It is of wide interest to study the brain activity that correlates to the control of brain-computer interface (BCI). In the present study, we have developed an approach to image the cortical rhythmic modulation associated with motor imagery using minimum-norm estimates in the frequency domain (MNEFD). The distribution of cortical sources of mu activity during online control of BCI was obtained with the MNEFD. Contralateral decrease (event-related desynchronization) and ipsilateral increase (event-related synchronization) are localized in the sensorimotor cortex during online control of BCI in a group of human subjects. Statistical source analysis revealed that maximum correlation with movement imagination is localized in sensorimotor cortex.

We report the design and performance of a brain-computer interface (BCI) system for real-time single-trial binary classification of viewed images based on participant-specific dynamic brain response signatures in high-density (128-channel) electroencephalographic (EEG) data acquired during a rapid serial visual presentation (RSVP) task. Image clips were selected from a broad area image and presented in rapid succession (12/s) in 4.1-s bursts. Participants indicated by subsequent button press whether or not each burst of images included a target airplane feature. Image clip creation and search path selection were designed to maximize user comfort and maintain user awareness of spatial context. Independent component analysis (ICA) was used to extract a set of independent source time-courses and their minimally-redundant low-dimensional informative features in the time and time-frequency amplitude domains from 128-channel EEG data recorded during clip burst presentations in a training session. The naive Bayes fusion of two Fisher discriminant classifiers, computed from the 100 most discriminative time and time-frequency features, respectively, was used to estimate the likelihood that each clip contained a target feature. This estimator was applied online in a subsequent test session. Across eight training/test session pairs from seven participants, median area under the receiver operator characteristic curve, by tenfold cross validation, was 0.97 for within-session and 0.87 for between-session estimates, and was nearly as high (0.83) for targets presented in bursts that participants mistakenly reported to include no target features.

The extraction of the salient characteristics from brain connectivity patterns is an open challenging topic since often the estimated cerebral networks have a relative large size and complex structure. Since a graph is a mathematical representation of a network, which is essentially reduced to nodes and connections between them, the use of a theoretical graph approach would extract significant information from the functional brain networks estimated through different neuroimaging techniques. The present work intends to support the development of the ldquobrain network analysis:rdquo a mathematical tool consisting in a body of indexes based on the graph theory able to improve the comprehension of the complex interactions within the brain. In the present work, we applied for demonstrative purpose some graph indexes to the time-varying networks estimated from a set of high-resolution EEG data in a group of healthy subjects during the performance of a motor task. The comparison with a random benchmark allowed extracting the significant properties of the estimated networks in the representative Alpha (7-12 Hz) band. Altogether, our findings aim at proving how the brain network analysis could reveal important information about the time-frequency dynamics of the functional cortical networks.

Significant strides have been recently made to develop highly sensorized cybernetic prostheses aimed at restoring sensorimotor limb functions to those who have lost them because of a traumatic event (amputation). In these cases, one of the main goals is to create a bidirectional link between the artificial devices (e.g., robotic hands, arms, or legs) and the nervous system. Several human-machine interfaces (HMIs) are currently used to this aim. Among them, interfaces with the peripheral nervous system and in particular longitudinal intrafascicular electrodes can be a promising solution able to improve the current situation. In this paper, the potentials and limits of the use of this interface to control robotic devices are presented. Specific information is provided on: 1) the neurophysiological bases for the use peripheral nerve interfaces; 2) a comparison of the potentials of the different peripheral neural interfaces; 3) the possibility of extracting and appropriately interpreting the neural code for motor commands and of delivering sensory feedback by stimulating afferent fibers by using longitudinal intrafascicular electrodes; 4) a preliminary comparative analysis of the performance of this approach with the ones of others HMIs; 5) the open issues which have to be addressed for a chronic usability of this approach.

This paper describes the performance of a 16-channel implanted neuroprosthesis for standing and transfers after spinal cord injury including four-contact nerve-cuff electrodes stimulating the femoral nerve for knee extension. Responses of the nerve-cuffs were stable and standing times increased by 600% over time-matched values with a similar eight-channel neuroprosthesis utilizing muscle-based electrodes on vastus lateralis for knee extension.

