Neural Coding of Finger and Wrist Movements

Previous work (Schieber and Hibbard, 1993) has shown that single motor cortical neurons do not discharge specifically for a particular flexion-extension finger movement but instead are active with movements of different fingers. In addition, neuronal populations active with movements of different fingers overlap extensively in their spatial locations in the motor cortex. These data suggested that control of any finger movement utilizes a distributed population of neurons. In this study we applied the neuronal population vector analysis (Georgopoulos et al., 1983) to these same data to determine (1) whether single cells are tuned in an abstract, three-dimensional (3D) instructed finger and wrist movement space with hand-like geometry and (2) whether the neuronal population encodes specific finger movements. We found that the activity of 132/176 (75%) motor cortical neurons related to finger movements was indeed tuned in this space. Moreover, the population vector computed in this space predicted well the instructed finger movement. Thus, although single neurons may be related to several disparate finger movements, and neurons related to different finger movements are intermingled throughout the hand area of the motor cortex, the neuronal population activity does specify particular finger movements.     

Decoding Individual Finger Movements from One Hand Using Human EEG Signals

Brain computer interface (BCI) is an assistive technology, which decodes neurophysiological signals generated by the human brain and translates them into control signals to control external devices, e.g., wheelchairs. One problem challenging noninvasive BCI technologies is the limited control dimensions from decoding movements of, mainly, large body parts, e.g., upper and lower limbs. It has been reported that complicated dexterous functions, i.e., finger movements, can be decoded in electrocorticography (ECoG) signals, while it remains unclear whether noninvasive electroencephalography (EEG) signals also have sufficient information to decode the same type of movements. Phenomena of broadband power increase and low-frequency-band power decrease were observed in EEG in the present study, when EEG power spectra were decomposed by a principal component analysis (PCA). These movement-related spectral structures and their changes caused by finger movements in EEG are consistent with observations in previous ECoG study, as well as the results from ECoG data in the present study. The average decoding accuracy of 77.11% over all subjects was obtained in classifying each pair of fingers from one hand using movement-related spectral changes as features to be decoded using a support vector machine (SVM) classifier. The average decoding accuracy in three epilepsy patients using ECoG data was 91.28% with the similarly obtained features and same classifier. Both decoding accuracies of EEG and ECoG are significantly higher than the empirical guessing level (51.26%) in all subjects (p<0.05). The present study suggests the similar movement-related spectral changes in EEG as in ECoG, and demonstrates the feasibility of discriminating finger movements from one hand using EEG. These findings are promising to facilitate the development of BCIs with rich control signals using noninvasive technologies.     

Grasp Detection from Human ECoG during Natural Reach-to-Grasp Movements

Various movement parameters of grasping movements, like velocity or type of the grasp, have been successfully decoded from neural activity. However, the question of movement event detection from brain activity, that is, decoding the time at which an event occurred (e.g. movement onset), has been addressed less often. Yet, this may be a topic of key importance, as a brain-machine interface (BMI) that controls a grasping prosthesis could be realized by detecting the time of grasp, together with an optional decoding of which type of grasp to apply. We, therefore, studied the detection of time of grasps from human ECoG recordings during a sequence of natural and continuous reach-to-grasp movements. Using signals recorded from the motor cortex, a detector based on regularized linear discriminant analysis was able to retrieve the time-point of grasp with high reliability and only few false detections. Best performance was achieved using a combination of signal components from time and frequency domains. Sensitivity, measured by the amount of correct detections, and specificity, represented by the amount of false detections, depended strongly on the imposed restrictions on temporal precision of detection and on the delay between event detection and the time the event occurred. Including neural data from after the event into the decoding analysis, slightly increased accuracy, however, reasonable performance could also be obtained when grasping events were detected 125 ms in advance. In summary, our results provide a good basis for using detection of grasping movements from ECoG to control a grasping prosthesis.     

