This paper presents an algorithm based on neural networks and fuzzy theory (S-dFasArt) to classify spontaneous mental activities from electroencephalogram (EEG) signals, in order to operate a noninvasive brain–computer interface. The focus is placed on the three-class problem, left-hand movement imagination, right movement imagination and word generation. The algorithm allows a supervised classification of temporal patterns improving the classification rates of the BCI Competition III (Data Set V: multiclass problem, continuous EEG). Using the precomputed data supplied for the competition and following the rules established there, a new method based on S-dFasArt, along with rule prune and voting strategy is proposed. The results have been compared with other published methods improving their success rates.

The oddball protocol is often used in brain–computer interfaces (BCIs) to induce P300 ERPs, although, recently, some issues have been shown to detrimentally effect its performance. In this paper, we study a new periodic protocol and explore whether it can compete with the standard oddball protocol within the context of a BCI mouse. We found that the new protocol consistently and significantly outperforms the standard oddball protocol in relation to information transfer rates (33 bits/min for the former and 22 bits/min for the latter, measured at 90% accuracy) as well as P300 amplitudes. Furthermore, we performed a comparison of two periodic protocols with two less conventional oddball-like protocols that reveals the importance of the interactions between task and sequence in determining the success of a protocol.

This paper quantifies and comparatively validates functional connectivity between neurons by measuring the statistical dependence between their firing rates. Based on statistical analysis of the pairwise functional connectivity, we estimate, exclusively from neural data, the neural assembly functional connectivity given a behavior task, which provides a quantifiable representation of the dynamic nature during the behavioral task. Because of the time scale of behavior (100–1000 ms), a statistical method that yields robust estimators for this small sample size is desirable. In this work, the temporal resolutions of four estimators of functional connectivity are compared on both simulated data and real neural ensemble recordings. The comparison highlights how the properties and assumptions of statistical-based and phase-based metrics affect the interpretation of connectivity. Simulation results show that mean square contingency (MSC) and mutual information (MI) create more robust quantification of functional connectivity under identical conditions than cross correlation (CC) and phase synchronization (PhS) when the sample size is 1 s. The results of the simulated analysis are extended to real neuronal recordings to assess the functional connectivity in monkey's cortex corresponding to three movement states in a food reaching task and construct the assembly graph given a movement state and the activation degree of a state-related assembly over time using the statistical test exclusively from neural data dependencies. The activation degree of a given state-related assembly reaches the peak repeatedly when the specific movement states occur, which also reveals the network of interactions among the neurons are key for the operation of a specific behavior.

Incorporating sensory feedback with prosthetic devices is now possible, but the optimal methods of providing such feedback are still unknown. The relative utility of amplitude and pulse train frequency modulated stimulation paradigms for providing vibrotactile feedback for object manipulation was assessed in 10 participants. The two approaches were studied during virtual object manipulation using a robotic interface as a function of presentation order and a simultaneous cognitive load. Despite the potential pragmatic benefits associated with pulse train frequency modulated vibrotactile stimulation, comparison of the approach with amplitude modulation indicates that amplitude modulation vibrotactile stimulation provides superior feedback for object manipulation.

Hammersmith Infant Neurological Examination (HINE) is a set of tests used for grading neurological development of infants on a scale of 0 to 3. These tests help in assessing neurophysiological development of babies, especially preterm infants who are born before (the fetus reaches) the gestational age of 36 weeks. Such tests are often conducted in the follow-up clinics of hospitals for grading infants with suspected disabilities. Assessment based on HINE depends on the expertise of the physicians involved in conducting the examinations. It has been noted that some of these tests, especially pulled-to-sit and lateral tilting, are difficult to assess solely based on visual observation. For example, during the pulled-to-sit examination, the examiner needs to observe the relative movement of the head with respect to torso while pulling the infant by holding wrists. The examiner may find it difficult to follow the head movement from the coronal view. Video object tracking based automatic or semi-automatic analysis can be helpful in this case. In this paper, we present a video based method to automate the analysis of pulled-to-sit examination. In this context, a dynamic programming and node pruning based efficient video object tracking algorithm has been proposed. Pulled-to-sit event detection is handled by the proposed tracking algorithm that uses a 2-D geometric model of the scene. The algorithm has been tested with normal as well as marker based videos of the examination recorded at the neuro-development clinic of the SSKM Hospital, Kolkata, India. It is found that the proposed algorithm is capable of estimating the pulled-to-sit score with sensitivity (80%–92%) and specificity (89%–96%).

