Brain-computer interfaces

Epistemological and Phenomenological Issues in the use of Brain-Computer Interfacess

by Richard Heersmink

In C. Ess, & R. Hagengruber (Eds.), Proceedings of the International Association for Computing and Philosophy 2011 (pp. 98-102). Münster: MV-Wissenschaft.

Brain-computer interfaces (BCIs) are an emerging and converging technology that translates the brain activity of its... more Brain-computer interfaces (BCIs) are an emerging and converging technology that translates the brain activity of its user into command signals for external devices, ranging from motorized wheelchairs, robotic hands, environmental control systems, and computer applications. In this paper I functionally decompose BCI systems and categorize BCI applications with similar functional properties into three categories, those with (1) motor, (2) virtual, and (3) linguistic applications. I then analyse the relationship between these distinct BCI applications and their users from an epistemological and phenomenological perspective. Specifically, I analyse functional properties of BCIs in relation to the abilities (particularly motor behaviour and communication) of their human users, asking how they may or may not extend these abilities. This includes a phenomenological analysis of whether BCIs are experienced as transparent extensions. Contrary to some recent philosophical claims, I conclude that, although BCIs have the potential to become bodily as well as cognitive extensions for skilled users, at this stage they are not. And while the electrodes and signal processor may to a variable degree be transparent and incorporated, the BCI system as a whole is not. Contemporary BCIs are difficult to use. Most systems only work in highly controlled laboratory settings, require a high amount of training and concentration, have very limited control options, have low and variable information transfer rates, and effector motions are often slow, clumsy and sometimes unsuccessful. These drawbacks considerably limit their possibilities for transparency and incorporation into either the body schema or cognitive system which is essential for bodily and cognitive extension. Current BCIs can therefore only be seen as a weak or metaphorical extension of the human central nervous system. To increase their potential for cognitive extension, I give suggestions for improving the interface design of what I refer to as linguistic applications.

Evolving Signal Processing for Brain-Computer Interface

by Zhilin Zhang

Scott Makeig, Christian Kothe, Tim Mullen, Nima Bigdely-Shamlo, Zhilin Zhang, Kenneth Kreutz-Delgado, to appear in Proceedings of the IEEE, May, 2012

Because of the increasing portability and wearability of noninvasive electrophysiological systems that record and... more Because of the increasing portability and wearability of noninvasive electrophysiological systems that record and process electrical signals from the human brain, automated systems for assessing changes in user cognitive state, intent, and response to events are of increasing interest. Brain-computer interface (BCI) systems can make use of such knowledge to deliver relevant feedback to the user or to an observer, or within a human–machine system to increase safety and enhance overall performance. Building robust and useful BCI models from accumulated biological knowledge and available data is a major challenge, as are technical problems associated with incorporating multimodal physiological, behavioral, and contextual data that may in the future be increasingly ubiquitous. While performance of current BCI modeling methods is slowly increasing, current performance levels do not yet support widespread uses. Here we discuss the current neuroscientific questions and data processing challenges facing BCI designers and outline some promising current and future directions to address them.

A Five-State P300-based Foot Lifter Orthosis: Proof of Concept

by Matthieu Duvinage

IEEE BRC2012, Manaus

Current lower limb prostheses do not integrate re-
cent developments in robotics and in Brain-Computer... more Current lower limb prostheses do not integrate re-
cent developments in robotics and in Brain-Computer Interfaces
(BCIs). In fact, active lower limb prostheses seldom consider
the user’s intent, they often determine the correct movement
from those of healthy parts of the body or from the residual
limb. Recently, an emerging idea for non-invasive BCIs was
proposed to allow such low bitrate systems to control a lower limb
prosthesis thanks to a Central Pattern Generator (CPG) widely
used in robotics. This CPG allows to automatically generate a
periodic gait pattern. Furthermore, the CPG pattern frequency
and magnitude can be adapted according to the specific gait
behavior of the patient and his desired speed.
This paper proves the concept of combining a human gait model
based on a CPG and a classic but non-natural P300 BCI in order
to consider the user’s intent. The details of how the entire chain
can be practically implemented are given. Finally, preliminary
results on four healthy subjects for a four-speed P300-based lower
limb orthosis with a non-control state are presented. Globally,
results are satisfying and prove the feasibility of such systems.

