Publications

Journal article

Niazi IK, Navid MS, Rashid U, Amjad I, Olsen S, Haavik H, Alder G, Kumari N, Signal N, Taylor D, Farina D, Jochumsen Met al., 2022,

Associative cued asynchronous BCI induces cortical plasticity in stroke patients.

, Ann Clin Transl Neurol, Vol: 9, Pages: 722-733

OBJECTIVE: We propose a novel cue-based asynchronous brain-computer interface(BCI) for neuromodulation via the pairing of endogenous motor cortical activity with the activation of somatosensory pathways. METHODS: The proposed BCI detects the intention to move from single-trial EEG signals in real time, but, contrary to classic asynchronous-BCI systems, the detection occurs only during time intervals when the patient is cued to move. This cue-based asynchronous-BCI was compared with two traditional BCI modes (asynchronous-BCI and offline synchronous-BCI) and a control intervention in chronic stroke patients. The patients performed ankle dorsiflexion movements of the paretic limb in each intervention while their brain signals were recorded. BCI interventions decoded the movement attempt and activated afferent pathways via electrical stimulation. Corticomotor excitability was assessed using motor-evoked potentials in the tibialis-anterior muscle induced by transcranial magnetic stimulation before, immediately after, and 30 min after the intervention. RESULTS: The proposed cue-based asynchronous-BCI had significantly fewer false positives/min and false positives/true positives (%) as compared to the previously developed asynchronous-BCI. Linear-mixed-models showed that motor-evoked potential amplitudes increased following all BCI modes immediately after the intervention compared to the control condition (p <0.05). The proposed cue-based asynchronous-BCI resulted in the largest relative increase in peak-to-peak motor-evoked potential amplitudes(141% ± 33%) among all interventions and sustained it for 30 min(111% ± 33%). INTERPRETATION: These findings prove the high performance of a newly proposed cue-based asynchronous-BCI intervention. In this paradigm, individuals receive precise instructions (cue) to promote engagement, while the timing of brain activity is accurately detected to establish a precise ass

Journal article

Bracklein M, Barsakcioglu DY, Del Vecchio A, Ibanez J, Farina Det al., 2022,

Reading and modulating cortical beta bursts from motor unit spiking activity

, JOURNAL OF NEUROSCIENCE, Vol: 42, ISSN: 0270-6474

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Citations: 9

Journal article

Thompson CK, Johnson MD, Negro F, Farina D, Heckman CJet al., 2022,

Motor Unit Discharge Patterns in Response to Focal Tendon Vibration of the Lower Limb in Cats and Humans

, Frontiers in Integrative Neuroscience, Vol: 16, ISSN: 1662-5145

High-frequency vibration of the tendon provides potent activation of Ia afferents time-locked to the stimulation frequency and provides excitatory ionotropic activation of homonymous motor pools. In cats, the evoked motor unit discharge is constrained to discharge at integer multiples of the vibration frequency, resulting in a probability of discharge that is highly punctuated. Here we quantify the robustness of this punctuated response in the cat and evaluate whether it is present in the human. Soleus electromyography (EMG) was collected from eight cats using 64 channel electrodes during three modes of motoneuron activation. First, tendon vibration parameters were modified. Second, secondary reflex inputs are applied concurrently with tendon vibration. Third, the state of the spinal cord was altered through pharmacological or surgical manipulations. Analogous surface high-density EMG was collected from the lower leg of six humans during both vibration evoked and matched volitional contractions. Array EMG signals from both the cat and human were decomposed into corresponding motor unit action potential spike trains, and the punctuation in discharge was quantified. In the cat, regardless of vibration parameters, secondary synaptic drive, and state of spinal circuitry, focal tendon vibration evoked punctuated motor unit discharge. However, in the human lower limb, the vibration-evoked contractions do not produce punctuated motor unit discharge.