Both the American Heart Association and the VA/DoD endorse upper-extremity robot-mediated rehabilitation therapy for stroke care. However, we do not know yet how to optimize therapy for a particular patient's needs. Here, we explore whether we must train patients for each functional task that they must perform during their activities of daily living or alternatively capacitate patients to perform a class of tasks and have therapists assist them later in translating the observed gains into activities of daily living. The former implies that motor adaptation is a better model for motor recovery. The latter implies that motor learning (which allows for generalization) is a better model for motor recovery. We quantified trained and untrained movements performed by 158 recovering stroke patients via 13 metrics, including movement smoothness and submovements. Improvements were observed both in trained and untrained movements suggesting that generalization occurred. Our findings suggest that, as motor recovery progresses, an internal representation of the task is rebuilt by the brain in a process that better resembles motor learning than motor adaptation. Our findings highlight possible improvements for therapeutic algorithms design, suggesting sparse-activity-set training should suffice over exhaustive sets of task specific training.

This paper presents the design and preliminary experimental validation of a multigrasp myoelectric controller. The described method enables direct and proportional control of multigrasp prosthetic hand motion among nine characteristic postures using two surface electromyography electrodes. To assess the efficacy of the control method, five nonamputee subjects utilized the multigrasp myoelectric controller to command the motion of a virtual prosthesis between random sequences of target hand postures in a series of experimental trials. For comparison, the same subjects also utilized a data glove, worn on their native hand, to command the motion of the virtual prosthesis for similar sequences of target postures during each trial. The time required to transition from posture to posture and the percentage of correctly completed transitions were evaluated to characterize the ability to control the virtual prosthesis using each method. The average overall transition times across all subjects were found to be 1.49 and 0.81 s for the multigrasp myoelectric controller and the native hand, respectively. The average transition completion rates for both were found to be the same (99.2%). Supplemental videos demonstrate the virtual prosthesis experiments, as well as a preliminary hardware implementation.

A new method of lower-limb exoskeleton control aimed at improving the agility of leg-swing motion is presented. In the absence of control, an exoskeleton's mechanism usually hinders agility by adding mechanical impedance to the legs. The uncompensated inertia of the exoskeleton will reduce the natural frequency of leg swing, probably leading to lower step frequency during walking as well as increased metabolic energy consumption. The proposed controller emulates inertia compensation by adding a feedback loop consisting of low-pass filtered angular acceleration multiplied by a negative gain. This gain simulates negative inertia in the low-frequency range. The resulting controller combines two assistive effects: increasing the natural frequency of the lower limbs and performing net work per swing cycle. The controller was tested on a statically mounted exoskeleton that assists knee flexion and extension. Subjects performed movement sequences, first unassisted and then using the exoskeleton, in the context of a computer-based task resembling a race. In the exoskeleton's baseline state, the frequency of leg swing and the mean angular velocity were consistently reduced. The addition of inertia compensation enabled subjects to recover their normal frequency and increase their selected angular velocity. The work performed by the exoskeleton was evidenced by catch trials in the protocol.

