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The neural dynamics underlying the coordination of spatially-directed limb and eye movements in humans is not well understood. Part of the difficulty has been a lack of signal processing tools suitable for the analysis of nonstationary electroencephalographic (EEG) signals. Here, we use multivariate empirical mode decomposition (MEMD), a data-driven approach that does not employ predefined basis functions. High-density EEG, and arm and eye movements were synchronously recorded in 10 subjects performing time-constrained reaching and/or eye movements. Subjects were allowed to move both the hand and the eyes, only the hand, or only the eyes following a 500-700 ms delay interval where the hand and gaze remained on a central fixation cross. An additional condition involved a nonspatially-directed “lift” movement of the hand. The neural activity during a 500 ms delay interval was decomposed into intrinsic mode functions (IMFs) using MEMD. Classification analysis revealed that gamma band (30 Hz <;) IMFs produced more classifiable features differentiating the EEG according to the different upcoming movements. A benchmark test using conventional algorithms demonstrated that MEMD was the best algorithm for extracting oscillatory bands from EEG, yielding the best classification of the different movement conditions. The gamma rhythm decomposed using MEMD showed a higher correlation with the eventual movement accuracy than any other band rhythm and than any other algorithm. [ABSTRACT FROM PUBLISHER]

Joint blind source separation (JBSS) is a means to extract common sources simultaneously found across multiple datasets, e.g., electroencephalogram (EEG) and kinematic data jointly recorded during reaching movements. Existing JBSS approaches are designed to handle multidimensional datasets, yet to our knowledge, there is no existing means to examine common components that may be found across a unidimensional dataset and a multidimensional one. In this paper, we propose a simple, yet effective method to achieve the goal of JBSS when concurrent multidimensional EEG and unidimensional kinematic datasets are available, by combining ensemble empirical mode decomposition (EEMD) with independent vector analysis (IVA). We demonstrate the performance of the proposed method through numerical simulations and application to data collected from reaching movements in Parkinson’s disease. The proposed method is a promising JBSS tool for real-world biomedical signal processing applications. [ABSTRACT FROM PUBLISHER]

Brain computer interfaces (BCIs) offer a broad class of neurologically impaired individuals an alternative means to interact with the environment. Many BCIs are “synchronous” systems, in which the system sets the timing of the interaction and tries to infer what control command the subject is issuing at each prompting. In contrast, in “asynchronous” BCIs subjects pace the interaction and the system must determine when the subject’s control command occurs. In this paper we propose a new idea for BCI which draws upon the strengths of both approaches. The subjects are externally paced and the BCI is able to determine when control commands are issued by decoding the subject’s intention for initiating control in dedicated time slots. A single task with randomly interleaved trials was designed to test whether it can be used as stimulus for inducing initiation and non-initiation states when the sensory and motor requirements for the two types of trials are very nearly identical. Further, the essential problem on the discrimination between initiation state and non-initiation state was studied. We tested the ability of EEG spectral power to distinguish between these two states. Among the four standard EEG frequency bands, beta band power recorded over parietal-occipital cortices provided the best performance, achieving an average accuracy of 86% for the correct classification of initiation and non-initiation states. Moreover, delta band power recorded over parietal and motor areas yielded a good performance and thus could also be used as an alternative feature to discriminate these two mental states. The results demonstrate the viability of our proposed idea for a BCI design based on conventional EEG features. Our proposal offers the potential to mitigate the signal detection challenges of fully asynchronous BCIs, while providing greater flexibility to the subject than traditional synchronous BCIs. [ABSTRACT FROM AUTHOR]

The perception of others' actions supports important skills such as communication, intention understanding, and empathy. Are mechanisms of action processing in the human brain specifically tuned to process biological agents? Humanoid robots can perform recognizable actions, but can look and move differently from humans, and as such, can be used in experiments to address such questions. Here, we recorded EEG as participants viewed actions performed by three agents. In the Human condition, the agent had biological appearance and motion. The other two conditions featured a state-of-the-art robot in two different appearances: Android, which had biological appearance but mechanical motion, and Robot, which had mechanical appearance and motion.We explored whether sensorimotor mu (8-13 Hz) and frontal theta (4-8 Hz) activity exhibited selectivity for biological entities, in particular for whether the visual appearance and/or the motion of the observed agent was biological. Sensorimotor mu suppression has been linked to the motor simulation aspect of action processing (and the human mirror neuron system, MNS), and frontal theta to semantic and memory-related aspects. For all three agents, action observation induced significant attenuation in the power of mu oscillations, with no difference between agents. Thus, mu suppression, considered an index of MNS activity, does not appear to be selective for biological agents. Observation of the Robot resulted in greater frontal theta activity compared to the Android and the Human, whereas the latter two did not differ from each other. Frontal theta thus appears to be sensitive to visual appearance, suggesting agents that are not sufficiently biological in appearance may result in greater memory processing demands for the observer. Studies combining robotics and neuroscience such as this one can allow us to explore neural basis of action processing on the one hand, and inform the design of social robots on the other. [ABSTRACT FROM AUTHOR]

