Machine learning-based dexterous control of hand prostheses

Upper-limb myoelectric prostheses are controlled by muscle activity information recorded on the skin surface using electromyography (EMG). Intuitive prosthetic control can be achieved by deploying statistical and machine learning (ML) tools to decipher the user’s movement intent from EMG signals. This thesis proposes various means of advancing the capabilities of non-invasive, ML-based control of myoelectric hand prostheses. Two main directions are explored, namely classification-based hand grip selection and proportional finger position control using regression methods. Several practical aspects are considered with the aim of maximising the clinical impact of the proposed methodologies, which are evaluated with offline analyses as well as real-time experiments involving both able-bodied and transradial amputee participants. It has been generally accepted that the EMG signal may not always be a reliable source of control information for prostheses, mainly due to its stochastic and non-stationary properties. One particular issue associated with the use of surface EMG signals for upper-extremity myoelectric control is the limb position effect, which is related to the lack of decoding generalisation under novel arm postures. To address this challenge, it is proposed to make concurrent use of EMG sensors and inertial measurement units (IMUs). It is demonstrated this can lead to a significant improvement in both classification accuracy (CA) and real-time prosthetic control performance. Additionally, the relationship between surface EMG and inertial measurements is investigated and it is found that these modalities are partially related due to reflecting different manifestations of the same underlying phenomenon, that is, the muscular activity. In the field of upper-limb myoelectric control, the linear discriminant analysis (LDA) classifier has arguably been the most popular choice for movement intent decoding. This is mainly attributable to its ease of implementation, low computational requirements, and acceptable decoding performance. Nevertheless, this particular method makes a strong fundamental assumption, that is, data observations from different classes share a common covariance structure. Although this assumption may often be violated in practice, it has been found that the performance of the method is comparable to that of more sophisticated algorithms. In this thesis, it is proposed to remove this assumption by making use of general class-conditional Gaussian models and appropriate regularisation to avoid overfitting issues. By performing an exhaustive analysis on benchmark datasets, it is demonstrated that the proposed approach based on regularised discriminant analysis (RDA) can offer an impressive increase in decoding accuracy. By combining the use of RDA classification with a novel confidence-based rejection policy that intends to minimise the rate of unintended hand motions, it is shown that it is feasible to attain robust myoelectric grip control of a prosthetic hand by making use of a single pair of surface EMG-IMU sensors. Most present-day commercial prosthetic hands offer the mechanical abilities to support individual digit control; however, classification-based methods can only produce pre-defined grip patterns, a feature which results in prosthesis under-actuation. Although classification-based grip control can provide a great advantage over conventional strategies, it is far from being intuitive and natural to the user. A potential way of approaching the level of dexterity enjoyed by the human hand is via continuous and individual control of multiple joints. To this end, an exhaustive analysis is performed on the feasibility of reconstructing multidimensional hand joint angles from surface EMG signals. A supervised method based on the eigenvalue formulation of multiple linear regression (MLR) is then proposed to simultaneously reduce the dimensionality of input and output variables and its performance is compared to that of typically used unsupervised methods, which may produce suboptimal results in this context. An experimental paradigm is finally designed to evaluate the efficacy of the proposed finger position control scheme during real-time prosthesis use. This thesis provides insight into the capacity of deploying a range of computational methods for non-invasive myoelectric control. It contributes towards developing intuitive interfaces for dexterous control of multi-articulated prosthetic hands by transradial amputees

