[Show abstract][Hide abstract] ABSTRACT: Objective
Stroke is a leading cause of long-term motor disability. Stroke patients with severe hand weakness do not profit from rehabilitative treatments. Recently, brain-controlled robotics and sequential functional electrical stimulation allowed some improvement. However, for such therapies to succeed, it is required to decode patients' intentions for different arm movements. Here, we evaluated whether residual muscle activity could be used to predict movements from paralyzed joints in severely impaired chronic stroke patients.
Muscle activity was recorded with surface-electromyography (EMG) in 41 patients, with severe hand weakness (Fugl-Meyer Assessment [FMA] hand subscores of 2.93 ± 2.7), in order to decode their intention to perform six different motions of the affected arm, required for voluntary muscle activity and to control neuroprostheses. Decoding of paretic and nonparetic muscle activity was performed using a feed-forward neural network classifier. The contribution of each muscle to the intended movement was determined.
Decoding of up to six arm movements was accurate (>65%) in more than 97% of nonparetic and 46% of paretic muscles.
These results demonstrate that some level of neuronal innervation to the paretic muscle remains preserved and can be used to implement neurorehabilitative treatments in 46% of patients with severe paralysis and extensive cortical and/or subcortical lesions. Such decoding may allow these patients for the first time after stroke to control different motions of arm prostheses through muscle-triggered rehabilitative treatments.
Annals of Clinical and Translational Neurology. 12/2015; 7.
[Show abstract][Hide abstract] ABSTRACT: The loss of hand function can result in severe physical and psychosocial impairment. Thus, compensation of a lost hand function using assistive robotics that can be operated in daily life is very desirable. However, versatile, intuitive, and reliable control of assistive robotics is still an unsolved challenge. Here, we introduce a novel brain/neural-computer interaction (BNCI) system that integrates electroencephalography (EEG) and electrooculography (EOG) to improve control of assistive robotics in daily life environments. To evaluate the applicability and performance of this hybrid approach, five healthy volunteers (HV) (four men, average age 26.5±3.8 years) and a 34-year-old patient with complete finger paralysis due to a brachial plexus injury (BPI) used EEG (condition 1) and EEG/EOG (condition 2) to control grasping motions of a hand exoskeleton. All participants were able to control the BNCI system (BNCI control performance HV: 70.24±16.71%, BPI: 65.93±24.27%), but inclusion of EOG significantly improved performance across all participants (HV: 80.65±11.28, BPI: 76.03±18.32%). This suggests that hybrid BNCI systems can achieve substantially better control over assistive devices, e.g., a hand exoskeleton, than systems using brain signals alone and thus may increase applicability of brain-controlled assistive devices in daily life environments.
[Show abstract][Hide abstract] ABSTRACT: Introduction: Different techniques for neurofeedback of voluntary brain activations are currently being explored for clinical application in brain disorders. One of the most frequently used approaches is the self-regulation of oscillatory signals recorded with electroencephalography (EEG). Many patients are, however, unable to achieve sufficient voluntary control of brain activity. This could be due to the specific anatomical and physiological changes of the patient's brain after the lesion, as well as to methodological issues related to the technique chosen for recording brain signals. Methods: A patient with an extended ischemic lesion of the cortex did not gain volitional control of sensorimotor oscillations when using a standard EEG-based approach. We provided him with neurofeedback of his brain activity from the epidural space by electrocorticography (ECoG). Results: Ipsilesional epidural recordings of field potentials facilitated self-regulation of brain oscillations in an online closed-loop paradigm and allowed reliable neurofeedback training for a period of 4 weeks. Conclusion: Epidural implants may decode and train brain activity even when the cortical physiology is distorted following severe brain injury. Such practice would allow for reinforcement learning of preserved neural networks and may well provide restorative tools for those patients who are severely afflicted.