Classification of finger movements by using the ultra-wide band radar

The coding system of finger movements depends on the differences in the characteristics of the muscles that are responsible for these movements. The ability of ultra-wide band (UWB) radar for use as a tool for identifying the movements of each finger is presented. This will facilitate the ability of the UWB radar in designing a coding system for the movement of fingers of each hand.     

Classification of finger movements by using the ultra-wide band radar

The coding system of finger movements depends on the differences in the characteristics of the muscles that are responsible for these movements. The ability of ultra-wide band (UWB) radar for use as a tool for identifying the movements of each finger is presented. This will facilitate the ability of the UWB radar in designing a coding system for the movement of fingers of each hand.     

fNIRS Exhibits Weak Tuning to Hand Movement Direction

Functional near-infrared spectroscopy (fNIRS) has become an established tool to investigate brain function and is, due to its portability and resistance to electromagnetic noise, an interesting modality for brain-machine interfaces (BMIs). BMIs have been successfully realized using the decoding of movement kinematics from intra-cortical recordings in monkey and human. Recently, it has been shown that hemodynamic brain responses as measured by fMRI are modulated by the direction of hand movements. However, quantitative data on the decoding of movement direction from hemodynamic responses is still lacking and it remains unclear whether this can be achieved with fNIRS, which records signals at a lower spatial resolution but with the advantage of being portable. Here, we recorded brain activity with fNIRS above different cortical areas while subjects performed hand movements in two different directions. We found that hemodynamic signals in contralateral sensorimotor areas vary with the direction of movements, though only weakly. Using these signals, movement direction could be inferred on a single-trial basis with an accuracy of ∼65% on average across subjects. The temporal evolution of decoding accuracy resembled that of typical hemodynamic responses observed in motor experiments. Simultaneous recordings with a head tracking system showed that head movements, at least up to some extent, do not influence the decoding of fNIRS signals. Due to the low accuracy, fNIRS is not a viable alternative for BMIs utilizing decoding of movement direction. However, due to its relative resistance to head movements, it is promising for studies investigating brain activity during motor experiments.     

Timing of secondary vestibular neuron responses to a range of rotational head movements

Secondary vestibular neurons exhibit a wide variety of responses to a head movement, with the response of each secondary neuron depending upon the particular primary afferents converging onto it. A single head movement is thereby registered in a distributed manner. This paper focuses on implications of afferent convergence to the relative timing of secondary neuron response modulation during rotational movements about a combination of horizontal axes. In particular, the neurons of interest are those that receive input from afferents innervating the vertical semicircular canals, and the movements of interest are those that have a sinusoidal component about one vertical canal axis and a sinusoidal component about another, approximately orthogonal, vertical canal axis. Under these conditions, the present research shows that it is possible for two or more secondary neurons to have a different relative timing of response (i.e., different relative phase of the periodic modulation in firing rate) for different head movements, and for the neurons to switch their order of response for different movements. For particular head movements, those same neurons will respond in phase. From the point of view of the nervous system, the relative timing of neuron responses may tell which movement is taking place, but with certain restrictions as discussed in the present paper. Shown here is that, among those head movements for which the two components of rotation may be at any phase relative to one another and have any relative amplitude, an in-phase response of just two neurons cannot identify a single motion. Two neurons that respond in phase for one motion must respond in phase for an entire range of motions; all motions in that range are thus response-equivalent, in the sense that the pair of neurons cannot distinguish between the two motions. On the other hand, an in-phase response of three neurons can identify a single motion, for certain patterns of primary afferent convergence. Received: 16 December 1996 / Accepted in revised form: 3 April 1998     

回转器旋转对体外培养的鸡胚神经元的影响

用回转器旋转研究重力改变对体外培养的鸡胚神经元的影响。结果发现在回转器里经过７－９小时的处理后,神经元的形态和行为发生明显改变。只有６．３％的神经元呈现两极型的运动形态,而对照组正常培养的细胞运动形态的神经元占３４．２％。某些细胞的突起出现异常,呈现扭曲的形状。在培养基质上的神经元伸出许多丝状突起,丝状突起的末端锚定在周围的细胞上或锚定在培养基质上。经过重力改变处理的细胞重新放在相差显微镜下观察,结果显示神经元的运动活性降低,许多细胞没有观察到明显运动的迹象。     