Neural network classification of brain hemodynamic responses from four mental tasks

by Berdakh Abibullaev

Int. J. Optomechatronics. Vol.5, No.4, pp.340-359, Dec. 2011, Taylor & Francis

We investigate subjects’ brain hemodynamic activities during mental tasks using a nearinfrared spectroscopy. A wavelet... more We investigate subjects’ brain hemodynamic activities during mental tasks using a nearinfrared spectroscopy. A wavelet and neural network-based methodology is presented for recognition of brain hemodynamic responses. The recognition is performed by a single layer neural network classifier according to a backpropagation algorithm with two error minimizing techniques. The performance of the classifier varied depending on the neural network model, but the performance was usually at least 90%. The classifier usually converged faster and attained a somewhat greater level of performance when an input was presented with only relevant features. The overall classification rate was higher than 94%. The study demonstrates the accurate classifiablity of human brain hemodynamic useful in various brain studies.

Brain–Computer Interface: Past, Present & Future

by Ibrahim Arafat

Abstract—BCI allows users to communicate with others by
using only brain activity without using peripheral nerves... more Abstract—BCI allows users to communicate with others by
using only brain activity without using peripheral nerves and
muscles of human body. On BCI research the Electroencephalogram
(EEG)
is used for recording the
electrical activity along the scalp. EEG is used to measure the
voltage fluctuations resulting from ionic current flows within
the neurons of the brain. A German neuroscientist, Hans
Berger’s discovered the electrical activity of human brain by
using EEG in 1924. Hans Berger was the first one who
recorded an Alpha Wave from a human brain. In 1970
Defense Advanced Research Projects Agency of USA initiated
the program to explore brain communications using EEG. In
1999 the First International Meeting on BCI was arranged at
New York. The Fourth International BCI Conference was
held at Asilomar, CA, USA in 2010. In the present year the 5
International BCI Conference 2011 was held at Austria in
September. In this paper some indicators and predictions for
the future of BCI is mentioned later.
th

Non-invasive brain-computer interface system: towards its application as assistive technology

by Andrea Cherubini

New Ways to Interact, Naturally and Efficiently!

by Bo Höge

Co-authored with Rötting, M. and Dzaack, J. in Proceedings of the ITU-TUB Joint Conference 2010
Publisher: ITU, Istanbul, Turkey
Editors: Asan, Umut and Soyer, Ayberk and Serdar Asan, Şeyda
Pages: 36 - 46
ISBN: 978-605-62699-0-5

Technical systems are complex and dynamic environments incorporating humans and technical components. In these... more Technical systems are complex and dynamic environments incorporating humans and technical components. In these socio-technical systems humans are crucial parts and highly contribute to their resilience. Nevertheless, humans will produce failures, decide wrong or show erratic behavior. Thus, it is important to provide methods to increase human reliability and performance. A promising approach is to provide interaction methods that take into account human factors and that provide contextual support. We present three research projects incorporating new technologies for human-machine interaction. (1) Multimodal human-machine interaction: we present a software tool to connect several input and output devices to provide multimodal interaction systems. (2) Brain-computer interaction: we present our brain-computer interaction (BCI) approach that extracts information directly from the human brain. And (3) augmented reality: With the help of augmented reality techniques, it is possible to combine and display virtual and real information within the visual field of a user concurrently. We present our research on how to support users with augmented reality in collaborative problem solving tasks. All together these three concepts aim to support human users and offer new interaction techniques to
design future interaction in a more natural and efficient way.

