A Local Neural Classifier for the Recognition of EEG Patterns Associated to Mental Tasks

This paper proposes a novel and simple local neural classifier for the recognition of mental tasks from on-line spontaneous EEG signals. The proposed neural classifier recognizes three mental tasks from on-line spontaneousEEGsignals. Correct recognition is around 70%. This modest rate is largely compensated by two properties, namely low percentage of wrong decisions (below 5%) and rapid responses (every 1/2 s). Interestingly, the neural classifier achieves this performance with a few units, normally just one per mental task. Also, since the subject and his/her personal interface learn simultaneously from each other, subjects master it rapidly (in a few days of moderate training). Finally, analysis of learned EEG patterns confirms that for a subject to operate satisfactorily a brain interface, the latter must fit the individual features of the former

Evolution of the Mental States Operating a Brain-Computer Interface

This study analyses the location of patterns of brain activity in the signal space while a human subject is trained to operate a brain-computer interface. This evaluation plays an important role in the understanding of the underlying system, and it gives valuable information about the translation algorithms. The relative position and morphology of the patterns in a training session, and from one session to another, enable us to evaluate the performance of both the interface and the user. Thanks to these aforementioned variables we are also able to appreciate stable trajectories of the mental states during the sessions, which shows both the adaptability of the user to the interface, and vice versa

Combining EEG and Eye Tracking: Using Fixation-Locked Potentials in Visual Search

Visual search is a complex task that involves many neural pathways to identify relevant areas of interest within a scene. Humans remain a critical component in visual search tasks, as they can effectively perceive anomalies within complex scenes. However, this task can be challenging, particularly under time pressure. In order to improve visual search training and performance, an objective, process-based measure is needed. Eye tracking technology can be used to drive real-time parsing of EEG recordings, providing an indication of the analysis process. In the current study, eye fixations were used to generate ERPs during a visual search task. Clear differences were observed following performance, suggesting that neurophysiological signatures could be developed to prevent errors in visual search tasks

Adaptive Brain Interfaces for Communication and Control

This paper describes our work on a portable non-invasive brain-computer interface (BCI), called Adaptive Brain Interfaces (ABI), that analysis online the users spontaneous electroencephalogram (EEG) signals from which a neural classifier recognizes 3 different mental states. The outputs of the classifier are used as mental commands to operate communication and control devices. Although still at a research stage, BCIs offer the possibility to augment human capabilities in a natural way and are particularly relevant as an aid for paralyzed humans

Asynchronous BCI and Local Neural Classifiers: An Overview of the Adaptive Brain Interface Project

In this paper we give an overview of our work on an asynchronous BCI (where the subject makes self-paced decisions on when to switch from a mental task to the next) that responds every 1/2 second. A local neural classifier tries to recognize three different mental tasks, but may also respond unknown for uncertain samples as the classifier has incorporated statistical rejection criteria. We report our experience with different subjects (around 15 up to now). We also describe briefly two brain-actuated applications we have developed, namely a virtual keyboard and a mobile robot (similar to a motorized wheelchair)

Non-Invasive Brain-Actuated Control of a Mobile Robot

Recent experiments have shown the near possibility to use the brain electrical activity to directly control the movement of robotics or prosthetic devices. In this paper we report results with a portable non-invasive brain-computer interface that makes possible the continuous control of a mobile robot in a house-like environment. The interface uses 8 surface electrodes to measure electroencephalogram (EEG) signals from which a statistical classifier recognizes 3 different mental states. Until now, brain-actuated control of robots has relied on invasive approaches-requiring surgical implantation of electrodes-since EEG-based systems have been considered too slow for controlling rapid and complex sequences of movements. Here we show that, after a few days of training, two human subjects successfully moved a robot between several rooms by mental control only. Furthermore, mental control was only marginally worse than manual control on the same task

Introduction: Evolution of Brain-Computer Interfaces

International audienceBrain-Computer Interfaces (BCIs) are systems that translate a measure of a user‘s brain activity into messages or commands for an interactive application. A typical example of a BCI is a system that enables a user to move a ball on a computer screen towards the left or towards the right, by imagining left or right hand movement respectively. The very term BCI was coined in the 70’s, and since then, interest and research efforts in BCIs grew tremendously, with possibly hundreds of laboratories around the world studying this topic. This has resulted in a very large number of paradigms, methods, concepts and applications of such technology. This handbook thus aims at providing an overview and tutorials of the multiple and rich facets of BCIs.As an introduction to this vast endeavor, we would like to present a short and brief history of BCIs, in order to explain where they come from. Since we are no historians of science, such historical introduction is likely to be incomplete and biased, according to our background, views and (conscious or not) preferences. Nonetheless, we hope this will enable the readers to get a quick overview of the development in BCIs these last 30 or 40 years, and will motivate them to learn more about BCI concepts, which this handbook should make easier

Non-Invasive Estimation of Local Field Potentials for Neuroprosthesis Control

Recent experiments have shown the possibility to use the brain electrical activity to directly control the movement of robots or prosthetic devices in real time. Such neuroprostheses can be invasive or non-invasive, depending on how the brain signals are recorded. In principle, invasive approaches will provide a more natural and flexible control of neuroprostheses, but their use in humans is debatable given the inherent medical risks. Non-invasive approaches mainly use scalp electroencephalogram (EEG) signals and their main disadvantage is that these signals represent the noisy spatiotemporal overlapping of activity arising from very diverse brain regions; i.e., a single scalp electrode picks up and mixes the temporal activity of myriads of neurons at very different brain areas. In order to combine the benefits of both approaches, we propose to rely on the non-invasive estimation of local field potentials (LFP) in the whole human brain from the scalp measured EEG data using a recently developed inverse solution (ELECTRA) to the EEG inverse problem. The goal of a linear inverse procedure is to de-convolve or un-mix the scalp signals attributing to each brain area its own temporal activity. To illustrate the advantage of this approach we compare, using identical set of spectral features, classification of rapid voluntary finger self-tapping with left and right hands based on scalp EEG and non-invasively estimated LFP on two subjects using different number of electrodes