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Citations: 2

Journal article

Mendez Guerra I, Barsakcioglu DY, Vujaklija I, Wetmore DZ, Farina Det al., 2022,

Far-field electric potentials provide access to the output from the spinal cord from wrist-mounted sensors

, Journal of Neural Engineering, Vol: 19, ISSN: 1741-2552

OBJECTIVE: Neural interfaces need to become more unobtrusive and socially acceptable to appeal to general consumers outside rehabilitation settings. APPROACH: We developed a non-invasive neural interface that provides access to spinal motor neuron activities from the wrist, which is the preferred location for a wearable. The interface decodes far-field potentials present at the tendon endings of the forearm muscles using blind source separation. First, we evaluated the reliability of the interface to detect motor neuron firings based on far-field potentials, and thereafter we used the decoded motor neuron activity for the prediction of finger contractions in offline and real-time conditions. MAIN RESULTS: The results showed that motor neuron activity decoded from the far-field potentials at the wrist accurately predicted individual and combined finger commands and therefore allowed for highly accurate real-time task classification. SIGNIFICANCE: These findings demonstrate the feasibility of a non-invasive, neural interface at the wrist for precise real-time control based on the output of the spinal cord.

Journal article

Eden J, Bräcklein M, Ibáñez J, Barsakcioglu DY, Di Pino G, Farina D, Burdet E, Mehring Cet al., 2022,

Principles of human movement augmentation and the challenges in making it a reality

, Nature Communications, Vol: 13, ISSN: 2041-1723

Augmenting the body with artificial limbs controlled concurrently to one's natural limbs has long appeared in science fiction, but recent technological and neuroscientific advances have begun to make this possible. By allowing individuals to achieve otherwise impossible actions, movement augmentation could revolutionize medical and industrial applications and profoundly change the way humans interact with the environment. Here, we construct a movement augmentation taxonomy through what is augmented and how it is achieved. With this framework, we analyze augmentation that extends the number of degrees-of-freedom, discuss critical features of effective augmentation such as physiological control signals, sensory feedback and learning as well as application scenarios, and propose a vision for the field.

Journal article

Puttaraksa G, Muceli S, Barsakcioglu DY, Holobar A, Clarke AK, Charles SK, Pons JL, Farina Det al., 2022,

Online tracking of the phase difference between neural drives to antagonist muscle pairs in essential tremor patients

, IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol: 30, Pages: 709-718, ISSN: 1534-4320

Transcutaneous electrical stimulation has been applied in tremor suppression applications. Out-of-phase stimulation strategies applied above or below motor threshold result in a significant attenuation of pathological tremor. For stimulation to be properly timed, the varying phase relationship between agonist-antagonist muscle activity during tremor needs to be accurately estimated in real-time. Here we propose an online tremor phase and frequency tracking technique for the customized control of electrical stimulation, based on a phase-locked loop (PLL) system applied to the estimated neural drive to muscles. Surface electromyography signals were recorded from the wrist extensor and flexor muscle groups of 13 essential tremor patients during postural tremor. The EMG signals were pre-processed and decomposed online and offline via the convolution kernel compensation algorithm to discriminate motor unit spike trains. The summation of motor unit spike trains detected for each muscle was bandpass filtered between 3 to 10 Hz to isolate the tremor related components of the neural drive to muscles. The estimated tremorogenic neural drive was used as input to a PLL that tracked the phase differences between the two muscle groups. The online estimated phase difference was compared with the phase calculated offline using a Hilbert Transform as a ground truth. The results showed a rate of agreement of 0.88 ± 0.22 between offline and online EMG decomposition. The PLL tracked the phase difference of tremor signals in real-time with an average correlation of 0.86 ± 0.16 with the ground truth (average error of 6.40° ± 3.49°). Finally, the online decomposition and phase estimation components were integrated with an electrical stimulator and applied in closed-loop on one patient, to representatively demonstrate the working principle of the full tremor suppression system. The results of this study support the feasibility of real-time estimation of the pha

Journal article

Ghaderi P, Nosouhi M, Jordanic M, Marateb HR, Mañanas MA, Farina Det al., 2022,

Kernel Density Estimation of Electromyographic Signals and Ensemble Learning for Highly Accurate Classification of a Large Set of Hand/Wrist Motions