Novel Bidirectional Body - Machine Interface to Control Upper Limb Prosthesis

Objective. The journey of a bionic prosthetic user is characterized by the opportunities and limitations involved in adopting a device (the prosthesis) that should enable activities of daily living (ADL). Within this context, experiencing a bionic hand as a functional (and, possibly, embodied) limb constitutes the premise for mitigating the risk of its abandonment through the continuous use of the device. To achieve such a result, different aspects must be considered for making the artificial limb an effective support for carrying out ADLs. Among them, intuitive and robust control is fundamental to improving amputees’ quality of life using upper limb prostheses. Still, as artificial proprioception is essential to perceive the prosthesis movement without constant visual attention, a good control framework may not be enough to restore practical functionality to the limb. To overcome this, bidirectional communication between the user and the prosthesis has been recently introduced and is a requirement of utmost importance in developing prosthetic hands. Indeed, closing the control loop between the user and a prosthesis by providing artificial sensory feedback is a fundamental step towards the complete restoration of the lost sensory-motor functions. Within my PhD work, I proposed the development of a more controllable and sensitive human-like hand prosthesis, i.e., the Hannes prosthetic hand, to improve its usability and effectiveness. Approach. To achieve the objectives of this thesis work, I developed a modular and scalable software and firmware architecture to control the Hannes prosthetic multi-Degree of Freedom (DoF) system and to fit all users’ needs (hand aperture, wrist rotation, and wrist flexion in different combinations). On top of this, I developed several Pattern Recognition (PR) algorithms to translate electromyographic (EMG) activity into complex movements. However, stability and repeatability were still unmet requirements in multi-DoF upper limb systems; hence, I started by investigating different strategies to produce a more robust control. To do this, EMG signals were collected from trans-radial amputees using an array of up to six sensors placed over the skin. Secondly, I developed a vibrotactile system to implement haptic feedback to restore proprioception and create a bidirectional connection between the user and the prosthesis. Similarly, I implemented an object stiffness detection to restore tactile sensation able to connect the user with the external word. This closed-loop control between EMG and vibration feedback is essential to implementing a Bidirectional Body - Machine Interface to impact amputees’ daily life strongly. For each of these three activities: (i) implementation of robust pattern recognition control algorithms, (ii) restoration of proprioception, and (iii) restoration of the feeling of the grasped object's stiffness, I performed a study where data from healthy subjects and amputees was collected, in order to demonstrate the efficacy and usability of my implementations. In each study, I evaluated both the algorithms and the subjects’ ability to use the prosthesis by means of the F1Score parameter (offline) and the Target Achievement Control test-TAC (online). With this test, I analyzed the error rate, path efficiency, and time efficiency in completing different tasks. Main results. Among the several tested methods for Pattern Recognition, the Non-Linear Logistic Regression (NLR) resulted to be the best algorithm in terms of F1Score (99%, robustness), whereas the minimum number of electrodes needed for its functioning was determined to be 4 in the conducted offline analyses. Further, I demonstrated that its low computational burden allowed its implementation and integration on a microcontroller running at a sampling frequency of 300Hz (efficiency). Finally, the online implementation allowed the subject to simultaneously control the Hannes prosthesis DoFs, in a bioinspired and human-like way. In addition, I performed further tests with the same NLR-based control by endowing it with closed-loop proprioceptive feedback. In this scenario, the results achieved during the TAC test obtained an error rate of 15% and a path efficiency of 60% in experiments where no sources of information were available (no visual and no audio feedback). Such results demonstrated an improvement in the controllability of the system with an impact on user experience. Significance. The obtained results confirmed the hypothesis of improving robustness and efficiency of a prosthetic control thanks to of the implemented closed-loop approach. The bidirectional communication between the user and the prosthesis is capable to restore the loss of sensory functionality, with promising implications on direct translation in the clinical practice

Intelligent Biosignal Processing in Wearable and Implantable Sensors

This reprint provides a collection of papers illustrating the state-of-the-art of smart processing of data coming from wearable, implantable or portable sensors. Each paper presents the design, databases used, methodological background, obtained results, and their interpretation for biomedical applications. Revealing examples are brain–machine interfaces for medical rehabilitation, the evaluation of sympathetic nerve activity, a novel automated diagnostic tool based on ECG data to diagnose COVID-19, machine learning-based hypertension risk assessment by means of photoplethysmography and electrocardiography signals, Parkinsonian gait assessment using machine learning tools, thorough analysis of compressive sensing of ECG signals, development of a nanotechnology application for decoding vagus-nerve activity, detection of liver dysfunction using a wearable electronic nose system, prosthetic hand control using surface electromyography, epileptic seizure detection using a CNN, and premature ventricular contraction detection using deep metric learning. Thus, this reprint presents significant clinical applications as well as valuable new research issues, providing current illustrations of this new field of research by addressing the promises, challenges, and hurdles associated with the synergy of biosignal processing and AI through 16 different pertinent studies. Covering a wide range of research and application areas, this book is an excellent resource for researchers, physicians, academics, and PhD or master students working on (bio)signal and image processing, AI, biomaterials, biomechanics, and biotechnology with applications in medicine