Tendon displacements during voluntary and involuntary finger movements

In the human hand, independent movement control of individual fingers is limited. One potential cause for this is mechanical connections between the tendons and muscle bellies corresponding to the different fingers. The aim of this study was to determine the tendon displacement of the flexor digitorum superficialis (FDS) of both the instructed and the neighboring, non-instructed fingers during single finger flexion movements. In nine healthy subjects (age 22–29 years), instructed and non-instructed FDS finger tendon displacement of the index, middle and ring finger was measured using 2D ultrasound analyzed with speckle tracking software in two conditions: active flexion of all finger joints with all fingers free to move and active flexion while the non-instructed fingers were restricted. Our results of the free movement protocol showed an average tendon displacement of 27 mm for index finger flexion, 21 mm for middle finger flexion and 17 mm for ring finger flexion. Displacements of the non-instructed finger tendons (≈12 mm) were higher than expected based of the amount of non-instructed finger movement. In the restricted protocol, we found that, despite minimal joint movements, substantial non-instructed finger tendon displacement (≈9 mm) was still observed, which was interpreted as a result of tendon strain. When this strain component was subtracted from the tendon displacement of the non-instructed fingers during the free movement condition, the relationship between finger movement and tendon displacement of the instructed and non-instructed finger became comparable. Thus, when studying non-instructed finger tendon displacement it is important to take tendon strain into consideration.     

Cortical mechanisms of smooth eye movements revealed by dynamic covariations of neural and behavioral responses

Neural activity in the frontal eye fields controls smooth pursuit eye movements, but the relationship between single neuron responses, cortical population responses, and eye movements is not well understood. We describe an approach to dynamically link trial-to-trial fluctuations in neural responses to parallel variations in pursuit and demonstrate that individual neurons predict eye velocity fluctuations at particular moments during the course of behavior, while the population of neurons collectively tiles the entire duration of the movement. The analysis also reveals the strength of correlations in the eye movement predictions derived from pairs of simultaneously recorded neurons and suggests a simple model of cortical processing. These findings constrain the primate cortical code for movement, suggesting that either a few neurons are sufficient to drive pursuit at any given time or that many neurons operate collectively at each moment with remarkably little variation added to motor command signals downstream from the cortex.     

Coordinated force production in multi-finger tasks: finger interaction and neural network modeling

During maximal voluntary contraction (MVC) with several fingers, the following three phenomena are observed: (1) the total force produced by all the involved fingers is shared among the fingers in a specific manner (sharing); (2) the force produced by a given finger in a multi-finger task is smaller than the force generated by this finger in a single-finger task (force deficit); (3) the fingers that are not required to produce any force by instruction are involuntary activated (enslaving). We studied involuntary force production by individual fingers (enslaving effects, EE) during tasks when (an)other finger(s) of the hand generated maximal voluntary pressing force in isometric conditions. The subjects (n = 10) were instructed to press as hard as possible on the force sensors with one, two, three and four fingers acting in parallel in all possible combinations. The EE were (A) large, the slave fingers always producing a force ranging from 10.9% to 54.7% of the maximal force produced by the finger in the single-finger task; (B) nearly symmetrical; (C) larger for the neighboring fingers; and (D) non-additive. In most cases, the EE from two or three fingers were smaller than the EE from at least one finger (this phenomenon was coined occlusion). The occlusion cannot be explained only by anatomical musculo-tendinous connections. Therefore, neural factors contribute substantially to the EE. A neural network model that accounts for all the three effects has been developed. The model consists of three layers: the input layer that models a central neural drive; the hidden layer modeling transformation of the central drive into an input signal to the muscles serving several fingers simultaneously (multi-digit muscles); and the output layer representing finger force output. The output of the hidden layer is set inversely proportional to the number of fingers involved. In addition, direct connections between the input and output layers represent signals to the hand muscles serving individual fingers (uni-digit muscles). The network was validated using three different training sets. Single digit muscles contributed from 25% to 50% of the total finger force. The master matrix and the enslaving matrix were computed; they characterize the ability of a given finger to enslave other fingers and its ability to be enslaved. Overall, the neural network modeling suggests that no direct correspondence exists between neural command to an individual finger and finger force. To produce a desired finger force, a command sent to an intended finger should be scaled in accordance with the commands sent to the other fingers. Received: 17 October 1997 / Accepted in revised form: 12 May 1998     

