Palmar Grasps Research Articles

Non-Linear Adapted Spatio-Temporal Filter for Single-Trial Identification of Movement-Related Cortical Potential

The execution or imagination of a movement is reflected by a cortical potential that can be recorded by electroencephalography (EEG) as Movement-Related Cortical Potentials (MRCPs). The identification of MRCP from a single trial is a challenging possibility to get a natural control of a Brain–Computer Interface (BCI). We propose a novel method for MRCP detection based on optimal non-linear filters, processing different channels of EEG including delayed samples (getting a spatio-temporal filter). Different outputs can be obtained by changing the order of the temporal filter and of the non-linear processing of the input data. The classification performances of these filters are assessed by cross-validation on a training set, selecting the best ones (adapted to the user) and performing a majority voting from the best three to get an output using test data. The method is compared to another state-of-the-art filter recently introduced by our group when applied to EEG data recorded from 16 healthy subjects either executing or imagining 50 self-paced upper-limb palmar grasps. The new approach has a median accuracy on the overall dataset of 80%, which is significantly better than that of the previous filter (i.e., 63%). It is feasible for online BCI system design with asynchronous, self-paced applications.

Decoding hand movements from human EEG to control a robotic arm in a simulation environment

Objective. Daily life tasks can become a significant challenge for motor impaired persons. Depending on the severity of their impairment, they require more complex solutions to retain an independent life. Brain-computer interfaces (BCIs) are targeted to provide an intuitive form of control for advanced assistive devices such as robotic arms or neuroprostheses. In our current study we aim to decode three different executed hand movements in an online BCI scenario from electroencephalographic (EEG) data. Approach. Immersed in a desktop-based simulation environment, 15 non-disabled participants interacted with virtual objects from daily life by an avatar’s robotic arm. In a short calibration phase, participants performed executed palmar and lateral grasps and wrist supinations. Using this data, we trained a classification model on features extracted from the low frequency time domain. In the subsequent evaluation phase, participants controlled the avatar’s robotic arm and interacted with the virtual objects in case of a correct classification. Main results. On average, participants scored online 48% of all movement trials correctly (3-condition scenario, adjusted chance level 40%, alpha = 0.05). The underlying movement-related cortical potentials (MRCPs) of the acquired calibration data show significant differences between conditions over contralateral central sensorimotor areas, which are retained in the data acquired from the online BCI use. Significance. We could show the successful online decoding of two grasps and one wrist supination movement using low frequency time domain features of the human EEG. These findings can potentially contribute to the development of a more natural and intuitive BCI-based control modality for upper limb motor neuroprostheses or robotic arms for people with motor impairments.

Unimanual and Bimanual Reach-and-Grasp Actions Can Be Decoded From Human EEG.

While most tasks of daily life can be handled through a small number of different grasps, many tasks require the action of both hands. In these bimanual tasks, the second hand has either a supporting role (e.g. for fixating a jar) or a more active role (e.g. grasping a pot on both handles). In this study we attempt to discriminate the neural correlates of unimanual (performed with left and right hand) from bimanual reach-and-grasp actions using the low-frequency time-domain electroencephalogram (EEG). In a self-initiated movement task, 15 healthy participants were asked to perform unimanual (palmar and lateral grasps with left and right hand) and bimanual (double lateral, mixed palmar/lateral) reach-and-grasps on objects of daily life. Using EEG time-domain features in the frequency range of 0.3-3Hz, we achieved multiclass-classification accuracies of 38.6 ± 6.6% (7 classes, 17.1% chance level) for a combination of 6 movements and 1 rest condition. The grand average confusion matrix shows highest true positive rates (TPR) for the rest (63%) condition while TPR for the movement classes varied between 33 to 41%. The underlying movement-related cortical potentials (MRCPs) show significant differences between unimanual (e.g left hand vs. right hand grasps) as well unimanual vs. bimanual conditions which both can be attributed to lateralization effects. We believe that these findings can be exploited and further used for attempts in providing persons with spinal cord injury a form of natural control for bimanual neuroprostheses.

Quantification of Movement-Related EEG Correlates Associated with Motor Training: A Study on Movement-Related Cortical Potentials and Sensorimotor Rhythms.