Network Modeling of Motor Pathways from Neural Recordings

During cued motor tasks, for both speech and limb movement, information propagates from primary sensory areas, to association areas, to primary and supplementary motor and language areas. Through the recent advent of high density recordings at multiple scales, it has become possible to simultaneously observe activity occurring from these disparate regions at varying resolution. Models of brain activity generally used in brain-computer interface (BCI) control do not take into account the global differences in recording site function, or the interactions between them. Through the use of connectivity measures, however, it has been made possible to determine the contribution of individual recording sites to the global activity, as they vary with task progression. This dissertation extends those connectivity models to provide summary information about the importance of individual sites. This is achieved through the application of network measures on the adjacency structure determined by connectivity measures. Similarly, by analyzing the coordinated activity of all of the electrode sites simultaneously during task performance, it is possible to elucidate discrete functional units through clustering analysis of the electrode recordings. In this dissertation, I first describe a BCI system using simple motor movement imagination at single recording sites. I then incorporate connectivity through the use of TV-DBN modeling on higher resolution electrode recordings, specifically electrocorticography (ECoG). I show that PageRank centrality reveals information about task progression and regional specificity which was obscured by direct application of the connectivity measures, due to the combinatorial increase in feature dimensionality. I then show that clustering of ECoG recordings using a method to determine the inherent cluster count algorithmically provides insight into how network involvement in task execution evolves, though in a manner dependent on grid coverage. Finally, I extend clustering analysis to show how individual neurons in motor cortex form distinct functional communities. These communities are shown to be task-specific, suggesting that neurons can form functional units with distinct neural populations across multiple recording sites in a context dependent impermanent manner. This work demonstrates that network measures of connectivity models of neurophysiological recordings are a rich source of information relevant to the field of neuroscience, as well as offering the promise of improved degree-of-freedom and naturalness possible through direct BCI control. These models are shown to be useful at multiple recording scales, from cortical-area level ECoG, to highly localized single unit microelectrode recordings

Rehabilitation Engineering

Population ageing has major consequences and implications in all areas of our daily life as well as other important aspects, such as economic growth, savings, investment and consumption, labour markets, pensions, property and care from one generation to another. Additionally, health and related care, family composition and life-style, housing and migration are also affected. Given the rapid increase in the aging of the population and the further increase that is expected in the coming years, an important problem that has to be faced is the corresponding increase in chronic illness, disabilities, and loss of functional independence endemic to the elderly (WHO 2008). For this reason, novel methods of rehabilitation and care management are urgently needed. This book covers many rehabilitation support systems and robots developed for upper limbs, lower limbs as well as visually impaired condition. Other than upper limbs, the lower limb research works are also discussed like motorized foot rest for electric powered wheelchair and standing assistance device

Human skill capturing and modelling using wearable devices

Industrial robots are delivering more and more manipulation services in manufacturing. However, when the task is complex, it is difficult to programme a robot to fulfil all the requirements because even a relatively simple task such as a peg-in-hole insertion contains many uncertainties, e.g. clearance, initial grasping position and insertion path. Humans, on the other hand, can deal with these variations using their vision and haptic feedback. Although humans can adapt to uncertainties easily, most of the time, the skilled based performances that relate to their tacit knowledge cannot be easily articulated. Even though the automation solution may not fully imitate human motion since some of them are not necessary, it would be useful if the skill based performance from a human could be firstly interpreted and modelled, which will then allow it to be transferred to the robot. This thesis aims to reduce robot programming efforts significantly by developing a methodology to capture, model and transfer the manual manufacturing skills from a human demonstrator to the robot. Recently, Learning from Demonstration (LfD) is gaining interest as a framework to transfer skills from human teacher to robot using probability encoding approaches to model observations and state transition uncertainties. In close or actual contact manipulation tasks, it is difficult to reliabley record the state-action examples without interfering with the human senses and activities. Therefore, wearable sensors are investigated as a promising device to record the state-action examples without restricting the human experts during the skilled execution of their tasks. Firstly to track human motions accurately and reliably in a defined 3-dimensional workspace, a hybrid system of Vicon and IMUs is proposed to compensate for the known limitations of the individual system. The data fusion method was able to overcome occlusion and frame flipping problems in the two camera Vicon setup and the drifting problem associated with the IMUs. The results indicated that occlusion and frame flipping problems associated with Vicon can be mitigated by using the IMU measurements. Furthermore, the proposed method improves the Mean Square Error (MSE) tracking accuracy range from 0.8˚ to 6.4˚ compared with the IMU only method. Secondly, to record haptic feedback from a teacher without physically obstructing their interactions with the workpiece, wearable surface electromyography (sEMG) armbands were used as an indirect method to indicate contact feedback during manual manipulations. A muscle-force model using a Time Delayed Neural Network (TDNN) was built to map the sEMG signals to the known contact force. The results indicated that the model was capable of estimating the force from the sEMG armbands in the applications of interest, namely in peg-in-hole and beater winding tasks, with MSE of 2.75N and 0.18N respectively. Finally, given the force estimation and the motion trajectories, a Hidden Markov Model (HMM) based approach was utilised as a state recognition method to encode and generalise the spatial and temporal information of the skilled executions. This method would allow a more representative control policy to be derived. A modified Gaussian Mixture Regression (GMR) method was then applied to enable motions reproduction by using the learned state-action policy. To simplify the validation procedure, instead of using the robot, additional demonstrations from the teacher were used to verify the reproduction performance of the policy, by assuming human teacher and robot learner are physical identical systems. The results confirmed the generalisation capability of the HMM model across a number of demonstrations from different subjects; and the reproduced motions from GMR were acceptable in these additional tests. The proposed methodology provides a framework for producing a state-action model from skilled demonstrations that can be translated into robot kinematics and joint states for the robot to execute. The implication to industry is reduced efforts and time in programming the robots for applications where human skilled performances are required to cope robustly with various uncertainties during tasks execution

