Electromyogram (EMG) Removal by Adding Sources of EMG (ERASE) -- A novel ICA-based algorithm for removing myoelectric artifacts from EEG -- Part 2

Extraction of the movement-related high-gamma (80 - 160 Hz) in electroencephalogram (EEG) from traumatic brain injury (TBI) patients who have had hemicraniectomies, remains challenging due to a confounding bandwidth overlap with surface electromyogram (EMG) artifacts related to facial and head movements. In part 1, we described an augmented independent component analysis (ICA) approach for removal of EMG artifacts from EEG, and referred to as EMG Reduction by Adding Sources of EMG (ERASE). Here, we tested ERASE on EEG recorded from six TBI patients with hemicraniectomies while they performed a thumb flexion task. ERASE removed a mean of 52 +/- 12% (mean +/- S.E.M) (maximum 73%) of EMG artifacts. In contrast, conventional ICA removed a mean of 27 +/- 19\% (mean +/- S.E.M) of EMG artifacts from EEG. In particular, high-gamma synchronization was significantly improved in the contralateral hand motor cortex area within the hemicraniectomy site after ERASE was applied. We computed fractal dimension (FD) of EEG high-gamma on each channel. We found relative FD of high-gamma over hemicraniectomy after applying ERASE were strongly correlated to the amplitude of finger flexion force. Results showed that significant correlation coefficients across the electrodes related to thumb flexion averaged 0.76, while the coefficients across the homologous electrodes in non-hemicraniectomy areas were nearly 0. Across all subjects, an average of 83% of electrodes significantly correlated with force was located in the hemicraniectomy areas after applying ERASE. After conventional ICA, only 19% of electrodes with significant correlations were located in the hemicraniectomy. These results indicated that the new approach isolated electrophysiological features during finger motor activation while selectively removing confounding EMG artifacts

Towards a synergy framework across neuroscience and robotics: Lessons learned and open questions. Reply to comments on: "Hand synergies: Integration of robotics and neuroscience for understanding the control of biological and artificial hands"

We would like to thank all commentators for their insightful commentaries. Thanks to their diverse and complementary expertise in neuroscience and robotics, the commentators have provided us with the opportunity to further discuss state-of-the-art and gaps in the integration of neuroscience and robotics reviewed in our article. We organized our reply in two sections that capture the main points of all commentaries [1–9]: (1) Advantages and limitations of the synergy approach in neuroscience and robotics, and (2) Learning and role of sensory feedback in biological and robotics synergies

A synergy-based hand control is encoded in human motor cortical areas

How the human brain controls hand movements to carry out different tasks is still debated. The concept of synergy has been proposed to indicate functional modules that may simplify the control of hand postures by simultaneously recruiting sets of muscles and joints. However, whether and to what extent synergic hand postures are encoded as such at a cortical level remains unknown. Here, we combined kinematic, electromyography, and brain activity measures obtained by functional magnetic resonance imaging while subjects performed a variety of movements towards virtual objects. Hand postural information, encoded through kinematic synergies, were represented in cortical areas devoted to hand motor control and successfully discriminated individual grasping movements, significantly outperforming alternative somatotopic or muscle-based models. Importantly, hand postural synergies were predicted by neural activation patterns within primary motor cortex. These findings support a novel cortical organization for hand movement control and open potential applications for brain-computer interfaces and neuroprostheses

A synergy-based hand control is encoded in human motor cortical areas

abstract: How the human brain controls hand movements to carry out different tasks is still debated. The concept of synergy has been proposed to indicate functional modules that may simplify the control of hand postures by simultaneously recruiting sets of muscles and joints. However, whether and to what extent synergic hand postures are encoded as such at a cortical level remains unknown. Here, we combined kinematic, electromyography, and brain activity measures obtained by functional magnetic resonance imaging while subjects performed a variety of movements towards virtual objects. Hand postural information, encoded through kinematic synergies, were represented in cortical areas devoted to hand motor control and successfully discriminated individual grasping movements, significantly outperforming alternative somatotopic or muscle-based models. Importantly, hand postural synergies were predicted by neural activation patterns within primary motor cortex. These findings support a novel cortical organization for hand movement control and open potential applications for brain-computer interfaces and neuroprostheses