, Frontiers in Neuroscience, Vol: 16, ISSN: 1662-4548

The performance of myoelectric control highly depends on the features extracted from surface electromyographic (sEMG) signals. We propose three new sEMG features based on the kernel density estimation. The trimmed mean of density (TMD), the entropy of density, and the trimmed mean absolute value of derivative density were computed for each sEMG channel. These features were tested for the classification of single tasks as well as of two tasks concurrently performed. For single tasks, correlation-based feature selection was used, and the features were then classified using linear discriminant analysis (LDA), non-linear support vector machines, and multi-layer perceptron. The eXtreme gradient boosting (XGBoost) classifier was used for the classification of two movements simultaneously performed. The second and third versions of the Ninapro dataset (conventional control) and Ameri’s movement dataset (simultaneous control) were used to test the proposed features. For the Ninapro dataset, the overall accuracy of LDA using the TMD feature was 98.99 ± 1.36% and 92.25 ± 9.48% for able-bodied and amputee subjects, respectively. Using ensemble learning of the three classifiers, the average macro and micro-F-score, macro recall, and precision on the validation sets were 98.23 ± 2.02, 98.32 ± 1.93, 98.32 ± 1.93, and 98.88 ± 1.31%, respectively, for the intact subjects. The movement misclassification percentage was 1.75 ± 1.73 and 3.44 ± 2.23 for the intact subjects and amputees. The proposed features were significantly correlated with the movement classes [Generalized Linear Model (GLM); P-value < 0.05]. An accurate online implementation of the proposed algorithm was also presented. For the simultaneous control, the overall accuracy was 99.71 ± 0.08 and 97.85 ± 0.10 for the XGBoost and LDA classifiers, respectively. The proposed features are thus promising for conventional and simultaneous myoelectric

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Citations: 4

Journal article

Sturma A, Stamm T, Hruby LA, Bischof B, Salminger S, Gstoettner C, Prahm C, Pittermann A, Wakolbinger R, Hofer C, Farina D, Aszmann OCet al., 2022,

Rehabilitation of high upper limb amputees after Targeted Muscle Reinnervation

, Journal of Hand Therapy, Vol: 35, Pages: 58-66, ISSN: 0894-1130

STUDY DESIGN: This is a Delphi study based on a scoping literature review. INTRODUCTION: Targeted muscle reinnervation (TMR) enables patients with high upper limb amputations to intuitively control a prosthetic arm with up to six independent control signals. Although there is a broad agreement regarding the importance of structured motor learning and prosthetic training after such nerve transfers, to date, no evidence-based protocol for rehabilitation after TMR exists. PURPOSE OF THE STUDY: We aimed at developing a structured rehabilitation protocol after TMR surgery after major upper limb amputation. The purpose of the protocol is to guide clinicians through the full rehabilitation process, from presurgical patient education to functional prosthetic training. METHODS: European clinicians and researchers working in upper limb prosthetic rehabilitation were invited to contribute to a web-based Delphi study. Within the first round, clinical experts were presented a summary of recent literature and were asked to describe the rehabilitation steps based on their own experience and scientific evidence. The second round was used to refine these steps, while the importance of each step was rated within the third round. RESULTS: Experts agreed on a rehabilitation protocol that consists of 16 steps and starts before surgery. It is based on two overarching principles, namely the necessity of multiprofessional teamwork and a careful selection and education of patients within the rehabilitation team. Among the different steps in therapy, experts rated the training with electromyographic biofeedback as the most important one. DISCUSSION: Within this study, a first rehabilitation protocol for TMR patients based on a broad experts' consensus and relevant literature could be developed. The detailed steps for rehabilitation start well before surgery and prosthetic fitting, and include relatively novel interventions as motor imagery and biofeedback. Future studies need to further inve

Journal article

Ma S, Chen C, Zhao J, Han D, Sheng X, Farina D, Zhu Xet al., 2022,

Analytical Modelling of Surface EMG Signals Generated by Curvilinear Fibers With Approximate Conductivity Tensor

, IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, Vol: 69, Pages: 1052-1062, ISSN: 0018-9294

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Citations: 1

Journal article

Ernst J, Weiss T, Wanke N, Frahm J, Felmerer G, Farina D, Schilling AF, Wilke MAet al., 2022,