Frontiers in Behavioral Neuroscience 12/2014; · 4.16 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: The combination of Brain-Computer Interface (BCI) technology, allowing online monitoring and decoding of brain activity, with virtual and mixed reality (MR) systems may help to shape and guide implicit and explicit learning using ecological scenarios. Real-time information of ongoing brain states acquired through BCI might be exploited for controlling data presentation in virtual environments. Brain states discrimination during mixed reality experience is thus critical for adapting specific data features to contingent brain activity. In this study we recorded electroencephalographic (EEG) data while participants experienced MR scenarios implemented through the eXperience Induction Machine (XIM). The XIM is a novel framework modeling the integration of a sensing system that evaluates and measures physiological and psychological states with a number of actuators and effectors that coherently reacts to the user's actions. We then assessed continuous EEG-based discrimination of spatial navigation, reading and calculation performed in MR, using linear discriminant analysis (LDA) and support vector machine (SVM) classifiers. Dynamic single trial classification showed high accuracy of LDA and SVM classifiers in detecting multiple brain states as well as in differentiating between high and low mental workload, using a 5 s time-window shifting every 200 ms. Our results indicate overall better performance of LDA with respect to SVM and suggest applicability of our approach in a BCI-controlled MR scenario. Ultimately, successful prediction of brain states might be used to drive adaptation of data representation in order to boost information processing in MR.
Frontiers in Behavioral Neuroscience 11/2014; 8(415). · 4.16 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Objective. Recently, there have been several approaches to utilize a brain–computer interface (BCI) for rehabilitation with stroke patients or as an assistive device for the paralyzed. In this study we investigated whether up to seven different hand movement intentions can be decoded from epidural electrocorticography (ECoG) in chronic stroke patients. Approach. In a screening session we recorded epidural ECoG data over the ipsilesional motor cortex from four chronic stroke patients who had no residual hand movement. Data was analyzed offline using a support vector machine (SVM) to decode different movement intentions. Main results. We showed that up to seven hand movement intentions can be decoded with an average accuracy of 61% (chance level 15.6%). When reducing the number of classes, average accuracies up to 88% can be achieved for decoding three different movement intentions. Significance. The findings suggest that ipsilesional epidural ECoG can be used as a viable control signal for BCI-driven neuroprosthesis. Although patients showed no sign of residual hand movement, brain activity at the ipsilesional motor cortex still shows enough intention-related activity to decode different movement intentions with sufficient accuracy. S Online supplementary data available from stacks.iop.org/jne/11/066008/mmedia Keywords: electrocorticography (ECOG), stroke, brain-computer interface (BCI) (Some figures may appear in colour only in the online journal)
Journal of Neural Engineering 10/2014; 11(6). · 3.42 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Electroencephalography (EEG) often fails to assess both the level (i.e., arousal) and the content (i.e., awareness) of pathologically altered consciousness in patients without motor responsiveness. This might be related to a decline of awareness, to episodes of low arousal and disturbed sleep patterns, and/or to distorting and attenuating effects of the skull and intermediate tissue on the recorded brain signals. Novel approaches are required to overcome these limitations. We introduced epidural electrocorticography (ECoG) for monitoring of cortical physiology in a late-stage amytrophic lateral sclerosis patient in completely locked-in state (CLIS). Despite long-term application for a period of six months, no implant-related complications occurred. Recordings from the left frontal cortex were sufficient to identify three arousal states. Spectral analysis of the intrinsic oscillatory activity enabled us to extract state-dependent dominant frequencies at <4, ~7 and ~20 Hz, representing sleep-like periods, and phases of low and elevated arousal, respectively. In the absence of other biomarkers, ECoG proved to be a reliable tool for monitoring circadian rhythmicity, i.e., avoiding interference with the patient when he was sleeping and exploiting time windows of responsiveness. Moreover, the effects of interventions addressing the patient's arousal, e.g., amantadine medication, could be evaluated objectively on the basis of physiological markers, even in the absence of behavioral parameters. Epidural ECoG constitutes a feasible trade-off between surgical risk and quality of recorded brain signals to gain information on the patient's present level of arousal. This approach enables us to optimize the timing of interactions and medical interventions, all of which should take place when the patient is in a phase of high arousal. Furthermore, avoiding low-responsiveness periods will facilitate measures to implement alternative communication pathways involving brain-computer interfaces (BCI).
