Quantifying the impact on navigation performance in visually impaired: Auditory information loss versus information gain enabled through electronic travel aids.

This study's purpose was to analyze and quantify the impact of auditory information loss versus information gain provided by electronic travel aids (ETAs) on navigation performance in people with low vision. Navigation performance of ten subjects (age: 54.9±11.2 years) with visual acuities >1.0 LogMAR was assessed via the Graz Mobility Test (GMT). Subjects passed through a maze in three different modalities: 'Normal' with visual and auditory information available, 'Auditory Information Loss' with artificially reduced hearing (leaving only visual information), and 'ETA' with a vibrating ETA based on ultrasonic waves, thereby facilitating visual, auditory, and tactile information. Main performance measures comprised passage time and number of contacts. Additionally, head tracking was used to relate head movements to motion direction. When comparing 'Auditory Information Loss' to 'Normal', subjects needed significantly more time (p<0.001), made more contacts (p<0.001), had higher relative viewing angles (p = 0.002), and a higher percentage of orientation losses (p = 0.011). The only significant difference when comparing 'ETA' to 'Normal' was a reduced number of contacts (p<0.001). Our study provides objective, quantifiable measures of the impact of reduced hearing on the navigation performance in low vision subjects. Significant effects of 'Auditory Information Loss' were found for all measures; for example, passage time increased by 17.4%. These findings show that low vision subjects rely on auditory information for navigation. In contrast, the impact of the ETA was not significant but further analysis of head movements revealed two different coping strategies: half of the subjects used the ETA to increase speed, whereas the other half aimed at avoiding contacts.

Single Versus Multiple Events Error Potential Detection in a BCI-Controlled Car Game With Continuous and Discrete Feedback.

OBJECTIVE: This work aimed to find and evaluate a new method for detecting errors in continuous brain-computer interface (BCI) applications. Instead of classifying errors on a single-trial basis, the new method was based on multiple events (MEs) analysis to increase the accuracy of error detection. METHODS: In a BCI-driven car game, based on motor imagery (MI), discrete events were triggered whenever subjects collided with coins and/or barriers. Coins counted as correct events, whereas barriers were errors. This new method, termed ME method, combined and averaged the classification results of single events (SEs) and determined the correctness of MI trials, which consisted of event sequences instead of SEs. The benefit of this method was evaluated in an offline simulation. In an online experiment, the new method was used to detect erroneous MI trials. Such MI trials were discarded and could be repeated by the users. RESULTS: We found that, even with low SE error potential (ErrP) detection rates, feasible accuracies can be achieved when combining MEs to distinguish erroneous from correct MI trials. Online, all subjects reached higher scores with error detection than without, at the cost of longer times needed for completing the game. CONCLUSION: Findings suggest that ErrP detection may become a reliable tool for monitoring continuous states in BCI applications when combining MEs. SIGNIFICANCE: This paper demonstrates a novel technique for detecting errors in online continuous BCI applications, which yields promising results even with low single-trial detection rates.

Cortical effects of user training in a motor imagery based brain-computer interface measured by fNIRS and EEG.

The present study aims to gain insights into the effects of training with a motor imagery (MI)-based brain-computer interface (BCI) on activation patterns of the sensorimotor cortex. We used functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG) to investigate long-term training effects across 10 sessions using a 2-class (right hand and feet) MI-based BCI in fifteen subjects. In the course of the training a significant enhancement of activation pattern emerges, represented by an [oxy-Hb] increase in fNIRS and a stronger event-related desynchronization in the upper ß-frequency band in the EEG. These effects were only visible in participants with relatively low BCI performance (mean accuracy ≤ 70%). We found that training with an MI-based BCI affects cortical activation patterns especially in users with low BCI performance. Our results may serve as a valuable contribution to the field of BCI research and provide information about the effects that training with an MI-based BCI has on cortical activation patterns. This might be useful for clinical applications of BCI which aim at promoting and guiding neuroplasticity.

Hybrid brain-computer interfaces and hybrid neuroprostheses for restoration of upper limb functions in individuals with high-level spinal cord injury.

BACKGROUND: The bilateral loss of the grasp function associated with a lesion of the cervical spinal cord severely limits the affected individuals' ability to live independently and return to gainful employment after sustaining a spinal cord injury (SCI). Any improvement in lost or limited grasp function is highly desirable. With current neuroprostheses, relevant improvements can be achieved in end users with preserved shoulder and elbow, but missing hand function. OBJECTIVE: The aim of this single case study is to show that (1) with the support of hybrid neuroprostheses combining functional electrical stimulation (FES) with orthoses, restoration of hand, finger and elbow function is possible in users with high-level SCI and (2) shared control principles can be effectively used to allow for a brain-computer interface (BCI) control, even if only moderate BCI performance is achieved after extensive training. PATIENT AND METHODS: The individual in this study is a right-handed 41-year-old man who sustained a traumatic SCI in 2009 and has a complete motor and sensory lesion at the level of C4. He is unable to generate functionally relevant movements of the elbow, hand and fingers on either side. He underwent extensive FES training (30-45min, 2-3 times per week for 6 months) and motor imagery (MI) BCI training (415 runs in 43 sessions over 12 months). To meet individual needs, the system was designed in a modular fashion including an intelligent control approach encompassing two input modalities, namely an MI-BCI and shoulder movements. RESULTS: After one year of training, the end user's MI-BCI performance ranged from 50% to 93% (average: 70.5%). The performance of the hybrid system was evaluated with different functional assessments. The user was able to transfer objects of the grasp-and-release-test and he succeeded in eating a pretzel stick, signing a document and eating an ice cream cone, which he was unable to do without the system. CONCLUSION: This proof-of-concept study has demonstrated that with the support of hybrid FES systems consisting of FES and a semiactive orthosis, restoring hand, finger and elbow function is possible in a tetraplegic end-user. Remarkably, even after one year of training and 415 MI-BCI runs, the end user's average BCI performance remained at about 70%. This supports the view that in high-level tetraplegic subjects, an initially moderate BCI performance cannot be improved by extensive training. However, this aspect has to be validated in future studies with a larger population.