Intelligent Sensors for Human Motion Analysis

The book, "Intelligent Sensors for Human Motion Analysis," contains 17 articles published in the Special Issue of the Sensors journal. These articles deal with many aspects related to the analysis of human movement. New techniques and methods for pose estimation, gait recognition, and fall detection have been proposed and verified. Some of them will trigger further research, and some may become the backbone of commercial systems

Discriminative dimensionality reduction: variations, applications, interpretations

Schulz A. Discriminative dimensionality reduction: variations, applications, interpretations. Bielefeld: Universität Bielefeld; 2017.The amount of digital data increases rapidly as a result of advances in information and sensor technology. Because the data sets grow with respect to their size, complexity and dimensionality, they are no longer easily accessible to a human user. The framework of dimensionality reduction addresses this problem by aiming to visualize complex data sets in two dimensions while preserving the relevant structure. While these methods can provide significant insights, the problem formulation of structure preservation is ill-posed in general and can lead to undesired effects. In this thesis, the concept of discriminative dimensionality reduction is investigated as a particular promising way to indicate relevant structure by specifying auxiliary data. The goal is to overcome challenges in data inspection and to investigate in how far discriminative dimensionality reduction methods can yield an improvement. The main scientific contributions are the following: (I) The most popular techniques for discriminative dimensionality reduction are based on the Fisher metric. However, they are restricted in their applicability as concerns complex settings: They can only be employed for fixed data sets, i.e. new data cannot be included in an existing embedding. Only data provided in vectorial representation can be processed. And they are designed for discrete-valued auxiliary data and cannot be applied to real-valued ones. We propose solutions to overcome these challenges. (II) Besides the problem that complex data are not accessible to humans, the same holds for trained machine learning models which often constitute black box models. In order to provide an intuitive interface to such models, we propose a general framework which allows to visualize high-dimensional functions, such as regression or classification functions, in two dimensions. (III) Although nonlinear dimensionality reduction techniques illustrate the structure of the data very well, they suffer from the fact that there is no explicit relationship between the original features and the obtained projection. We propose a methodology to create a connection, thus allowing to understand the importance of the features. (IV) Although linear mappings constitute a very popular tool, a direct interpretation of their weights as feature relevance can be misleading. We propose a methodology which enables a valid interpretation by providing relevance bounds for each feature. (V) The problem of transfer learning without given correspondence information between the source and target space and without labels is particularly challenging. Here, we utilize the structure preserving property of dimensionality reduction methods to transfer knowledge in a latent space given by dimensionality reduction

A Brain-computer Interface Architecture Based On Motor Mental Tasks And Music Imagery