Frontiers in Human Neuroscience 10/2014; 8:861. · 2.90 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: This pilot study aimed to explore whether criminal psychopaths can learn volitional regulation of the left anterior insula with real-time fMRI neurofeedback. Our previous studies with healthy volunteers showed that learned control of the blood oxygenation-level dependent (BOLD) signal was specific to the target region, and not a result of general arousal and global unspecific brain activation, and also that successful regulation modulates emotional responses, specifically to aversive picture stimuli but not neutral stimuli. In this pilot study, four criminal psychopaths were trained to regulate the anterior insula by employing negative emotional imageries taken from previous episodes in their lives, in conjunction with contingent feedback. Only one out of the four participants learned to increase the percent differential BOLD in the up-regulation condition across training runs. Subjects with higher Psychopathic Checklist-Revised (PCL:SV) scores were less able to increase the BOLD signal in the anterior insula than their lower PCL:SV counterparts. We investigated functional connectivity changes in the emotional network due to learned regulation of the successful participant, by employing multivariate Granger Causality Modeling (GCM). Learning to up-regulate the left anterior insula not only increased the number of connections (causal density) in the emotional network in the single successful participant but also increased the difference between the number of outgoing and incoming connections (causal flow) of the left insula. This pilot study shows modest potential for training psychopathic individuals to learn to control brain activity in the anterior insula.
Frontiers in Behavioral Neuroscience 10/2014; 8:344. · 4.16 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: This study contrasted the neurological correlates of calendar calculating (CC) between those individuals with autism spectrum disorder (ASD) and typically developing individuals. CC is the ability to correctly and quickly state the day of the week of a given date. Using magnetoencephalography (MEG), we presented 126 calendar tasks with dates of the present, past, and future. Event-related magnetic fields (ERF) of 3000 ms duration and brain activation patterns were compared in three savant calendar calculators with ASD (ASDCC) and three typically developing calendar calculators (TYPCC). ASDCC outperformed TYPCC in correct responses, but not in answering speed. Comparing amplitudes of their ERFs, there was a main effect of group between 1000 and 3000 ms, but no further effects of hemisphere or sensor location. We conducted CLARA source analysis across the entire CC period in each individual. Both ASDCC and TYPCC exhibited activation maxima in prefrontal areas including the insulae and the left superior temporal gyrus. This is in accordance with verbal fact retrieval and working memory as well as monitoring and coordination processes. In ASDCC, additional activation sites at the right superior occipital gyrus, the right precuneus, and the right putamen point to visual-spatial strategies and are in line with the preference of autistic individuals for engaging posterior regions relatively more strongly in various reasoning and problem solving tasks.
Brain and Cognition 10/2014; 90:157–164. · 2.68 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Background: Recent experimental evidence has indicated that the motor system coordinates muscle activations through a linear combination of muscle synergies that are specified at the spinal or brainstem networks level. After stroke upper limb impairment is characterized by abnormal patterns of muscle activations or synergies.
Objective: This study aimed at characterizing the muscle synergies in severely affected chronic stroke patients. Furthermore, the influence of integrity of the sensorimotor cortex on synergy modularity and its relation with motor impairment was evaluated.
Methods: Surface electromyography from 33 severely impaired chronic stroke patients was recorded during 6 bilateral movements. Muscle synergies were extracted and synergy patterns were correlated with motor impairment scales.