Hand function: peripheral and central constraints on performance.

The hand is one of the most fascinating and sophisticated biological motor systems. The complex biomechanical and neural architecture of the hand poses challenging questions for understanding the control strategies that underlie the coordination of finger movements and forces required for a wide variety of behavioral tasks, ranging from multidigit grasping to the individuated movements of single digits. Hence, a number of experimental approaches, from studies of finger movement kinematics to the recording of electromyographic and cortical activities, have been used to extend our knowledge of neural control of the hand. Experimental evidence indicates that the simultaneous motion and force of the fingers are characterized by coordination patterns that reduce the number of independent degrees of freedom to be controlled. Peripheral and central constraints in the neuromuscular apparatus have been identified that may in part underlie these coordination patterns, simplifying the control of multi-digit grasping while placing certain limitations on individuation of finger movements. We review this evidence, with a particular emphasis on how these constraints extend through the neuromuscular system from the behavioral aspects of finger movements and forces to the control of the hand from the motor cortex.     

Population Coding of Forelimb Joint Kinematics by Peripheral Afferents in Monkeys

Various peripheral receptors provide information concerning position and movement to the central nervous system to achieve complex and dexterous movements of forelimbs in primates. The response properties of single afferent receptors to movements at a single joint have been examined in detail, but the population coding of peripheral afferents remains poorly defined. In this study, we obtained multichannel recordings from dorsal root ganglion (DRG) neurons in cervical segments of monkeys. We applied the sparse linear regression (SLiR) algorithm to the recordings, which selects useful input signals to reconstruct movement kinematics. Multichannel recordings of peripheral afferents were performed by inserting multi-electrode arrays into the DRGs of lower cervical segments in two anesthetized monkeys. A total of 112 and 92 units were responsive to the passive joint movements or the skin stimulation with a painting brush in Monkey 1 and Monkey 2, respectively. Using the SLiR algorithm, we reconstructed the temporal changes of joint angle, angular velocity, and acceleration at the elbow, wrist, and finger joints from temporal firing patterns of the DRG neurons. By automatically selecting a subset of recorded units, the SLiR achieved superior generalization performance compared with a regularized linear regression algorithm. The SLiR selected not only putative muscle units that were responsive to only the passive movements, but also a number of putative cutaneous units responsive to the skin stimulation. These results suggested that an ensemble of peripheral primary afferents that contains both putative muscle and cutaneous units encode forelimb joint kinematics of non-human primates.     

Decoding Hindlimb Movement for a Brain Machine Interface after a Complete Spinal Transection

Stereotypical locomotor movements can be made without input from the brain after a complete spinal transection. However, the restoration of functional gait requires descending modulation of spinal circuits to independently control the movement of each limb. To evaluate whether a brain-machine interface (BMI) could be used to regain conscious control over the hindlimb, rats were trained to press a pedal and the encoding of hindlimb movement was assessed using a BMI paradigm. Off-line, information encoded by neurons in the hindlimb sensorimotor cortex was assessed. Next neural population functions, or weighted representations of the neuronal activity, were used to replace the hindlimb movement as a trigger for reward in real-time (on-line decoding) in three conditions: while the animal could still press the pedal, after the pedal was removed and after a complete spinal transection. A novel representation of the motor program was learned when the animals used neural control to achieve water reward (e.g. more information was conveyed faster). After complete spinal transection, the ability of these neurons to convey information was reduced by more than 40%. However, this BMI representation was relearned over time despite a persistent reduction in the neuronal firing rate during the task. Therefore, neural control is a general feature of the motor cortex, not restricted to forelimb movements, and can be regained after spinal injury.     