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

A neurophysiological examination of voluntary isometric contractions: modulations in sensorimotor oscillatory dynamics with contraction force and physical fatigue, and peripheral contributions to maximal force production

Human motor control is a complex process involving both central and peripheral components of the nervous system. Type Ia afferent input contributes to both motor unit recruitment and firing frequency, however, whether maximal force production is dependent on this input is unclear. Therefore, chapter 2 examined maximal and explosive force production of the knee extensors following prolonged infrapatellar tendon vibration; designed to attenuate the efficacy of the homonymous Ia afferent-α-motoneuron pathway. Despite a marked decrease in H-reflex amplitude, indicating an attenuated efficacy of the Ia afferent-α-motoneuron pathway, both maximal and explosive force production were unaffected after vibration. This suggested that maximal and explosive isometric quadriceps force production was not dependent upon Ia afferent input to the homonymous motor unit pool. Voluntary movements are linked with various modulations in ongoing neural oscillations within the supraspinal sensorimotor system. Despite considerable interest in the oscillatory responses to movements per se, the influence of the motor parameters that define these movements is poorly understood. Subsequently, chapters 3 and 4 investigated how the motor parameters of voluntary contractions modulated the oscillatory amplitude. Chapter 3 recorded electroencephalography from the leg area of the primary sensorimotor cortex in order to investigate the oscillatory responses to isometric unilateral contractions of the knee-extensors at four torque levels (15, 30, 45 and 60% max.). An increase in movement-related gamma (30-50 Hz) activity was observed with increments in knee-extension torque, whereas oscillatory power within the delta (0.5-3 Hz), theta (3-7 Hz), alpha (7-13 Hz) and beta (13-30 Hz) bands were unaffected. Chapter 4 examined the link between the motor parameters of voluntary contraction and modulations in beta (15-30 Hz) oscillations; specifically, movement-related beta decrease (MRBD) and post-movement beta rebound (PMBR). Magnetoencephalography (MEG) was recorded during isometric ramp and constant-force wrist-flexor contractions at distinct rates of force development (10.4, 28.9 and 86.7% max./s) and force output (5, 15, 35 and 60%max.), respectively. MRBD was unaffected by RFD or force output, whereas systematic modulation of PMBR by both contraction force and RFD was identified for the first time. Specifically, increments in isometric contraction force increased PMBR amplitude, and increments in RFD increased PMBR amplitude but decreased PMBR duration. Physical fatigue arises not only from peripheral processes within the active skeletal muscles but also from supraspinal mechanisms within the brain. However, exactly how cortical activity is modulated during fatigue has received a paucity of attention. Chapter 5 investigated whether oscillatory activity within the primary sensorimotor cortex was modulated when contractions were performed in a state of physical fatigue. MEG was recorded during submaximal isometric contractions of the wrist-flexors performed both before and after a fatiguing series of isometric wrist-flexions or a time matched control intervention. Physical fatigue offset the attenuation in MRBD observed during the control trial, whereas PMBR was increased when submaximal contractions were performed in a fatigued state

Extracting kinetic information from human motor cortical signals.

Brain machine interfaces (BMIs) have the potential to provide intuitive control of neuroprostheses to restore grasp to patients with paralyzed or amputated upper limbs. For these neuroprostheses to function, the ability to accurately control grasp force is critical. Grasp force can be decoded from neuronal spikes in monkeys, and hand kinematics can be decoded using electrocorticogram (ECoG) signals recorded from the surface of the human motor cortex. We hypothesized that kinetic information about grasping could also be extracted from ECoG, and sought to decode continuously-graded grasp force. In this study, we decoded isometric pinch force with high accuracy from ECoG in 10 human subjects. The predicted signals explained from 22% to 88% (60 ± 6%, mean ± SE) of the variance in the actual force generated. We also decoded muscle activity in the finger flexors, with similar accuracy to force decoding. We found that high gamma band and time domain features of the ECoG signal were most informative about kinetics, similar to our previous findings with intracortical LFPs. In addition, we found that peak cortical representations of force applied by the index and little fingers were separated by only about 4mm. Thus, ECoG can be used to decode not only kinematics, but also kinetics of movement. This is an important step toward restoring intuitively-controlled grasp to impaired patients