Results: Muscle synergies extracted revealed different physiological patterns dependent on the preservation of the sensorimotor cortex. Patients without intact sensorimotor cortex showed a high preservation of muscle synergies. On the contrary, patients with intact sensorimotor cortex showed poorer muscle synergies preservation and an increase in new generated synergies. Furthermore, the preservation of muscle synergies correlated positively with hand functionality in patients with intact sensorimotor cortex and subcortical lesions only.
Conclusion: Our results indicate that severely paralyzed chronic stroke patient with intact sensorimotor cortex might sculpt new synergy patterns as a response to maladaptive compensatory strategies.
Frontiers in Human Neuroscience 09/2014; 8:744. · 2.90 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: The aim of this review is to provide a critical overview of recent research in the field of neuroscientific and clinical application of real-time functional MRI neurofeedback (rtfMRI-nf).
[Show abstract][Hide abstract] ABSTRACT: In this paper, we present an experimental approach to design systems sensitive to emotion. We describe a system for the detection of emotional states based on physiological signals and an application use case utilizing the detected emotional state. The application is an emotion management system to be used for the support in the improvement of life conditions of users suffering from cerebral palsy (CP). The system presented here combines effectively biofeedback sensors and a set of software algorithms to detect the current emotional state of the user and to react to them appropriately.
Computers Helping People with Special Needs, Paris France; 07/2014
[Show abstract][Hide abstract] ABSTRACT: In amyotrophic lateral sclerosis (ALS), cognition is affected. Cortical atrophy in frontal and temporal areas has been associated with the cognitive profile of patients. Additionally, reduced metabolic turnover and regional cerebral blood flow in frontal areas indicative of reduced neural activity have been reported for ALS. We hypothesize that functional connectivity in non-task associated functional default mode network (DMN) is associated with cognitive profile and white matter integrity. This study focused on specific cognitive tasks known to be impaired in ALS such as verbal fluency and attention, and the relationship with functional connectivity in the DMN and white matter integrity. Nine patients and 11 controls were measured with an extensive neuropsychological battery. Resting-state functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI) data were acquired. Results showed that ALS patients performed significantly worse in attention and verbal fluency task. Patients showed increased functional connectivity in parahippocampal and parietal areas of the non-task associated DMN compared to controls. The more pronounced the cognitive deficits, the stronger the increase in functional connectivity in those areas. White matter integrity was reduced in frontal areas in the patients. In conclusion, increased connectivity in the DMN in parahippocampal and parietal areas might represent recruitment of accessory brain regions to compensate for dysfunctional frontal networks.
[Show abstract][Hide abstract] ABSTRACT: Introduction: Prostheses for upper-limb amputees are currently controlled by either myoelectric or peripheral neural signals. Performance and dexterity of these devices is still limited, particularly when it comes to controlling hand function. Movement-related brain activity might serve as a complementary bio-signal for motor control of hand prosthesis. Methods: We introduced a methodology to implant a cortical interface without direct exposure of the brain surface in an upper-limb amputee. This bi-directional interface enabled us to explore the cortical physiology following long-term transhumeral amputation. In addition, we investigated neurofeedback of electrocorticographic brain activity related to the patient's motor imagery to open his missing hand, i.e., phantom hand movement, for real-time control of a virtual hand prosthesis. Results: Both event-related brain activity and cortical stimulation revealed mutually overlapping cortical representations of the phantom hand. Phantom hand movements could be robustly classified and the patient required only three training sessions to gain reliable control of the virtual hand prosthesis in an online closed-loop paradigm that discriminated between hand opening and rest. Conclusion: Epidural implants may constitute a powerful and safe alternative communication pathway between the brain and external devices for upper-limb amputees, thereby facilitating the integrated use of different signal sources for more intuitive and specific control of multi-functional devices in clinical use.