Incorporating the length-dependent passive-force generating muscle properties of the extrinsic finger muscles into a wrist and finger biomechanical musculoskeletal model

Dynamic movement trajectories of low mass systems have been shown to be predominantly influenced by passive viscoelastic joint forces and torques compared to momentum and inertia. The hand is comprised of 27 small mass segments. Because of the influence of the extrinsic finger muscles, the passive torques about each finger joint become a complex function dependent on the posture of multiple joints of the distal upper limb. However, biomechanical models implemented for the dynamic simulation of hand movements generally don’t extend proximally to include the wrist and distal upper limb. Thus, they cannot accurately represent these complex passive torques. The purpose of this short communication is to both describe a method to incorporate the length-dependent passive properties of the extrinsic index finger muscles into a biomechanical model of the upper limb and to demonstrate their influence on combined movement of the wrist and fingers. Leveraging a unique set of experimental data, that describes the net passive torque contributed by the extrinsic finger muscles about the metacarpophalangeal joint of the index finger as a function of both metacarpophalangeal and wrist postures, we simulated the length-dependent passive properties of the extrinsic finger muscles. Dynamic forward simulations demonstrate that a model including these properties passively exhibits coordinated movement between the wrist and finger joints, mimicking tenodesis, a behavior that is absent when the length-dependent properties are removed. This work emphasizes the importance of incorporating the length-dependent properties of the extrinsic finger muscles into biomechanical models to study healthy and impaired hand movements.     

Basal ganglia neural mechanisms of natural movement sequences

Natural rodent grooming and other instinctive behavior serves as a natural model of complex movement sequences. Rodent grooming has syntactic (rule-driven) sequences and more random movement patterns. Both incorporate the same movements--only the serial structure differs. Recordings of neural activity in the dorsolateral striatum and the substantia nigra pars reticulata indicate preferential activation during syntactic sequences over more random sequences. Neurons that are responsive during syntactic grooming sequences are often unresponsive or have reverse activation profiles during kinematically similar movements that occur in flexible or random grooming sequences. Few neurons could be categorized as strictly movement related--instead they were activated only in the context of particular sequential patterns of movements. Particular sequential patterns included "syntactic chain" grooming sequences of paw, head, and body movements and also "warm-up" sequences, which consist of head and body/limb movements that precede locomotion after a period of quiet resting (Golani 1992). Activation during warm-up was less intense and less frequent than during grooming sequences, but both sequences activated neurons above baseline levels, and the same neurons sometimes responded to both sequences. The fact that striatal neurons code 2 natural sequences which are made up of different constituent movements suggests that the basal ganglia may have a generalized role in sequence control. The basal ganglia are modulated by the context of the sequence and may play an executive function in the complex natural patterns of sequenced behaviour.     

Combined Use of FSR Sensor Array and SVM Classifier for Finger Motion Recognition Based on Pressure Distribution Map

For controlling dexterous prosthetic hand with a high number of active Degrees of Freedom (DOF),it is necessary to reliably extract control volitions of finger motions from the human body.In this study,a large variety of finger motions are discriminated based on the diversities of the pressure distribution produced by the mechanical actions of muscles on the forearm.The pressure distribution patterns corresponding to the motions were measured by sensor array which is composed of 32 Force Sensitive Resistor (FSR) sensors.In order to map the pressure patterns with different finger motions,a multiclass classifier was designed based on the Support Vector Machine (SVM) algorithm.The multi-subject experiments show that it is possible to identify as many as seventeen different finger motions,including individual finger motions and multi-finger grasping motions,with the accuracy above 99％ in the in-session validation.Further,the cross-session validation demonstrates that the performance of the proposed method is robust for use if the FSR array is not reset.The results suggest that the proposed method has great application prospects for the control of multi-DOF dexterous hand prosthesis.     

