Christian Breitwieser

Tools for Brain-Computer Interaction: A General Concept for a Hybrid BCI

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 in the background. The hBCI might decide on the one hand which input channel(s) offer the most reliable signal(s) and switch between input channels to improve information transfer rate, usability, or other factors, or on the other hand fuse various input channels. One major goal therefore is to bring the BCI technology to a level where it can be used in a maximum number of scenarios in a simple way. To achieve this, it is of great importance that the hBCI is able to operate reliably for long periods, recognizing and adapting to changes as it does so. This goal is only possible if many different subsystems in the hBCI can work together. Since one research institute alone cannot provide such different functionality, collaboration between institutes is necessary. To allow for such a collaboration, a new concept and common software framework is introduced. It consists of four interfaces connecting the classical BCI modules: signal acquisition, preprocessing, feature extraction, classification, and the application. But it provides also the concept of fusion and shared control. In a proof of concept, the functionality of the proposed system was demonstrated.

Switching between Manual Control and Brain-Computer Interface Using Long Term and Short Term Quality Measures

Assistive devices for persons with limited motor control translate or amplify remaining functions to allow otherwise impossible actions. These assistive devices usually rely on just one type of input signal which can be derived from residual muscle functions or any other kind of biosignal. When only one signal is used, the functionality of the assistive device can be reduced as soon as the quality of the provided signal is impaired. The quality can decrease in case of fatigue, lack of concentration, high noise, spasms, tremors, depending on the type of signal. To overcome this dependency on one input signal, a combination of more inputs should be feasible. This work presents a hybrid Brain-Computer Interface (hBCI) approach where two different input signals (joystick and BCI) were monitored and only one of them was chosen as a control signal at a time. Users could move a car in a game-like feedback application to collect coins and avoid obstacles via either joystick or BCI control. Both control types were constantly monitored with four different long term quality measures to evaluate the current state of the signals. As soon as the quality dropped below a certain threshold, a monitoring system would switch to the other control mode and vice versa. Additionally, short term quality measures were applied to check for strong artifacts that could render voluntary control impossible. These measures were used to prohibit actions carried out during times when highly uncertain signals were recorded. The switching possibility allowed more functionality for the users. Moving the car was still possible even after one control mode was not working any more. The proposed system serves as a basis that shows how BCI can be used as an assistive device, especially in combination with other assistive technology.

Proposing a Standardized Protocol for Raw Biosignal Transmission

In this paper, we propose a standardized interface called TiA (TOBI interface A) to transmit raw biosignals, supporting multirate and block-oriented transmission of different kinds of signals from various acquisition devices (e.g., EEG, electrooculogram, near-infrared spectroscopy signals, etc.) at the same time. To facilitate a distinction between those kinds of signals, so-called signal types are introduced. TiA is a single-server, multiple-client system, whereby clients can connect to the server at runtime. Information transfer between client and server is divided into control and data connections. The control connections use transmission control protocol (TCP) and transmit extensible-markup-language (XML)-encoded meta information. The data transmission utilizes a user datagram protocol (UDP) or TCP with a binary data stream. A standardized handshaking procedure for the connection setup and a standardized binary data packet has been defined. Thus, a standardized layer, abstracting used hardware devices and facilitating distributed raw data transmission in a standardized way, has been evolved. A cross-platform library, implemented in C++, is available for download.

Somatosensory evoked potentials elicited by stimulating two fingers from one hand — Usable for BCI?

Steady-state somatosensory evoked potentials (SSSEPs) have been elicited using vibro-tactile stimulation on two fingers of the right hand. Fourteen healthy subjects participated in this study. A screening session, stimulating each participants thumb, was conducted to determine individual optimal resonance-like frequencies. After this screening session, two stimulation frequencies per subject were selected. Stimulation was then applied simultaneously on the participants thumbs and middle finger. It was investigated whether it is possible to classify SSSEP changes based on an attention modulation task to determine possible BCI applications. A cue indicated the participants to shift their attention to either the thumb or the middle finger. Offline classification with a lock-in analyzer system (LAS) and a linear discriminant analysis (LDA) classifier was performed. One bipolar channel and no further optimization methods were used. All participants except one reached classification results above chance level classifying a reference period without focused attention against focused attention either to the thumb or the middle finger. Only two subjects reached accuracies above chance, classifying focused attention to the thumb vs. attention to the middle finger.

A hybrid three-class brain-computer interface system utilizing SSSEPs and transient ERPs.