Case Report: Plasticity in Central Sensory Finger Representation and Touch Perception After Microsurgical Reconstruction of Infraclavicular Brachial Plexus Injury

, FRONTIERS IN NEUROSCIENCE, Vol: 16

Journal article

de Oliveira DS, Casolo A, Balshaw TG, Maeo S, Lanza MB, Martin NRW, Maffulli N, Kinfe TM, Eskofier B, Folland JP, Farina D, Del Vecchio Aet al., 2022,

Neural decoding from surface high-density EMG signals: influence of anatomy and synchronization on the number of identified motor units

<jats:title>Abstract</jats:title><jats:sec><jats:title>Objective</jats:title><jats:p>High-density surface electromyography (HD-sEMG) allows the reliable identification of individual motor unit (MU) action potentials. Despite the accuracy in decomposition, there is a large variability in the number of identified MUs across individuals and exerted forces. Here we present a systematic investigation of the anatomical and neural factors that determine this variability.</jats:p></jats:sec><jats:sec><jats:title>Approach</jats:title><jats:p>We investigated factors of influence on HD-sEMG decomposition, such as synchronization of MU discharges, distribution of MU territories, muscle-electrode distance (MED - subcutaneous fat thickness), maximum anatomical cross-sectional area (ACSA<jats:sub>max</jats:sub>), and fiber CSA. For this purpose, we recorded HD-sEMG signals, ultrasound, magnetic resonance imaging, and muscle biopsy of the biceps brachii muscle from two groups of participants – untrained-controls (UT=14) and strength-trained (>3 years of training, ST=16) – while they performed isometric ramp contractions with elbow flexors (at 15, 35, 50 and 70% maximum voluntary torque - MVT). We assessed the correlation between the number of accurately detected MUs by HD-sEMG decomposition and each measured parameter, for each target force level. Multiple regression analysis was then applied.</jats:p></jats:sec><jats:sec><jats:title>Main results</jats:title><jats:p>ST subjects showed lower MED (UT: 4.8 ± 1.4 vs. ST: 3.7 ± 0.8 mm) associated to a greater number of identified motor units (UT: 21.3 ± 10.2 vs. ST: 29.2 ± 11.8 MUs/subject). Both groups showed a negative correlation between MED and the number of identified MUs at low forces (r= −0.6, p=0.002 at 15% MVT). Moreover, the number of identified MUs was pos

Journal article

Yu T, Akhmadeev K, Le Carpentier E, Aoustin Y, Farina Det al., 2022,

Highly Accurate Real-Time Decomposition of Single Channel Intramuscular EMG

, IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, Vol: 69, Pages: 746-757, ISSN: 0018-9294

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Citations: 1

Book chapter

Nowak M, Vujaklija I, Castellini C, Farina Det al., 2022,

Highly Intuitive 3-DOF Simultaneous and Proportional Myocontrol of Wrist and Hand

, Biosystems and Biorobotics, Pages: 377-382

While simultaneous and proportional activation of multiple degrees of freedom (DOFs) is supported by novel prosthetic hands, there are still no commercial controllers to appropriately enable it. Here, we test a ridge regression based myocontrol method in two real-time scenarios: 13 subjects with an extended high-density EMG electrode set (192 channels) and 4 subjects with a reduced set of electrodes (16 channels). In each scenario, the algorithm was trained on 3 repetitions of single DOF motions (rest, wrist flexion/extension and rotation, and hand closing) and then subjects were asked to reach 24 on-screen goals consisting of one-DOF, two-DOF, and three-DOF targets. The results showed that participants were able to reach all types of targets and that their one-DOF success rate remained high despite the simultaneous control of multiple DoFs (95.9 ± 5.7 %). Moreover, the performance did not significantly change when reducing the number of electrodes (97.6 ± 4.5 % for 16 channels).