Accelerometry has the potential to measure balance, defined as high-frequency body sway, ambulatorily in a simple and inexpensive way. The aim of this study was to determine and compare the sensitivity of accelerometric balance parameters during the sit-to-stand (STS) movement. Eleven healthy subjects (four males, 28.2 plusmn7.9 years) and 31 patients with stroke (21 males; 63.3plusmn12.8 years) were included. The healthy subjects performed STS movements in four conditions with different levels of difficulty. Data of the patients were compared 1) with healthy subjects, 2) between patient subgroups, and 3) between different phases of recovery to assess the sensitivity of accelerometry for differences in balance control. Accelerometers were attached to the trunk, and force plate measurements were simultaneously done in the healthy subjects. Main outcome measures were root mean square (rms) and area under the curve (AUC) derived from the high-frequency component of the transversal acceleration signal of the trunk. In all comparisons there was a significant difference in AUC data (p < 0.05), and AUC appeared to be more sensitive than rms. Variability in AUC was not completely or mainly the result of changes and differences in the duration of the STS movement. As a conclusion, accelerometry is a potentially valuable technique to measure balance during the STS movement.

We investigated the performance of three user interfaces for restoration of cursor control in individuals with tetraplegia: head orientation, electromyography(EMG) from face and neck muscles, and a standard computer mouse (for comparison). Subjects engaged in a 2-D, center-out, Fitts' Law style task and performance was evaluated using several measures. Overall, head orientation commanded motion resembled mouse commanded cursor motion (smooth, accurate movements to all targets), although with somewhat lower performance. EMG commanded movements exhibited a higher average speed, but other performance measures were lower, particularly for diagonal targets. Compared to head orientation, EMG as a cursor command source was less accurate, was more affected by target direction and was more prone to overshoot the target. In particular, EMG commands for diagonal targets were more sequential, moving first in one direction and then the other rather than moving simultaneous in the two directions. While the relative performance of each user interface differs, each has specific advantages depending on the application.

We have developed a noninvasive, unobtrusive magnetic wireless tongue-computer interface, called ldquoTongue Drive,rdquo to provide people with severe disabilities with flexible and effective computer access and environment control. A small permanent magnet secured on the tongue by implantation, piercing, or tissue adhesives, is utilized as a tracer to track the tongue movements. The magnetic field variations inside and around the mouth due to the tongue movements are detected by a pair of three-axial linear magneto-inductive sensor modules mounted bilaterally on a headset near the user's cheeks. After being wirelessly transmitted to a portable computer, the sensor output signals are processed by a differential field cancellation algorithm to eliminate the external magnetic field interference, and translated into user control commands, which could then be used to access a desktop computer, maneuver a powered wheelchair, or control other devices in the user's environment. The system has been successfully tested on six able-bodied subjects for computer access by defining six individual commands to resemble mouse functions. Results show that the Tongue Drive system response time for 87% correctly completed commands is 0.8 s, which yields to an information transfer rate of ~ 130 b/min.

A technique is described for correcting for subject movement while imaging the residual limb of a person with a transtibial amputation. Small reflective markers were placed on the residual limb, and then their motions tracked during scanning using two stationary cameras. The marker position measurements were used to generate appropriate translational and rotational transformation matrices so that limb motion could be corrected for during the 1.5-s scan interval. Evaluation tests showed good performance for moderate (2-4 mm) to high (5-8 mm) motion cases. The difference in mean absolute cross-sectional area between the test scan and a stationary reference scan was reduced by approximately one half when motion correction was used compared with when motion correction was not used. The algorithm broke down for exaggerated motion (ges 9 mm) cases, particularly in areas outside the region encompassed by the markers. The developed method is useful in prosthetics research where high resolution shape measurement is needed, for example in cases where residual limb shape or volume change is of interest.

Virtual scanning keyboards are commonly used augmentative communication aids by persons with severe speech and motion impairments. Designers of virtual scanning keyboards face problems in evaluating alternate designs and hence in choosing the better design among alternatives. Automatic evaluation of designs will be helpful to designers in making the appropriate design choice. In this paper, we present performance models for virtual scanning keyboards that can be used for automatic evaluation. The proposed models address the limitations present in the reported work on similar models. We compared the model predictions with results from user trials and established the validity of the proposed models.