Brain-Computer Music Interface for Composition and Performance

by Prof Eduardo R. Miranda

Steering generative rules with the eeg: An approach to brain-computer music interfacing

by Prof Eduardo R. Miranda

Brain-computer music interface for generative music

by Prof Eduardo R. Miranda

Proc. 6th Intl Conf. Disability, Virtual Reality & Assoc. Tech., Esbjerg, Denmark, 2006. ISBN 07 049 98 65 3

This paper introduces a brain-computer interface (BCI) system that uses electroencephalogram (EEG) information to... more This paper introduces a brain-computer interface (BCI) system that uses electroencephalogram (EEG) information to steer generative rules in order to compose and perform music. It starts by noting the various attempts at the design of BCI systems, including systems for music. Then it presents a short technical introduction to EEG sensing and analysis. Next, it introduces the generative music component of the system, which employs an Artificial Intelligence technique for the computer-replication of musical styles. The system constantly monitors the EEG of the subject and activates generative rules associated with the activity of different frequency bands of the spectrum of the EEG signal. The system also measures the complexity of the EEG signal in order to modulate the tempo (beat) and dynamics (loudness) of the performance.

The Role of Highly-Active Neurons in Developing Networks

by Vladislav Volman

Brain-computer interfaces for 1-D and 2-D cursor control: designs using volitional control of the EEG spectrum or steady-state visual evoked potentials

by Len Trejo

Multimodal neuroelectric interface development

by Len Trejo

Embodied Tools, Cognitive Tools and Brain-Computer Interfaces

by Richard Heersmink

Neuroethics. doi:10.1007/s12152-011-9136-2

In this paper I explore systematically the relationship between Brain-Computer Interfaces (BCIs) and their human users... more In this paper I explore systematically the relationship between Brain-Computer Interfaces (BCIs) and their human users from a phenomenological and cognitive perspective. First, I functionally decompose BCI systems and develop a typology in which I categorize BCI applications with similar functional properties into three categories, those with (1) motor, (2) virtual, and (3) linguistic applications. Second, developing and building on the notions of an embodied tool and cognitive tool, I analyze whether these distinct BCI applications can be seen as bodily or cognitive extensions. Contrary to some recent philosophical claims, I will argue that, although BCI technology may have the potential to become bodily and cognitive extensions for skilled users, at this stage they are not. And while the electrodes may to a variable degree be transparent and incorporated in the body schema, the BCI system as a whole is not. Moreover, BCIs do not have a functional role characteristic for cognition and are therefore not cognitive extensions. Third, based on concepts from the distributed cognition framework, I give a number of suggestions to improve the interface design of linguistic applications, i.e. BCIs that allow its user to spell words by selecting letters on a screen. These suggestions may result in cognitive extension and would enhance the autonomy and quality of life of its users. In sum, in this paper I develop a typology, analysis and critique on the current philosophical debate on BCIs, thereby providing a richer conceptual understanding of BCI systems which allows me to offer some suggestions for improving the interface design of linguistic applications.

P300-Based BCI Mouse With Genetically-Optimized Analogue Control

by Luca Citi

In this paper we propose a brain-computer interface (BCI) mouse based on P300 waves in electroencephalogram (EEG)... more In this paper we propose a brain-computer interface (BCI) mouse based on P300 waves in electroencephalogram (EEG) signals. The system is analogue in that at no point a binary decision is made as to whether or not a P300 was actually produced in response to the stimuli. Instead, the 2-D motion of the pointer on the screen, using a novel BCI paradigm, is controlled by directly combining the amplitudes of the output produced by a filter in the presence of different stimuli. This filter and the features to be combined within it are optimised by an evolutionary algorithm.

Documenting, Modelling and Exploiting P300 Amplitude Changes Due to Variable Target Delays In Donchin's Speller