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Journal article

Del Vecchio A, Casolo A, Dideriksen JL, Aagaard P, Felici F, Falla D, Farina Det al., 2022,

Lack of increased rate of force development after strength training is explained by specific neural, not muscular, motor unit adaptations

, JOURNAL OF APPLIED PHYSIOLOGY, Vol: 132, Pages: 84-94, ISSN: 8750-7587

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Citations: 6

Journal article

Yao L, Jiang N, Mrachacz-Kersting N, Zhu X, Farina D, Wang Yet al., 2022,

Performance Variation of a Somatosensory BCI Based on Imagined Sensation: A Large Population Study

, IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING, Vol: 30, Pages: 2486-2493, ISSN: 1534-4320

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Citations: 6

Journal article

Dideriksen J, Markovic M, Lemling S, Farina D, Dosen Set al., 2022,

Electrotactile and Vibrotactile Feedback Enable Similar Performance in Psychometric Tests and Closed-Loop Control.

, IEEE Trans Haptics, Vol: 15, Pages: 222-231

Electro- and vibro-tactile stimulation are commonly employed for feedback in closed-loop human-machine interfacing. Although these feedback systems have been extensively investigated individually, they are rarely objectively compared. In this study, two state-of-the-art stimulation units (concentric electrode and C2-tactor) similar in shape and size were compared in psychometric and online control tests. The just noticeable difference and number of discriminable levels for intensity and frequency modulation were determined across values of carrier frequency and intensity, respectively. Next, subjects performed a compensatory tracking task, in which the feedback encoded the momentary tracking error. In the psychometric tests, intensity modulation outperformed frequency modulation and electrotactile stimulation enabled significantly higher resolution than vibrotactile stimulation, for the same carrier frequency. However, for the best-case settings (eletro-tactile: 100 Hz; vibro-tactile: 200 Hz), the two stimulation modalities were equivalent in the psychometric tests and in the online control tests, where the two stimulation methods resulted in similar correlation and deviation between the target and the generated trajectory. Time delay was slightly but significantly lower for the vibrotactile modality. Overall, the present assessment shows that despite psychometric differences between the two stimulation methods, they enable similar online control performance when parameters are optimally selected for each modality.

Journal article

Ting JE, Del Vecchio A, Sarma D, Verma N, Colachis SC, Annetta N, Collinger JL, Farina D, Weber DJet al., 2021,

Sensing and decoding the neural drive to paralyzed muscles during attempted movements of a person with tetraplegia using a sleeve array

, JOURNAL OF NEUROPHYSIOLOGY, Vol: 126, Pages: 2104-2118, ISSN: 0022-3077

Journal article

Jiang X, Liu X, Fan J, Ye X, Dai C, Clancy EA, Farina D, Chen Wet al., 2021,

Enhancing IoT security via cancelable HD-sEMG-based biometric authentication password, encoded by gesture

, IEEE Internet of Things Journal, Vol: 8, Pages: 16535-16547, ISSN: 2327-4662

Enhancing information security via reliable user authentication in wireless body area network (WBAN)-based Internet of Things (IoT) applications has attracted increasing attention. The noncancelability of traditional biometrics (e.g. fingerprint) for user authentication increases the privacy disclosure risks once the biometric template is exposed, because users cannot volitionally create a new template. In this work, we propose a cancelable biometric modality based on high-density surface electromyogram (HD-sEMG) encoded by hand gesture password, for user authentication. HD-sEMG signals (256 channels) were acquired from the forearm muscles when users performed a prescribed gesture password, forming their biometric token. Thirty four alternative hand gestures in common daily use were studied. Moreover, to reduce the data acquisition and transmission burden in IoT devices, an automatically generated password-specific channel mask was employed to reduce the number of active channels. HD-sEMG biometrics were also robust with reduced sampling rate, further reducing power consumption. HD-sEMG biometrics achieved a low equal error rate (EER) of 0.0013 when impostors entered a wrong gesture password, as validated on 20 subjects. Even if impostors entered the correct gesture password, the HD-sEMG biometrics still achieved an EER of 0.0273. If the HD-sEMG biometric template was exposed, users could cancel it by simply changing it to a new gesture password, with an EER of 0.0013. To the best of our knowledge, this is the first study to employ HD-sEMG signals under common daily hand gestures as biometric tokens, with training and testing data acquired on different days.

Journal article

Casolo A, Del Vecchio A, Balshaw TG, Maeo S, Lanza MB, Felici F, Folland JP, Farina Det al., 2021,