Journal: IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Event related potentials (ERPs) represent a noninvasive and widely available means to analyze neural correlates of sensory and cognitive processing. Recent developments in neural and cognitive engineering proposed completely new application fields of this well-established measurement technique when using an advanced single-trial processing. We have recently shown that 2-D diffusion filtering methods from image processing can be used for the denoising of ERP single-trials in matrix representations, also called ERP images. In contrast to conventional 1-D transient ERP denoising techniques, the 2-D restoration of ERP images allows for an integration of regularities over multiple stimulations into the denoising process. Advanced anisotropic image restoration methods may require directional information for the ERP denoising process. This is especially true if there is a lack of a priori knowledge about possible traces in ERP images. However due to the use of event related experimental paradigms, ERP images are characterized by a high degree of self-similarity over the individual trials. In this paper, we propose the simple and easy to apply nonlocal means method for ERP image denoising in order to exploit this self-similarity rather than focusing on the edge-based extraction of directional information. Using measured and simulated ERP data, we compare our method to conventional approaches in ERP denoising. It is concluded that the self-similarity in ERP images can be exploited for single-trial ERP denoising by the proposed approach. This method might be promising for a variety of evoked and event-related potential applications, including nonstationary paradigms such as changing exogeneous stimulus characteristics or endogenous states during the experiment. As presented, the proposed approach is for the a posteriori denoising of single-trial sequences.
A new form of augmentative and alternative communication (AAC) device for people with severe speech impairment-the voice-input voice-output communication aid (VIVOCA)-is described. The VIVOCA recognizes the disordered speech of the user and builds messages, which are converted into synthetic speech. System development was carried out employing user-centered design and development methods, which identified and refined key requirements for the device. A novel methodology for building small vocabulary, speaker-dependent automatic speech recognizers with reduced amounts of training data, was applied. Experiments showed that this method is successful in generating good recognition performance (mean accuracy 96%) on highly disordered speech, even when recognition perplexity is increased. The selected message-building technique traded off various factors including speed of message construction and range of available message outputs. The VIVOCA was evaluated in a field trial by individuals with moderate to severe dysarthria and confirmed that they can make use of the device to produce intelligible speech output from disordered speech input. The trial highlighted some issues which limit the performance and usability of the device when applied in real usage situations, with mean recognition accuracy of 67% in these circumstances. These limitations will be addressed in future work.
Following two decades of design and clinical research on robot-mediated therapy for the shoulder and elbow, therapeutic robotic devices for other joints are being proposed: several research groups including ours have designed robots for the wrist, either to be used as stand-alone devices or in conjunction with shoulder and elbow devices. However, in contrast with robots for the shoulder and elbow which were able to take advantage of descriptive kinematic models developed in neuroscience for the past 30 years, design of wrist robots controllers cannot rely on similar prior-art: wrist movement kinematics has been largely unexplored. This study aimed at examining speed profiles of fast, visuallyevoked, visually-guided, target-directed human wrist pointing movements. Thirteen hundred ninety-eight (1398) trials were recorded from seven unimpaired subjects who performed centerout flexion/extension and abduction/adduction wrist movements and fitted with nineteen models previously proposed for describing reaching speed profiles. A nonlinear, least-squares optimization procedure extracted parameters sets that minimized error between experimental and reconstructed data. Models performances were compared based on their ability to reconstruct experimental data. Results suggest that the support-bounded lognormal is the best model for speed profiles of fast, wrist pointing movements. Applications include design of control algorithms for therapeutic wrist robots and quantitative metrics of motor recovery.
This study aims to design a steady state visual evoked potentials (SSVEP) based brain-computer interface (BCI) system with only three electrodes. It is known that low frequency flickering induces more intensive SSVEP, but might cause users feel uncomfortable and easily tired. Therefore, this paper proposes a novel middle/high frequency flickering stimulus. However, users show different SSVEP responses when gazing at the same stimuli. It is improper to design fixed frequency flickering stimuli for all users. This study firstly proposes a strategy to adjust the stimuli frequency for each user that could cause better SSVEP. Moreover, to further enhance the SSVEP, this study incorporates flickering duty-cycle for stimuli design, which has been discussed less for SSVEP-based BCI systems. The proposed system consists of two modes, flicker frequency/duty-cycle selection mode and application mode. The flicker frequency/duty-cycle selection mode obtains two best frequencies between 24_Hz and 36_Hz with their related optimal duty-cycle. Then the system goes into the application mode to control the devices. A new fact that has been found is that the optimal flicker frequency and duty-cycle do not vary with time. It means once the optical flicker frequency and duty-cycle is determined the first time, flicker frequency/duty-cycle selection mode does not need to operate the next time. Furthermore, the phase coding technology is used to extend the one command/one frequency to multi command/one frequency. Experimental results show the proposed system has good performance with average accuracy 95% and average command transfer interval (CTI) 4.4925 seconds per command.
Cervical © and ocular (o) vestibular evoked myogenic potentials (VEMPs) provide important tools for measuring otolith function. However, two major drawbacks of this method are encountered in clinical practice. First, recording of oVEMPs is compromised by small n10 amplitudes. Second, VEMP analysis is currently based on the averaging technique, resulting in a loss of information compared to single sweep analysis. Here, we (1) developed a novel electromotive trigger mechanism for evoking VEMPs by bone-conducted vibration to the forehead and (2) established maximum entropy extraction of complex wavelet transforms for calculation of phase synchronization between VEMP single sweeps. Both c- and oVEMPs were recorded for n=10 healthy individuals. The oVEMP n10 amplitude was consistently higher (right: 24:849:71 V ; left: 27:4014:55 V ) than previously described. Stable VEMP signals were reached after a smaller number of head taps (oVEMPs < 6; cVEMPs < 11) compared to current recommendations. Phase synchronization vectors and phase shift values were successfully determined for simulated and clinically recorded VEMPs, providing information about the impact of noise and phase jitter on the VEMP signal. Thus, the proposed method constitutes an easy-to-use approach for the fast detection and analysis of VEMPs in clinical practice.