Frontiers in Human Neuroscience 05/2014; 8:285. · 2.90 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Amyotrophic lateral sclerosis (ALS) can result in the locked-in state (LIS), characterized by paralysis, and eventual respiratory failure, compensated by artificial ventilation,(1) or the completely LIS (CLIS), with additional total paralysis of eye muscles. Brain-computer interfaces (BCIs) have been used to allow paralyzed people to regain basic communication,(2) although current EEG-based BCIs have not succeeded with CLIS patients.(3) We present Class IV case evidence to establish that communication in the CLIS is possible with a metabolic BCI based on near-infrared spectroscopy (NIRS).
[Show abstract][Hide abstract] ABSTRACT: Brain-Machine Interfaces (BMI) allow manipulation of external devices and computers directly with brain activity without involvement of overt motor actions. The neurophysiological principles of such robotic brain devices and BMIs follow Hebbian learning rules as described and realized by Valentino Braitenberg in his book "Vehicles," in the concept of a "thought pump" residing in subcortical basal ganglia structures. We describe here the application of BMIs for brain communication in totally locked-in patients and argue that the thought pump may extinguish-at least partially-in those people because of extinction of instrumentally learned cognitive responses and brain responses. We show that Pavlovian semantic conditioning may allow brain communication even in the completely paralyzed who does not show response-effect contingencies. Principles of skill learning and habit acquisition as formulated by Braitenberg are the building blocks of BMIs and neuroprostheses.
[Show abstract][Hide abstract] ABSTRACT: Objective: Transcranial direct current stimulation (tDCS) improves motor learning and can affect emotional processing and attention. However, it is unclear whether learned electroencephalography (EEG)-based brain-machine interface (BMI) control during tDCS is feasible, how application of transcranial electric currents during BMI control would interfere with feature-extraction of physiological brain signals and how it affects brain control performance. Here we tested this combination and evaluated stimulation-dependent artifacts across different EEG frequencies and stability of motor imagery-based BMI control. Approach: Ten healthy volunteers were invited to two BMI-sessions, each comprising two 60-trial blocks. During the trials, learned desynchronization of mu-rhythms (8-15 Hz) associated with motor imagery (MI) recorded over C4 was translated into online cursor movements on a computer screen. During block 2, either sham (session A) or anodal tDCS (session B) was applied at 1 mA with the stimulation electrode placed 1 cm anterior of C4. Main results: tDCS was associated with a significant signal power increase in the lower frequencies most evident in the signal spectrum of the EEG channel closest to the stimulation electrode. Stimulation-dependent signal power increase exhibited a decay of 12 dB per decade, leaving frequencies above 9 Hz unaffected. Analysis of BMI control performance did not indicate a difference between blocks and tDCS conditions. Conclusion: Application of tDCS during learned EEG-based self-regulation of brain oscillations above 9 Hz is feasible and safe, and might improve applicability of BMI systems.
Frontiers in Behavioral Neuroscience 03/2014; 8:93. · 4.16 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Sensorimotor rhythms (SMR, 8-15 Hz) are brain oscillations associated with successful motor performance, imagery, and imitation. Voluntary modulation of SMR can be used to control brain-machine interfaces (BMI) in the absence of any physical movements. The mechanisms underlying acquisition of such skill are unknown. Here, we provide evidence for a causal link between function of the primary motor cortex (M1), active during motor skill learning and retention, and successful acquisition of abstract skills such as control over SMR. Thirty healthy participants were trained on 5 consecutive days to control SMR oscillations. Each participant was randomly assigned to one of 3 groups that received either 20 min of anodal, cathodal, or sham transcranial direct current stimulation (tDCS) over M1. Learning SMR control across training days was superior in the anodal tDCS group relative to the other 2. Cathodal tDCS blocked the beneficial effects of training, as evidenced with sham tDCS. One month later, the newly acquired skill remained superior in the anodal tDCS group. Thus, application of weak electric currents of opposite polarities over M1 differentially modulates learning SMR control, pointing to this primary cortical region as a common substrate for acquisition of physical motor skills and learning to control brain oscillatory activity.