The ability to learn motor tasks is important in both healthy and pathological conditions. Measurement tools commonly used to quantify the neurophysiological changes associated with motor training such as transcranial magnetic stimulation and functional magnetic resonance imaging pose some challenges, including safety concerns, utility, and cost. EEG offers an attractive alternative as a quantification tool. Different EEG phenomena, movement-related cortical potentials (MRCPs) and sensorimotor rhythms (event-related desynchronization—ERD, and event-related synchronization—ERS), have been shown to change with motor training, but conflicting results have been reported. The aim of this study was to investigate how the EEG correlates (MRCP and ERD/ERS) from the motor cortex are modulated by short (single session in 14 subjects) and long (six sessions in 18 subjects) motor training. Ninety palmar grasps were performed before and after 1 × 45 (or 6 × 45) min of motor training with the non-dominant hand (laparoscopic surgery simulation). Four channels of EEG were recorded continuously during the experiments. The MRCP and ERD/ERS from the alpha/mu and beta bands were calculated and compared before and after the training. An increase in the MRCP amplitude was observed after a single session of training, and a decrease was observed after six sessions. For the ERD/ERS analysis, a significant change was observed only after the single training session in the beta ERD. In conclusion, the MRCP and ERD change as a result of motor training, but they are subject to a marked intra- and inter-subject variability.

Functional classification of grasp strategies used by hemiplegic patients.

This study aimed to identify and qualify grasp-types used by patients with stroke and determine the clinical parameters that could explain the use of each grasp. Thirty-eight patients with chronic stroke-related hemiparesis and a range of motor and functional capacities (17 females and 21 males, aged 25–78), and 10 healthy subjects were included. Four objects were used (tissue packet, teaspoon, bottle and tennis ball). Participants were instructed to “grasp the object as if you are going to use it”. Three trials were video-recorded for each object. A total of 456 grasps were analysed and rated using a custom-designed Functional Grasp Scale. Eight grasp-types were identified from the analysis: healthy subjects used Multi-pulpar, Pluri-digital, Lateral-pinch and Palmar grasps (Standard Grasps). Patients used the same grasps with in addition Digito-palmar, Raking, Ulnar and Interdigital grasps (Alternative Grasps). Only patients with a moderate or relatively good functional ability used Standard grasps. The correlation and regression analyses showed this was conditioned by sufficient finger and elbow extensor strength (Pluri-digital grasp); thumb extensor and wrist flexor strength (Lateral pinch) or in forearm supinator strength (Palmar grasp). By contrast, the patients who had severe impairment used Alternative grasps that did not involve the thumb. These strategies likely compensate specific impairments. Regression and correlation analyses suggested that weakness had a greater influence over grasp strategy than spasticity. This would imply that treatment should focus on improving hand strength and control although reducing spasticity may be useful in some cases.

Feature domain-specific movement intention detection for stroke rehabilitation with brain-computer interfaces.

Brain-computer interface (BCI) driven electrical stimulation has been proposed for neuromodulation for stroke rehabilitation by pairing intentions to move with somatosensory feedback from electrical stimulation. Movement intentions have been detected in several studies using different techniques, with temporal and spectral features being the most common. A few studies have compared temporal and spectral features, but conflicting results have been reported. In this study, the aim was to investigate if complexity measures can be used for movement intention detection and to compare the detection performance based on features extracted from three different domains (time, frequency and complexity) from single-trial EEG. Two data sets were used where four different isometric palmar grasps or dorsiflexions were performed while continuous EEG was recorded. 39 healthy subjects performed or imagined these movements and 11 stroke patients attempted to perform the movements. The EEG was pre-processed and divided into two epoch classes: Background EEG (2 s) and movement intention (2 s). To obtain an estimated detection performance, temporal, spectral and complexity features were extracted and classified (linear discriminant analysis) after the feature vector was reduced using sequential forward selection. The results show that accuracies between 82-87% and 74-80% are obtained for foot and hand movements, respectively. The temporal feature domain was the most dominant for foot movement intention detection, while the spectral features contributed more to the hand movement detection. The complexity features could be used to detect movement intentions, but the performance was much lower compared to temporal and spectral features.

Detecting and classifying three different hand movement types through electroencephalography recordings for neurorehabilitation.

Brain-computer interfaces can be used for motor substitution and recovery; therefore, detection and classification of movement intention are crucial for optimal control. In this study, palmar, lateral and pinch grasps were differentiated from the idle state and classified from single-trial EEG using only information prior to the movement onset. Fourteen healthy subjects performed the three grasps 100 times, while EEG was recorded from 25 electrodes. Temporal and spectral features were extracted from each electrode, and feature reduction was performed using sequential forward selection (SFS) and principal component analysis (PCA). The detection problem was investigated as the ability to discriminate between movement preparation and the idle state. Furthermore, all task pairs and the three movements together were classified. The best detection performance across movements (79±8%) was obtained by combining temporal and spectral features. The best movement-movement discrimination was obtained using spectral features: 76±9% (2-class) and 63±10% (3-class). For movement detection and discrimination, the performance was similar across grasp types and task pairs; SFS outperformed PCA. The results show it is feasible to detect different grasps and classify the distinct movements using only information prior to the movement onset, which may enable brain-computer interface-based neurorehabilitation of upper limb function through Hebbian learning mechanisms.