Co-registration of transcranial magnetic stimulation (TMS) and electroencephalography (EEG) is a new, promising method for assessing cortical excitability and connectivity. Using this technique, a TMS evoked potential (TEP) can be induced and registered with the EEG. However, the TEP contains an early, short lasting artifact due to the magnetic pulse, and a second artifact, which depends on the location of stimulation and can last up to 40 milliseconds. Different causes for this second artifact have been suggested in literature. In this study, we used principal component analysis (PCA) to suppress both the first and second artifact in TMS-EEG data. Single pulse TMS was applied at the motor and visual cortex in 18 healthy subjects. PCA using singular value decomposition was applied on single trials to suppress the artifactual components. A large artifact suppression was realized after the removal of the first 5 PCA components, thereby revealing early TEP peaks, with only a small suppression of later TEP components. The spatial distribution of the second artifact suggests that it is caused by electrode movement due to activation of the temporal musculature. In conclusion, we showed that PCA can be used to reduce TMS-induced artifacts in EEG, thereby revealing components of the TMS evoked potential.
Electrical vagus nerve stimulation is a treatment alternative for many epileptic and depressed patients whose symptoms are not well managed with pharmaceutical therapy. However, the fixed stimulus, open loop dosing mechanism limits its efficacy and precludes major advances in the quality of therapy. A real-time, responsive form of vagus nerve stimulation is needed to control nerve activation according to therapeutic need. This personalized approach to therapy will improve efficacy and reduce the number and severity of side effects. We present autonomous neural control, a responsive, biofeedbackdriven approach that uses the degree of measured nerve activation to control stimulus delivery. We demonstrate autonomous neural control in rats, showing that it rapidly learns how to most efficiently activate any desired proportion of vagal A, B, and/or C fibers over time. This system will maximize efficacy by minimizing patient response variability and by minimizing therapeutic failures resulting from longitudinal decreases in nerve activation with increasing durations of treatment. The value of autonomous neural control equally applies to other applications of electrical nerve stimulation.
Characterizing brain dynamics during anesthesia is a main current challenge in anesthesia study. Several single channel Electroencephalogram (EEG) -based commercial monitors like the Bispectral index (BIS) have suggested to examine EEG signal. But, the BIS index has obtained numerous critiques. In this study, we evaluate the concentration-dependent effect of the propofol on long-range frontal-temporal synchronization of EEG signals collected from eight subjects during a controlled induction and recovery design. We used order patterns cross recurrence plot and provide an index named order pattern laminarity (OPL) to assess changes in neuronal synchronization as the mechanism forming the foundation of conscious perception. The prediction probability of 0.9 and 0.84 for OPL and BIS specified that the OPL index correlated more strongly with effect-site propofol concentration. Also, our new index makes faster reaction to transients in EEG recordings based on pharmacokinetic and pharmacodynamic model parameters and demonstrates less variability at the point of loss of consciousness (standard deviation of 0.04 for OPL compared with 0.09 for BIS index). The result show that the OPL index can estimate anesthetic state of patient more efficiently than the BIS index in lightly sedated state with more tolerant of artifacts.
An inability to adapt myoelectric interfaces to a novel user’s unique style of hand motion, or even to adapt to the motion style of an opposite limb upon which the interface is trained, are important factors inhibiting the practical application of myoelectric interfaces. This is mainly attributed to the individual differences in the exhibited electromyogram (EMG) signals generated by the muscles of different limbs. We propose in this paper a multiuser myoelectric interface which easily adapts to novel users and maintains good movement recognition performance. The main contribution is a framework for implementing style-independent feature transformation by using canonical correlation analysis (CCA) in which different users' data is projected onto a unified-style space. The proposed idea is summarized into three steps: 1) train a myoelectric pattern classifier on the set of style-independent features extracted from multiple users using the proposed CCA-based mapping; 2) create a new set of features describing the movements of a novel user during a quick calibration session; and 3) project the novel user’s features onto a lower dimensional unified-style space with features maximally correlated with training data and classify accordingly. The proposed method has been validated on a set of eight intact-limbed subjects, left-and-right handed, performing ten classes of bilateral synchronous fingers movements with four electrodes on each forearm. The method was able to overcome individual differences through the style-independent framework with accuracies of > 83% across multiple users. Testing was also performed on a set of ten intact-limbed and six below-elbow amputee subjects as they performed finger and thumb movements. The proposed framework allowed us to train the classifier on a normal subject’s data while subsequently testing it on an amputee’s data after calibration with a performance of > 82% on average across all amputees.
In recent years, intelligent prosthetic knees have been developed that enable amputees to walk as normally as possible when compared to healthy subjects. Although semi-active prosthetic knees utilizing magnetorheological (MR) dampers offer several advantages, they lack the ability to generate active force that is required during some states of a normal gait cycle. This prevents semi-active knees from achieving the same level of performance as active devices. In this work, a new control algorithm for a semi-active prosthetic knee during the swing phase is proposed to reduce this gap. The controller uses neural network predictive control and particle swarm optimization to calculate suitable command signals. Simulation results using a double pendulum model show that the generated knee trajectory of the proposed controller is more similar to the normal gait than previous open-loop controllers at various ambulation speed. Moreover, the investigation shows that the algorithm can be calculated in real-time by an embedded system, allowing for easy implementation on real prosthetic knees.