Functional decline of lower-limb affects the ability of locomotion and the age-related brain differences have been elucidated among the elderly. Cycling exercise is a common training program for restoring motor function in the deconditioned elderly or stroke patients. The provision of speed feedback has been commonly suggested to clinical therapists for facilitating learning of controlled cycling performance and maintaining motivation in training programs with elderly participants. However, the cortical control of pedaling movements and the effect of external feedback remain poorly understanding. This study investigated the regional cortical activities detected by functional near infrared spectroscopy (fNIRS) in 12 healthy young and 13 healthy elderly subjects under conditions of cycling without-(free cycling) and with feedback (target cycling). The elderly exhibited predominant activation of the sensorimotor cortex during free cycling similar to young subjects but with poorer cycling performance. The cycling performance improved in both groups, and the elderly showed increased brain activities of the supplementary motor area and premotor cortex under target cycling condition. These findings demonstrated age-related changes in the cortical control in processing external feedback and pedaling movements. Use of fNIRS to evaluate brain activation patterns after training may facilitate brain-based design of tailored therapeutic rehabilitation strategies.

Intracortical microstimulation (ICMS) has promise as a means for delivering somatosensory feedback in neuroprosthetic systems. Various tactile sensations could be encoded by temporal, spatial, or spatiotemporal patterns of ICMS. However, the applicability of temporal patterns of ICMS to artificial tactile sensation during active exploration is unknown, as is the minimum discriminable difference between temporally modulated ICMS patterns. We trained rhesus monkeys in an active exploration task in which they discriminated periodic pulse-trains of ICMS (200 Hz bursts at a 10 Hz secondary frequency) from pulse trains with the same average pulse rate, but distorted periodicity (200 Hz bursts at a variable instantaneous secondary frequency). The statistics of the aperiodic pulse trains were drawn from a gamma distribution with mean inter-burst intervals equal to those of the periodic pulse trains. The monkeys distinguished periodic pulse trains from aperiodic pulse trains with coefficients of variation 0.25 or greater. Reconstruction of movement kinematics, extracted from the activity of neuronal populations recorded in the sensorimotor cortex concurrent with the delivery of ICMS feedback, improved when the recording intervals affected by ICMS artifacts were removed from analysis. These results add to the growing evidence that temporally patterned ICMS can be used to simulate a tactile sense for neuroprosthetic devices.

Electrical stimulation of arm and hand muscles can be a functional tool for patients with motor dysfunction. Sufficient stimulation of finger and thumb musculature can support natural grasping function. Yet it remains unclear how different grasping movements can be selectively supported by electrical stimulation. The goal of this study is to determine to what extent activation of individual fingers is possible with surface electrical stimulation for the purpose of rehabilitation following stroke. The extensor digitorum communis (EDC) muscle, flexor pollicis longus (FPL) muscle, and the thenar muscle group, all involved in grasp and release, were selected for stimulation. The evoked forces in individual fingers were measured. Stimulation thresholds and selective ranges were determined for each subject. Electrode locations where the highest selective range occurred were compared between subjects and influences of different isometric wrist positions were assessed. In all subjects selective stimulation of middle finger extension and thumb flexion was possible. In addition, selective stimulation of index and ring finger extension was possible in most cases. In nine out of the ten EDC subjects we were able to stimulate three or all four fingers selectively. However, large variability in electrode locations for high selectivity was observed between the subjects. Within the designs of grasping prostheses and grasping rehabilitation devices, the variability of electrode locations should be taken into account. The results of our study facilitate the optimization of such designs and favour a design which allows individualized stimulation locations.

The P300-based speller is a well-established brain–computer interface for communication. It displays a matrix of objects on the computer screen, flashes each object in sequence, and looks for a P300 response induced by flashing the desired object. Most existing P300 spellers uses a fixed set of flash objects. We demonstrate that performance can be significantly improved by sequential selections from a hierarchy of flash sets containing variable number of objects. Theoretically, the optimal hierarchy of flash sets—with respect to a given statistical language model—can be found by solving a stochastic control problem of low computational complexity. Experimentally, statistical analysis demonstrates that the average time per output character at 85% accuracy is reduced by over 50% using our variable-flash-set approach as compared to traditional fixed-flash-set spellers.