This present research proposes a Brain-Computer Interface (BCI) architecture adapted to motor mental tasks and music imagery. For that purpose the statistical properties of the electroencephalographic signal (EEG) were studied, such as its probability distribution function, stationarity, correlation and signal-to-noise ratio (SNR), in order to obtain a minimal empirical and well-founded parameter system for online classification. Stationarity tests were used to estimate the length of the time windows and a minimum length of 1.28 s was obtained. Four algorithms for artifact reduction were tested: threshold analysis, EEG filtering and two Independent Component Analysis (ICA) algorithms. This analysis concluded that the algorithm fastICA is suitable for online artifact removal. The feature extraction used the Power Spectral Density (PSD) and three methods were tested for automatic selection of features in order to have a training step independent of the mental task paradigm, with the best performance obtained with the Kullback-Leibler symmetric divergence method. For the classification, the Linear Discriminant Analysis (LDA) was used and a step of reclassification is suggested. A study of four motor mental tasks and a non-motor related mental task is performed by comparing their periodograms, Event-Related desynchronization/synchronization (ERD/ERS) and SNR. The mental tasks are the imagination of either movement of right and left hands, both feet, rotation of a cube and sound imagery. The EEG SNR was estimated by a comparison with the correlation between the ongoing average and the final ERD/ERS curve, in which we concluded that the mental task of sound imagery would need approximately five times more epochs than the motor-related mental tasks. The ERD/ERS could be measured even for frequencies near 100 Hz, but in absolute amplitudes, the energy variation at 100 Hz was one thousand times smaller than for 10 Hz, which implies that there is a small probability of online detection for BCI applications in high frequency. Thus, most of the usable information for online processing and BCIs corresponds to the α/µ band (low frequency). Finally, the ERD/ERS scalp maps show that the main difference between the sound imagery task and the motor-related mentaltasks is the absence of ERD at the µ band, in the central electrodes, and the presence of ERD at the αband in the temporal and lateral-frontal electrodes, which correspond tothe auditory cortex, the Wernickes area and the Brocas area

Neural coding of grasp force planning and control in macaque areas AIP, F5, and M1