Effect of the loci of visual cues on ability of polyrhythmic bimanual movement

Abstract

The present study investigated whether the deviation of the performed movement cycle from the required cycle during polyrhythmic bimanual (BM) movement depends on the loci of the visual cues that guide the rhythm of finger movements. Twelve healthy right-handed males rhythmically abducted and adducted the index finger or index fingers with the rhythm of the visual cues. During UM movement, the visual cue guiding the rhythm of finger movement was provided in the left or right visual hemifield. During 2:3 polyrhythmic BM movement, two visual cues, one guiding the rhythm of the left finger movement and another guiding the rhythm of the right finger movement, were provided in a single visual hemifield, or each visual cue guiding each finger movement was provided in each visual hemifield. During polyrhythmic BM movement, the cycle duration of the slower side of the movement guided by the rhythm of the visual cues provided in one visual hemifield was shorter than the required cycle duration, and the magnitude of the shortage in this condition was greater than that guided by each visual cue provided in each visual hemifield. Slower side of the movement is more precisely performed by each visual cue guiding each finger movement in each visual hemifield rather than that guided by visual cues provided in one visual hemifield during polyrhythmic BM movement. This may be explained by bottle-neck model in which visual information overflows the processing capacity when two visual processes are simultaneously provided in a single visual cortex.     

Rate effects on timing, key velocity, and finger kinematics in piano performance

We examined the effect of rate on finger kinematics in goal-directed actions of pianists. In addition, we evaluated whether movement kinematics can be treated as an indicator of personal identity. Pianists' finger movements were recorded with a motion capture system while they performed melodies from memory at different rates. Pianists' peak finger heights above the keys preceding keystrokes increased as tempo increased, and were attained about one tone before keypress. These rate effects were not simply due to a strategy to increase key velocity (associated with tone intensity) of the corresponding keystroke. Greater finger heights may compensate via greater tactile feedback for a speed-accuracy tradeoff that underlies the tendency toward larger temporal variability at faster tempi. This would allow pianists to maintain high temporal accuracy when playing at fast rates. In addition, finger velocity and accelerations as pianists' fingers approached keys were sufficiently unique to allow pianists' identification with a neural-network classifier. Classification success was higher in pianists with more extensive musical training. Pianists' movement "signatures" may reflect unique goal-directed movement kinematic patterns, leading to individualistic sound.     

Force and torque production in static multifinger prehension: biomechanics and control. II. Control

 The coordination of digits during combined force/torque production tasks was further studied using the data presented in the companion paper [Zatsiorsky et al. Biol Cybern this issue, Part I]. Optimization was performed using as criteria the cubic norms of (a) finger forces, (b) finger forces normalized with respect to the maximal forces measured in single-finger tasks, (c) finger forces normalized with respect to the maximal forces measured in a four-finger task, and (d) finger forces normalized with respect to the maximal moments that can be generated by the fingers. All four criteria failed to predict antagonist finger moments when these moments were not imposed by the task mechanics. Reconstruction of neural commands: The vector of neural commands c was reconstructed from the equation c=W ⁻¹ F, where W is the finger interconnection weight matrix and F is the vector of finger forces. The neural commands ranged from zero (no voluntary force production) to one (maximal voluntary contraction). For fingers producing moments counteracting the external torque (`agonist' fingers), the intensity of the neural commands was well correlated with the relative finger forces normalized to the maximal forces in a four-finger task. When fingers produced moments in the direction of the external torque (`antagonist' fingers), the relative finger forces were always larger than those expected from the intensity of the corresponding neural commands. The individual finger forces were decomposed into forces due to `direct' commands and forces induced by enslaving effects. Optimization of the neural commands resulted in the best correspondence between actual and predicted finger forces. The antagonist moments are, at least in part, due to enslaving effects: strong commands to agonist fingers also activated antagonist fingers. Received: 8 August 2001 / Accepted in revised form: 7 February 2002