Error potential detection during continuous movement of an artificial arm controlled by brain-computer interface.

Patients who benefit from Brain-Computer Interfaces (BCIs) may have difficulties to generate more than one distinct brain pattern which can be used to control applications. Other BCI issues are low performance, accuracy, and, depending on the type of BCI, a long preparation and/or training time. This study aims to show possible solutions. First, we used time-coded motor imagery (MI) with only one pattern. Second, we reduced the training time by recording only 20 trials of active MI to set up a BCI classifier. Third, we investigated a way to record error potentials (ErrPs) during continuous feedback. Ten subjects controlled an artificial arm by performing MI over target time periods between 1 and 4 s. The subsequent movement of this arm served as continuous feedback. Discrete events, which are required to elicit ErrPs, were added by mounting blinking LEDs on top of the continuously moving arm to indicate the future movements. Time epochs after these events were used to evaluate ErrPs offline. The achieved error rate for the arm movement was on average 26.9%. Obtained ErrPs looked similar to results from the previous studies dealing with error detection and the detection rate was above chance level which is a positive outcome and encourages further investigation.

Development of a non-invasive, multifunctional grasp neuroprosthesis and its evaluation in an individual with a high spinal cord injury.

Over the last decade the improvement of a missing hand function by application of neuroprostheses in particular the implantable Freehand system has been successfully shown in high spinal cord injured individuals. The clinically proven advantages of the Freehand system is its ease of use, the reproducible generation of two distinct functional grasp patterns and an analog control scheme based on movements of the contralateral shoulder. However, after the Freehand system is not commercially available for more than ten years, alternative grasp neuroprosthesis with a comparable functionality are still missing. Therefore, the aim of this study was to develop a non-invasive neuroprosthesis and to show that a degree of functional restoration can be provided to end users comparable to implanted devices. By introduction of an easy to handle forearm electrode sleeve the reproducible generation of two grasp patterns has been achieved. Generated grasp forces of the palmar grasp are in the range of the implanted system. Though pinch force of the lateral grasp is significantly lower, it can effectively used by a tetraplegic subject to perform functional tasks. The non-invasive grasp neuroprosthesis developed in this work may serve as an easy to apply and inexpensive way to restore a missing hand and finger function at any time after spinal cord injury.

What does clean EEG look like?

Lack of a clear analytical metric for identifying artifact free, clean electroencephalographic (EEG) signals inhibits robust comparison of different artifact removal methods and lowers confidence in the results of EEG analysis. An algorithm is presented for identifying clean EEG epochs by thresholding statistical properties of the EEG. Thresholds are trained on EEG datasets from both healthy subjects and stroke / spinal cord injury patient populations via differential evolution (DE).

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.

First Steps Toward a Motor Imagery Based Stroke BCI: New Strategy to Set up a Classifier.

A new approach in motor rehabilitation after stroke is to use motor imagery (MI). To give feedback on MI performance brain-computer interface (BCIs) can be used. This requires a fast and easy acquisition of a reliable classifier. Usually, for training a classifier, electroencephalogram (EEG) data of MI without feedback is used, but it would be advantageous if we could give feedback right from the beginning. The sensorimotor EEG changes of the motor cortex during active and passive movement (PM) and MI are similar. The aim of this study is to explore, whether it is possible to use EEG data from active or PM to set up a classifier for the detection of MI in a group of elderly persons. In addition, the activation patterns of the motor cortical areas of elderly persons were analyzed during different motor tasks. EEG was recorded from three Laplacian channels over the sensorimotor cortex in a sample of 19 healthy elderly volunteers. Participants performed three different tasks in consecutive order, passive, active hand movement, and hand MI. Classifiers were calculated with data of every task. These classifiers were then used to detect event-related desynchronization (ERD) in the MI data. ERD values, related to the different tasks, were calculated and analyzed statistically. The performance of classifiers calculated from passive and active hand movement data did not differ significantly regarding the classification accuracy for detecting MI. The EEG patterns of the motor cortical areas during the different tasks was similar to the patterns normally found in younger persons but more widespread regarding localization and frequency range of the ERD. In this study, we have shown that it is possible to use classifiers calculated with data from passive and active hand movement to detect MI. Hence, for working with stroke patients, a physiotherapy session could be used to obtain data for classifier set up and the BCI-rehabilitation training could start immediately.

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.