The human-computer connection : an overview of brain-computer interfaces

This article introduces the field of brain-computer interfaces (BCI), which allows the control of devices without the generation of any active motor output but directly from the decoding of the user?s brain signals. Here we review the current state of the art in the BCI field, discussing the main components of such an interface and illustrating ongoing research questions and prototypes for controlling a large variety of devices, from virtual keyboards for communication to robotics systems to replace lost motor functions and even clinical interventions for motor rehabilitation after a stroke. The article concludes with some insights into the future of BCI

Recent and upcoming BCI progress: overview, analysis, and recommendations

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 analyze brain activity in field settings? Which devices and applications are most useful for different people? How can we make BCIs more natural and sensitive, and how can BCI technologies improve usability? What are some general trends and issues, such as combining different BCIs or assessing and comparing performance? This book chapter provides an overview of the different sections of this book, providing a summary of how authors address these and other questions. We also present some predictions and recommendations that ensue from our experience from discussing these and other issues with our authors and other researchers and developers within the BCI community. We conclude that, although some directions are hard to predict, the field is definitely growing and changing rapidly, and will continue doing so in the next several years

Unsupervised adaptation for acceleration-based activity recognition: robustness to sensor displacement and rotation

A common assumption in activity recognition is that the system remains unchanged between its design and its posterior operation. However, many factors affect the data distribution between two different experimental sessions. One of these factors is the potential change in the sensor location (e.g. due to replacement or slippage) affecting the classification performance. Assuming that changes in the sensor placement mainly result in shifts in the feature distributions, we propose an unsupervised adaptive classifier that calibrates itself using an online version of expectation-maximisation. Tests using three activity recognition scenarios show that the proposed adaptive algorithm is robust against shift in the feature space due to sensor displacement and rotation. Moreover, since the method estimates the change in the feature distribution, it can also be used to roughly evaluate the reliability of the system during online operatio

Vibration-induced dynamical weakening of pyroclastic flows: Insights from rotating drum experiments

Pyroclastic flows are characterized by their high mobility, which is often attributed to gas fluidization of the usually fine and/or low-density particles. However, the physical mechanism that might drive sustained fluidization of pyroclastic flows over extraordinarily long runout distances is elusive. In this letter it is proposed that a powerful mechanism to weaken the frictional resistance of pyroclastic flows would arise from the prolonged and intense mechanical vibrations that commonly accompany these dense gravitational fluid-particle flows. The behavior of fine powders in a slowly rotating drum subjected to vibrations suggests that fluid-particle relative oscillations in granular beds can effectively promote the pore gas pressure at reduced shear rates. Dynamical weakening, as caused by the enhancement of pore fluid pressure, may be an important mechanism in any geophysical process that involves vibrations of granular beds in a viscous fluid. This is particularly relevant for granular flows involving large amounts of fine and/or light particles such as pyroclastic density currents.Ministerio de Economia y Competitividad CTQ2014-52763-C2-2-RJunta de Andalucía FQM-573

On the Need for On-Line Learning in Brain-Computer Interfaces

In this paper we motivate the need for on-line learning in BCI and illustrate its benefits with the simplest method, namely fixed learning rates. However, the use of this method is supported by the risk of hampering the user to acquire suitable control of the BCI if the embedded classifier changes too rapidly. We report the results with 3 beginner subjects in a series of consecutive recording, where the classifiers are iteratively trained with the data of a given session and tested on the next session. At the end of these sessions 2 of the subjects reach a suitable performance that is close to allow them to start operating a brain-actuated device

Brain-Machine Interfaces: The Perception-Action Closed Loop

A brain-machine interface (BMI) is about transforming neural activity into action and sensation into perception (Figure 1). In a BMI system, neural signals recorded from the brain are fed into a decoding algorithm that translates these signals into motor outputs to control a variety of practical devices for motor-disabled people [1]-[5]. Feedback from the prosthetic device, conveyed to the user either via normal sensory pathways or directly through brain stimulation, establishes a closed control loop

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