by Luca Citi

The P300 is an endogenous event-related potential (ERP) that is naturally elicited by rare and significant external... more The P300 is an endogenous event-related potential (ERP) that is naturally elicited by rare and significant external stimuli. P300s are used increasingly frequently in brain-computer interfaces (BCIs) because the users of ERP-based BCIs need no special training. However, P300 waves are hard to detect and, therefore, multiple target stimulus presentations are needed before an interface can make a reliable decision. While significant improvements have been made in the detection of P300s, no particular attention has been paid to the variability in shape and timing of P300 waves in BCIs. In this paper we start filling this gap by documenting, modelling and exploiting a modulation in the amplitude of P300s related to the number of non-targets preceding a target in a Donchin speller. The basic idea in our approach is to use an appropriately weighted average of the responses produced by a classifier during multiple stimulus presentations, instead of the traditional plain average. This makes it possible to weigh more heavily events that are likely to be more informative, thereby increasing the accuracy of classification. The optimal weights are determined through a mathematical model that precisely estimates the accuracy of our speller as well as the expected performance improvement w.r.t. the traditional approach. Tests with two independent datasets show that our approach provides a marked statistically significant improvement in accuracy over the top-performing algorithm presented in the literature to date. The method and the theoretical models we propose are general and can easily be used in other P300-based BCIs with minimal changes.

Prospects of Brain-Machine Interfaces for Space System Control

by Luca Citi

The dream of controlling and guiding computer-based systems using human brain signals has slowly but steadily become a... more The dream of controlling and guiding computer-based systems using human brain signals has slowly but steadily become a reality. The available technology allows real-time implementation of systems that measure neuronal activity, convert their signals, and translate their output for the purpose of controlling mechanical and electronic systems. This paper describes the state of the art of non-invasive brain-machine interfaces (BMIs) and critically investigates both the current technological limits and the future potential that BMIs have for space applications. We present an assessment of the advantages that BMIs can provide and justify the preferred candidate concepts for space applications together with a vision of future directions for their implementation.

Android tele-operation through Brain-Computer Interfacing: A real-world demo with non-expert users

by Nikolaos Mavridis

Co-authored with: Christoforou, C. Machado, E.L., Spanoudis, G., published in Proceedings of the International Symposium on Robotics and Intelligent Sensors, Nagoya, IRIS 2010, program available at:
http://ns1.ohka.cs.is.nagoya-u.ac.jp/~iris_admin/IRIS2010_files/IRIS2010%20Detailed%20Program.pdf

Towards our vision of natural robotic tele-presence by thought, a real-world system which was demonstrated in public... more Towards our vision of natural robotic tele-presence by thought, a real-world system which was demonstrated in public with non-expert users is presented. The system consists of an EEG-based brain computer interface module (BCI), located in Cyprus, and relying on machine learning methods for recognizing brain patterns and translating them into control commands. The BCI module is controlling Ibn Sina, an android robot which is located 1400 miles away in the United Arab Emirates, in real-time, through an IP connection. Visual feedback from the robot is provided through a video link to the users. Previously untrained human users operated the system in multiple occasions (during public demos). We describe the demonstrations, as well as the structure of our system, and report quantitative measures of our system’s performance. This is up to our knowledge the first time that non-expert humans have controlled a remote android through thought during a public demonstration.

Brain Machine Interfaces for Space Applications: Enhancing Astronaut Capabilities

by Leopold Summerer

Combining fNIRS and EEG to improve motor cortex activity classification during an imagined movement-based task

by Darren Leamy

Submitted and accepted to HCII 2011.

This work serves as an initial investigation into improvements to classification accuracy of an imagined... more This work serves as an initial investigation into improvements to classification accuracy of an imagined movement-based Brain Computer Interface (BCI) by combining the feature spaces of two unique measurement modalities: functional near infrared spectroscopy (fNIRS) and electroencephalography (EEG). Our dual-modality system recorded concurrent and co-locational hemodynamic and electrical responses in the motor cortex during an imagined movement task, participated in by two subjects. Offline analysis and classification of fNIRS and EEG data was performed using leave-one-out cross-validation (LOOCV) and linear discriminant analysis (LDA). Classification of 2-dimensional fNIRS and EEG feature spaces was performed separately and then their feature spaces were combined for further classification. Results of our investigation indicate that by combining feature spaces, modest gains in classification accuracy of an imagined movement-based BCI can be achieved by employing a supplemental measurement modality. It is felt that this technique may be particularly useful in the design of BCI devices for the augmentation of rehabilitation therapy.