Detecting and classifying movement-related cortical potentials associated with hand movements in healthy subjects and stroke patients from single-electrode, single-trial EEG

Objective. To detect movement intention from executed and imaginary palmar grasps in healthy subjects and attempted executions in stroke patients using one EEG channel. Moreover, movement force and speed were also decoded. Approach. Fifteen healthy subjects performed motor execution and imagination of four types of palmar grasps. In addition, five stroke patients attempted to perform the same movements. The movements were detected from the continuous EEG using a single electrode/channel overlying the cortical representation of the hand. Four features were extracted from the EEG signal and classified with a support vector machine (SVM) to decode the level of force and speed associated with the movement. The system performance was evaluated based on both detection and classification. Main results. ∼75% of all movements (executed, imaginary and attempted) were detected 100 ms before the onset of the movement. ∼60% of the movements were correctly classified according to the intended level of force and speed. When detection and classification were combined, ∼45% of the movements were correctly detected and classified in both the healthy and stroke subjects, although the performance was slightly better in healthy subjects. Significance. The results indicate that it is possible to use a single EEG channel for detecting movement intentions that may be combined with assistive technologies. The simple setup may lead to a smoother transition from laboratory tests to the clinic.

Multi-Pad Electrode for Effective Grasping: Design

We designed a new surface multi-pad electrode for the electrical stimulation of the forearm that is effective in controlling functional grasp in hemiplegic patients. The electrode shape and size were designed on the basis of the surface stimulation map of the forearm, determined from measurements in seven hemiplegic patients who had limited or absent voluntary movements of the fingers, thumb and wrist. The stimulation map for each patient was assessed with a conventional set of single pad Pals Platinum electrodes. Since the sites for the stimulation varied greatly between patients, the end result was a rather large multi-pad electrode. Modulating multi-pad electrode size, shape, position and individual pad stimulation parameters allows us to accommodate the diversity of the neural tissues in patients that need to be activated for functional grasp. This also allows asynchronous activation of different portions of the muscle and dynamic adaptation of the stimulation sites to appropriate underlying tissues during functional use. The validity of the determined stimulation map was tested in the same group of hemiplegic patients. The selected set of active pads resulted in fully functional and reproducible palmar and lateral grasps similar to healthy-like grasps.

Perception and production of object-related grasping in 6-month-olds

In this study, 6-month-olds’ perception of an object-related human grasping action was compared with their level of grasping performance using a within-participants design. In the action perception task, infants were presented with the video of an actor’s grasping movement toward an occluded target object. Subsequently, an expected and an unexpected final state of this grasping movement were presented simultaneously, and infants’ looking times were measured. In the action production task, infants were presented with three graspable objects. Infants’ grasping behavior was coded to be either palmar or thumb-opposite grasping. Results indicate that infants who were already able to perform a thumb-opposite grasp differentiated between the two final states in the action perception task by looking longer toward the unexpected final state. In contrast, infants who showed only palmar grasps looked equally long toward both final states. This finding supports the assumption that action perception and action control are already closely related in infants as young as 6 months.

Quantifying Arm Movements And Grasps To Support End-users' Needs During Prosthetic Limb Development

PURPOSE: To quantify arm movements and common grasps among blue collar workers (BCW) and white collar workers (WCW) over the normal course of daily activities. Data will guide scientists when designing a prosthetic arm with an ample energy source and sufficient degrees of motion and strength to enable users to perform real-life arm functions. METHODS: Participants were videotaped at their workplace during an entire workday. Videos were analyzed to determine dominant arm movement observations including: 1) total arm movements, 2) total and specific type of shoulder, elbow and wrist movements (e.g. shoulder abduction/adduction), 3) total loaded (object grasped in hand during movement) versus unloaded movements, and 4) total compliant (pliable) versus non-compliant (rigid) object grasps. Movements were recorded if any action was observed, regardless of the range of arm movement. Commonly observed grasps during the workday were also recorded. RESULTS: Four men with a mean (±SD) age of 40±8 yrs were studied, consisting of two BCW (machinist and auto-mechanic) and two WCW (engineer and researcher). Total arm movements observed for the workday were: machinist = 15,700, auto-mechanic = 16,103, engineer = 9,130, and researcher = 9,305. Sixty-three percent of movements for BCW were loaded (object grasped in hand during movement) compared to 21% for WCW. The BCW had more than twice the amount of observed object grasps compared to WCW (combined totals: 4,806 for BCW vs. 2,236 for WCW). Eighty-two percent of grasps for BCW were for non-compliant objects, whereas 29% were observed in WCW. Common grasps included the tip, cylindrical and spherical grasp for the BCW and spherical, palmar and lateral grasps for the WCW. Objects commonly grasped by the BCW were tools and drill bits, while WCW grasps included paper and a computer mouse. CONCLUSIONS: BCW exhibit a common pattern of grasps of non-compliant objects with loaded arm movements; in contrast, WCW exhibit a pattern of compliant object grasps with non-loaded arm movements. To optimize function and use, prosthetic limbs should be designed considering the movement/grasp patterns and strength needs of the end-user. This work was sponsored by the DARPA Revolutionizing Prosthetics Program, Contract N66001-06-C-8005 (abstract is approved for public release, distribution unlimited).