Virtual reality (VR) allows precise control and manipulation of rich, dynamic stimuli that, when coupled with on-line motion capture and neural monitoring, can provide a powerful means both of understanding brain behavioral relations in the high dimensional world and of assessing and treating a variety of neural disorders. Here we present a system that combines state-of-the-art, fully immersive, 3D, multi-modal VR with temporally aligned electroencephalographic (EEG) recordings. The VR system is dynamic and interactive across visual, auditory, and haptic interactions, providing sight, sound, touch, and force. Crucially, it does so with simultaneous EEG recordings while subjects actively move about a 20\times 20\ ft^2 space. The overall end-to-end latency between real movement and its simulated movement in the VR is approximately 40 ms. Spatial precision of the various devices is on the order of millimeters. The temporal alignment with the neural recordings is accurate to within approximately 1 ms. This powerful combination of systems opens up a new window into brain-behavioral relations and a new means of assessment and rehabilitation of individuals with motor and other disorders. [ABSTRACT FROM PUBLISHER]

Spatial representations and walking speed in rodents are consistently related to the phase, frequency, and/or amplitude of θ rhythms in hippocampal local field potentials. However, neuropsychological studies in humans have emphasized the importance of parietal cortex for spatial navigation, and efforts to identify the electrophysiological signs of spatial navigation in humans have been stymied by the difficulty of recording during free exploration of complex environments. We resolved the recording problem and experimentally probed brain activity of human participants who were fully ambulant. On each of 2 d, electroencephalography was synchronized with head and body movement in 13 subjects freely navigating an extended virtual environment containing numerous unique objects. θ phase and amplitude recorded over parietal cortex were consistent when subjects walked through a particular spatial separation at widely separated times. This spatial displacement θ autocorrelation (STAcc) was quantified and found to be significant from 2 to 8 Hz within the environment. Similar autocorrelation analyses performed on an electrooculographic channel, used to measure eye movements, showed no significant spatial autocorrelations, ruling out eye movements as the source of STAcc. Strikingly, the strength of an individual's STAcc maps from day 1 significantly predicted object location recall success on day 2. θ was also significantly correlated with walking speed; however, this correlation appeared unrelated to STAcc and did not predict memory performance. This is the first demonstration of memory-related, spatial maps in humans generated during active spatial exploration. [ABSTRACT FROM AUTHOR]

A personal narrative which explores the author's experience with rehabilitation robotics is presented.

Development and application of virtual reality (VR) systems for rehabilitation is an iterative process produced by collaboration of an inter-disciplinary team of engineers, neuroscientists and clinician-scientists. In this paper the use of virtual reality technology for the rehabilitation of individuals post-stroke is described. The development of the hardware is based on principles of motor control. Development of the software uses findings from the enrichment and motor plasticity and training literatures as well as principles of motor learning. Virtual environments are created to afford individuals post-stroke opportunities to practice tasks for which they require rehabilitation. These tasks, related to hand function and gait, are trained both at the impairment and functional level. The training engages users to allow for the repetitive intensive practice required for behavioral motor plasticity. Results from a series of upper and lower extremity studies indicate that use of VR technology to augment rehabilitation of individuals post-stroke merits further study. [ABSTRACT FROM AUTHOR]

Discusses the development of a personal computer-based desktop virtual reality system for rehabilitating hand function in stroke patients. Use of traditional therapeutic interventions in rehabilitation; Effect recovery of functional motor skills; Structure of the current health care system.

Recent research indicated that patients with Parkinson's disease (PD) have elevated thresholds for detecting proprioceptive stimuli. What is not known is whether the integration of proprioceptive information across multiple joints, a process that forms the basis of limb perception, is impaired in PD. This study examined how subjects perceive the curvature of their own hand trajectories--a task relying on the integration of proprioceptive information across multiple joints. Without vision, patients with mild-to-moderate PD (N = 11) moved the handle of a two-joint robotic manipulandum that created force fields resembling a "virtual box." The left wall of the virtual box was curved concave or convex and subjects had to judge its curvature using a forced-choice paradigm. Subjects either moved their hands actively within the box or the robot moved their hands passively. One virtual box was located the right hemispace while a second box was in the left hemispace. The right hemifield box required a larger degree of multijoint movement to explore hand curvature than the left hemifield box. After each trial, the curvature of the wall was adjusted using an adaptive staircase algorithm to obtain individual subject thresholds for linearity. We found that patient performance fell into two subgroups: a more impaired PD group and a less impaired PD group. The more impaired PD group had significantly lower sensitivity to curvature than the control group (p = 0.001). Curvature sensitivity in the right hemifield was significantly lower in all groups when compared with the left hemifield, with the more impaired group showing the lowest sensitivity of all groups. Active movement did not aid PD patients to overcome the deficits in sensitivity. Our study provides evidence that multijoint integration of proprioceptive signals is compromised in PD. It adds to a growing body of literature indicating that PD impairs kinesthesia. [ABSTRACT FROM AUTHOR]

Focuses on the morphological processes of the American Sign Language. Assessment of the basic lexical units; Rearrangement of sounds to form a variety of words; Reflection of the temporal patterning in inflectional process.