OBJECTIVE This paper investigates the fusion of steady-state somatosensory evoked potentials (SSSEPs) and transient event-related potentials (tERPs), evoked through tactile simulation on the left and right-hand fingertips, in a three-class EEG based hybrid brain-computer interface. It was hypothesized, that fusing the input signals leads to higher classification rates than classifying tERP and SSSEP individually. APPROACH Fourteen subjects participated in the studies, consisting of a screening paradigm to determine person dependent resonance-like frequencies and a subsequent online paradigm. The whole setup of the BCI system was based on open interfaces, following suggestions for a common implementation platform. During the online experiment, subjects were instructed to focus their attention on the stimulated fingertips as indicated by a visual cue. The recorded data were classified during runtime using a multi-class shrinkage LDA classifier and the outputs were fused together applying a posterior probability based fusion. Data were further analyzed offline, involving a combined classification of SSSEP and tERP features as a second fusion principle. The final results were tested for statistical significance applying a repeated measures ANOVA. MAIN RESULTS A significant classification increase was achieved when fusing the results with a combined classification compared to performing an individual classification. Furthermore, the SSSEP classifier was significantly better in detecting a non-control state, whereas the tERP classifier was significantly better in detecting control states. Subjects who had a higher relative band power increase during the screening session also achieved significantly higher classification results than subjects with lower relative band power increase. SIGNIFICANCE It could be shown that utilizing SSSEP and tERP for hBCIs increases the classification accuracy and also that tERP and SSSEP are not classifying control- and non-control states with the same level of accuracy.

A Tactile Stimulation Device for EEG Measurements in Clinical Use

A tactile stimulation device for EEG measurements in clinical environments is proposed. The main purpose of the tactile stimulation device is to provide tactile stimulation to different parts of the body. To stimulate all four major types of mechanoreceptors, different stimulation patterns with frequencies in the range of 5-250 Hz have to be generated. The device provides two independent channels, delivers enough power to drive different types of electromagnetic transducers, is small and portable, and no expensive components are required to construct this device. The generated stimulation patterns are very stable, and deterministic control of the device is possible. To meet electrical safety requirements, the device was designed to be fully galvanically isolated. Leakage currents of the entire EEG measurement system including the tactile stimulation device were measured by the European Testing and Certifying Body for Medical Products Graz (Notified Body 0636). All measured currents were far below the maximum allowable currents defined in the safety standard EN 60601-1:2006 for medical electrical equipment. The successful operation of the tactile stimulation device was tested during an EEG experiment. The left and right wrist of one healthy subject were randomly stimulated with seven different frequencies. Steady-state somatosensory evoked potential (SSSEPs) could successfully be evoked and significant tuning curves at electrode positions contralateral to the stimulated wrist could be found. The device is ready to be used in clinical environment in a variety of applications to investigate the somatosensory system, in brain-computer interfaces (BCIs), or to provide tactile feedback.

iScope: viewing biosignals on mobile devices

We developed an iOS based application called iScope to monitor biosignals online. iScope is able to receive different signal types via a wireless network connection and is able to present them in the time or the frequency domain. Thus it is possible to inspect recorded data immediately during the recording process and detect potential artifacts early without the need to carry around heavy equipment like laptops or complete PC workstations. The iScope app has been tested during various measurements on the iPhone 3GS as well as on the iPad 1 and is fully functional.

A concept to standardize raw biosignal transmission for brain-computer interfaces

With this concept we introduced the attempt of a standardized interface called TiA to transmit raw biosignals. TiA is able to deal with multirate and block-oriented data transmission. Data is distinguished by different signal types (e.g., EEG, EOG, NIRS, …), whereby those signals can be acquired at the same time from different acquisition devices. TiA is built as a client-server model. Multiple clients can connect to one server. Information is exchanged via a control- and a separated data connection. Control commands and meta information are transmitted over the control connection. Raw biosignal data is delivered using the data connection in a unidirectional way. For this purpose a standardized handshaking protocol and raw data packet have been developed. Thus, an abstraction layer between hardware devices and data processing was evolved facilitating standardization.

Sigviewer and Signalserver – Open Source Software Projects for Biosignal Analysis

We have developed two open source biosignal processing applications used at both ends of the signal processing chain, namely the signal acquisition server SignalServer and the signal visualization and analysis application SigViewer. Both programs are cross-platform (that is, they run under Windows, Mac OS X, and Linux operating systems), free open source software, and licensed under the GNU General Public License (GPL). SignalServer records raw data from various data acquisition devices and sends the data over the network in a standardized format. SigViewer reads many different biosignal formats and visualizes the contained multi-channel time series data. In addition, SigViewer supports annotations via custom event markers.