Control of prehension for the transradial prosthesis: Natural-like image recognition system

We describe the hardware and software for the control of prehension for a dexterous transradial prosthesis. The prehension process comprises hand orientation (three degrees of freedom) and the opening of the hand in a manner that is appropriate for the shape and size of the object. The hardware consists of a standard web camera, accelerometer, ultrasound distance sensor, laser pointer and an LED illumination system. Software operating in real time estimates the shape and size of the object as well as the relative orientation of the hand with respect to the object. Based on this data, the controller generates signals that are sent to the three-dimensional (3D) wrist rotator, and drives which control fingers and thumb of the transradial prosthesis, thereby preparing the hand for palmar, lateral, or precision (2-digit or 3-digit) grasps. The choice of the grasp follows heuristics captured from healthy humans when grasping and expressed in the form of IF-THEN rules.

Automatic determination of the optimal shape of a surface electrode: Selective stimulation

We present a method for automatic determination of the shape and position of the surface electrode for selective control of fingers extension and flexion by means of electrical stimulation. The multi-pad electrodes used in the experiments comprised 24 pads (1 cm diameter) distributed over an area (7 cm × 10 cm) positioned over dorsal and volar aspects of the forearm. The four-channel stimulation system for grasping comprised also an oval reference electrode over the carpal tunnel, and two oval electrodes over the thenar and thumb extensor muscles. We measured seven angles: proximal inter-phalangeal and metacarpal phalangeal index and ring finger joint rotations, wrist extension/flexion and ulnar/radial rotation, and pronation/supination of the forearm. The optimal electrode was determined as the combination of pads that led to fingers, wrist and forearm rotations being similar to the trajectories of healthy individuals when grasping. The similarity of trajectories was assessed by analyzing the aggregate error defined as the sum of squares of differences between the angles measured when stimulating the forearm in tetraplegics and the angles measured in healthy individuals. The aggregate errors were determined from measurements during sequential stimulation of each of the 24 pads. The analysis comprised hand opening and closing for palmar and lateral grasps. The time for determining the optimal electrode was about 10 min. The optimal electrodes had different branched shapes in each of the six tetraplegics; however, once determined they remained unchanged when tested on different days.

Multi‐Field Surface Electrode for Selective Electrical Stimulation

We designed a 24-field array and an on-line control box that selects which and how many of 24 fields will conduct electrical charge during functional electrical stimulation. The array was made using a conductive microfiber textile, silver two-component adhesive, and the conductive ink imprint on the polycarbonate. The control box comprised 24 switches that corresponded one-to-one to the fields on the array. Each field could be made conductive or nonconductive by simple pressing of the corresponding push-button type switch on the control box. We present here representative results of the selectivity of the new electrode measured in three tetraplegic patients during functional electrical stimulation of the forearm. The task was to generate finger flexion and extension with minimal interference of the wrist movement during lateral and palmar grasps. Therapists determined the appropriate pattern that lead to effective grasping, lasting on average 5 min per stimulation channel in the first session. This optimal conductive pattern (size and shape) provided effective finger flexion and extension with minimal wrist flexion/extension and ulnar/radial deviations (<10 degrees). The optimal size and shape of the electrode in all cases had a branched pattern. The selection of the optimal stimulation site was achieved without moving the electrode. The size and shape were reproducible in the same subject from session to session, yet were different from subject to subject. The optimal electrode size and shape changed when subjects pronated and supinated their forearm. The control box includes a program that can dynamically change the number and sites of the conductive fields; hence, it is feasible to use this during functional movements. Subjects learned how to determine the optimal electrode pattern; hence, these electrodes could be effective for home usage.

Synthesis of hand grasp using functional neuromuscular stimulation.

A functional neuromuscular stimulation system has been developed to provide grasp-release function in quadriplegic individuals. A single command input from the subject controls the stimulus levels to a number of electrodes, thus simultaneously activating several muscles. A method for synthesizing the command input to stimulus output relationship has been developed. The first step involves electrode profiling, which is a method for characterizing the output of an individual electrode/muscle combination. The electrodes are then grouped according to function and a set of rule based procedures is used to synthesize the basic grasp parameters. Results are presented demonstrating the output from lateral and palmar grasps developed by this method. The method has successfully resulted in grasping patterns that can be utilized functionally. Limitations of the method and future improvements are discussed.