluates the role of shared control approach in a BCI-based telepresence framework. Driving a mobile device by using human brain signals might improve the quality of life of people suffering from severely physical disabilities. By means of a bidirectional audio/video connection to a robot, the BCI user is able to interact...

In this paper we show how healthy subjects can operate a non-invasive asynchronous BCI for controlling a FES neuroprosthesis and manipulate objects to carry out daily tasks in ecological conditions. Both, experienced and novel subjects proved to be able to deliver mental commands with high accuracy and speed. Our neuroprosthetic approach relies on...

In recent years, new research has brought the field of electroencephalogram (EEG)-based brain–computer interfacing (BCI) out of its infancy and into a phase of relative maturity through many demonstrated prototypes such as brain-controlled wheelchairs, keyboards, and computer games. With this proof-of-concept phase in the past, the time is now ripe...

Practical Brain-Computer Interfaces (BCIs) for disabled people should allow them to use all their remaining functionalities as control possibilities. Sometimes these people have residual activity of their muscles, most likely in the morning when they are not exhausted. In this work we fuse electromyographic (EMG) with electroencephalographic (EEG)...

To patients who have lost the functionality of their hands as a result of a severe spinal cord injury or brain stroke, the development of new techniques for grasping is indispensable for reintegration and independency in daily life. Functional Electrical Stimulation (FES) of residual muscles can reproduce the most dominant grasping tasks and can be...

Brain-Computer Interface (BCI) research has made great progress recently [1-3]. However, this progress has some negative side effects: growing fragmentation among different researchers, confusion about the best research directions, and ongoing disagreement over terms and definitions. Future BNCI is a Coordination and Support Action funded by the Eu...

This study investigates the influence of eye movement direction on patterns of brain activation. The processing of visual input was investigated by quantifying event-related desynchronization (ERD) in the electroencephalogram (EEG). Cue-based vertical and horizontal eye movements were measured with an eye tracker. Differences between vertical and h...

Near-infrared spectroscopy (NIRS) is a non-invasive optical technique for the assessment of functional activity in the human brain. With NIRS characteristic hemodynamic responses (concentration changes in oxy- (HbO2) and deoxyhemoglobin (Hb)) during cognitive, visual, or motor tasks can be measured. The NIRS signal consists of slow hemodynamic re...

BCIs offer a new means of playing videogames or interacting with 3D virtual environments. Several impressive prototypes already exist that let users navigate in virtual scenes or manipulate virtual objects solely by means of their cerebral activity, recorded on the scalp via electroencephalography electrodes. Meanwhile, virtual reality technologies...

Near-infrared spectroscopy (NIRS) is a non-invasive optical technique for the assessment of functional activity in the human brain. With NIRS characteristic hemodynamic responses (changes in oxy- and deoxyhemoglobin concentration) during cognitive, visual, or motor tasks can be measured. In order to determine whether the recorded signal is due to a...