In den letzte Jahrzehnten wurde viel daran geforscht zu entschlüsseln wie das Gehirn Greifbewegungen koordiniert. Das anteriore intraparietale Areal (AIP), das Hand Areal des ventralen premotorischen Kortex (F5), und das Hand Areal des primären motorischen Kortex (M1) wurden als essentielle kortikale Arealen für die Kontrolle der Hand identifiziert. Nichtsdestotrotz ist deutlich weniger darüber bekannt wie die Neuronen dieser Areale einen weiteren essentielle Parameter von Greifbewegungen kodieren: Greifkraft. Insbesondere die Rolle der tertiären, kortikalen Areale AIP und F5 in diesen Prozess ist bisher unklar. Die hier durchgeführte Studie befasst sich mit der Wissenslücke über die neuronale Kodierung von Greifkraft Planung und Steuerung in diesen Arealen. Um dies zu erreichen, haben wir zwei Makaken (Macaca mulatta) trainiert eine verzögerte Greifaufgabe auszuführen mit zwei Grifftypen (ein Griff mit der ganzen Hand oder ein Präzisionsgriff) und mit drei verschiedene Kraftniveaus (0-12 N). Während die Affen die Aufgabe ausführten, haben wir die Aktivität von “single-units“ (einzelnen Neuronen) und “multi-units“ (Gruppen von mehreren Neuronen) in den Arealen AIP, F5 und M1 aufgenommen. Wir berechneten den Prozentsatz von Grifftyp modulierten und Griffkraft modulierten “units“ (cluster-based permutation test) und berechneten wie viel Varianz in der Population von “units“ durch Grifftyp und Kraft erklärbar ist, separat für jedes Gehirn Areal mit einer modernen Dimensionalitätsreduktionsanalyse (demixed principal component analysis). 18 Wir zeigen hier zum ersten Mal die Modulation von einzelnen AIP Neuronen durch Greifkraft. Weiterhin bestätigen und erweitern wir hier vorherige Ergebnisse, welche solche neuronale Modulationen bereits in F5 und M1 gezeigt haben. Überaschenderweise war der Prozentsatz von “units“ welche durch Griffkraft moduliert werden, in AIP und F5 nicht wesentlich kleiner als in M1 und ähnlich zu dem Prozentsatz an Grifftyp modulierte Neuronen. Der Anteil an erklärte Varianz in F5 durch Greifkraft war nahezu so groß, wie der Anteil erklärt durch Grifftyp. In AIP und M1 war klar mehr Varianz durch Grifftyp erklärt als durch Kraft, aber der Anteil an erklärte Varianz beider Arealen war ausreichend, um zuverlässig Kraftbedingung zu dekodieren. Wir fanden ebenfalls eine starke neuronale Modulation für Griffkraftbedingungen vor der Bewegungsinitiierung in F5, was wahrscheinlich eine Rolle dieses Areals in der Greifkraftplanung repräsentiert. In AIP war Greifkraftplanungsaktivität nur in einen der beiden Affen vorhanden und wie erwartet nicht präsent in M1 (gemessen nur in einen Affen). Letztendlich, obwohl Greifkraftmodulation in einigen Fällen durch Grifftypmodulation beeinflusst war, war nur ein kleiner Anteil der Populationsvarianz, in den jeweiligen Arealen, durch interaktive Modulation erklärt. Information über Greifkraft können somit folglich separat vom Grifftyp extrahiert werden. Diese Ergebnisse legen eine wichtige Rolle von AIP und F5 bei der Greifkraftkontrolle, neben M1, nah. F5 ist mit hoher Wahrscheinlichkeit auch bei der Planung von Greifkraft involviert, während die Rolle von AIP und M1 geringer ist in diesem Prozess. Letztendlich, da Grifftyp- und Kraftinformation separat extrahierbar sind, zeigen diese Ergebnisse, dass Greifkraft vermutlich unabhängig von Grifftyp, im kortikalen Greifnetzwerk kodiert ist.In de laatste decennia is er veel onderzoek gedaan om te interpreteren hoe de hersenen grijpbewegingen besturen. Het anterieure intra pariëtale gebied (AIP), het handgebied van de ventrale premotorische schors (F5) en het handgebied van de primaire motorische schors (M1) zijn geïdentificeerd als essentiële gebieden van de hersenschors die de vorm van de hand besturen. Maar er is veel minder bekend over hoe de hersenen een andere parameter van grijpbewegingen bestuurt: grijpkracht. Vooral de rol in dit proces van AIP en F5, gebieden van hogere orde, is nog nagenoeg onbekend. Deze studie richt zich op het gebrek aan kennis over de neurale codering van het plannen en besturen van grijpkracht. Om dit te bereiken, hebben we twee makaken (Macaca mulatta) getraind om een vertraagde grijptaak uit te voeren met twee grepen van de hand (een grip met de hele hand of een precisie grip) en met drie verschillende krachtniveaus (0-12 N). Terwijl de apen de taak uitvoerden, maten we de activiteit van single-units (individuele neuronen) en multiunits (collectie van enkele neuronen) in de gebieden AIP, F5 en M1. We berekenden het percentage van units die hun activiteit moduleerden op basis van grip vorm of kracht met een moderne statistieke test (cluster-based permutation test) en we berekenden de hoeveelheid variantie die werd verklaard door de grip vorm en kracht door de populatie van units van elk hersengebied met een moderne dimensie vermindering techniek (demixed principal component analysis). We laten hier voor het eerst zien dat individuele neuronen van AIP hun activiteit moduleren op basis van grijpkracht. Verder bevestigen we dat neuronen van F5 en M1 20 dergelijke modulaties vertonen en breiden we de kennis hierover uit. Verassend genoeg was het percentage units dat reageert op het besturen van grijpkracht in AIP en F5 niet veel lager dan in M1 en ongeveer gelijk aan de hoeveelheid units dat reageert op grip vorm. De hoeveelheid variantie die werd verklaard door grijpkracht in F5 was bijna net zo hoog als wat werd verklaard door grip vorm. In AIP en M1 verklaarde grip vorm duidelijk meer variantie dan grijpkracht, maar ook in deze gebieden was de hoeveelheid variantie dat grijpkracht verklaarde hoog genoeg om de kracht conditie te decoderen. We vonden ook een sterke neurale modulatie voor grijpkracht condities in F5 voordat de arm bewoog, wat mogelijk een rol voor dit gebied representeert in het plannen van grijpkracht. In AIP was activiteit voor het plannen van grijpkracht alleen in één van beide apen gevonden en zoals verwacht was het niet gevonden in M1 (onderzocht in één aap). Tenslotte vonden we dat, hoewel modulatie voor kracht werd beïnvloedt door grip vorm in sommige eenheden, slechts een kleine fractie van de variantie van de neurale populatie van elk hersengebied een gemixte selectiviteit voor grip vorm en kracht had. Informatie over grijpkracht kon daarom onafhankelijk van grip vorm worden geëxtraheerd. Deze bevindingen suggereren een belangrijke rol voor AIP en F5 in het besturen van grijpkracht, samen met M1. F5 is waarschijnlijk ook betrokken met het plannen van grijpkracht, terwijl AIP en M1 waarschijnlijk een kleinere rol hebben in dit proces. Tenslotte, omdat informatie over grip vorm en grijpkracht onafhankelijk konden worden geëxtraheerd, laten deze resultaten zien dat grijpkracht vermoedelijk onafhankelijk van hand vorm is gecodeerd in het grijpnetwerk van de hersenschors