Dario Farina

Dario Farina (Fellow’18) is a full professor and Chair in Neurorehabilitation Engineering at the Department of Bioengineering of Imperial College London, UK. He was full professor at Aalborg University, Denmark (until 2010) and at the University Medical Center Göttingen, Georg-August University, Germany, where he founded and directed the Department of Neurorehabilitation Systems (2010-2016), acting as the Chair in Neuroinformatics of the Bernstein Focus Neurotechnology Göttingen. He is currently the Editor-in- Chief of the Journal of Electromyography and Kinesiology, and Associated Editor for IEEE Transactions on Biomedical Engineering and the Journal of Physiology.

Associated articles

TBME, Featured Articles
Decoding Covert Somatosensory Attention by a BCI System Calibrated With Tactile Sensation
Objective: We propose a novel calibration strategy to facilitate the decoding of covert somatosensory attention by exploring the oscillatory dynamics induced by tactile sensation. Methods: It was hypothesized that the similarity of the oscillatory pattern between stimulation sensation (SS, real... Read more
TBME, Featured Articles
Neural Data-Driven Musculoskeletal Modeling for Personalized Neurorehabilitation Technologies
The development of personalized neurorehabilitation and augmentation technologies requires the profound understanding of the neuro-mechanical processes underlying an individual’s motor function, impairment, and recovery. A major challenge is the difficulty of accessing the in vivo neural activity underlying human movement... Read more
TNSRE, Featured Articles
Endogenous Sensory Discrimination and Selection by a Fast Brain Switch for a High Transfer Rate Brain-Computer Interface
In this study, we present a novel multi-class brain-computer interface (BCI) system for communication and control. In this system, the information processing is shared by the algorithm (computer) and the user (human). Specifically, an electro-tactile cycle was presented to the... Read more
TNSRE, Featured Articles
Multichannel Electrotactile Feedback With Spatial and Mixed Coding for Closed-Loop Control of Grasping Force in Hand Prostheses
Providing somatosensory feedback to the user of a myoelectric prosthesis is an important goal since it can improve the utility as well as facilitate the embodiment of the assistive system. Most often, the grasping force was selected as the feedback... Read more
TNSRE, Featured Articles
Psychophysical Evaluation of Subdermal Electrical Stimulation in Relation to Prosthesis Sensory Feedback
      This study evaluated the psychophysical properties of subdermal electrical stimulation to investigate its feasibility in providing sensory feedback for limb prostheses. The detection threshold (DT), pain threshold (PT), just noticeable difference (JND), as well as the elicited sensation quality, comfort,... Read more
TNSRE, Featured Articles
The Extraction of Neural Information from the Surface EMG for the Control of Upper-Limb Prostheses: Emerging Avenues and Challenges
Farina, D. ; Jiang, N. ; Rehbaum, H. ; Holobar, A. ; Graimann, B. ; Dietl, H. ; Aszmann, O.C. Abstract Despite not recording directly from neural cells, the surface electromyogram (EMG) signal contains information on the neural drive to muscles, i.e,... Read more
TNSRE, Featured Articles
A Stimulus-Independent Hybrid BCI Based on Motor Imagery and Somatosensory Attentional Orientation
       Distinctive EEG signals from the motor and somatosensory cortex are generated during mental tasks of motor imagery (MI) and somatosensory attentional orientation (SAO). In this study, we hypothesize that a combination of these two signal modalities provides improvements in BCI... Read more
TNSRE, Featured Articles
Discriminative Manifold Learning Based Detection of Movement-Related Cortical Potentials
The detection of voluntary motor intention from EEG has been applied to closed-loop brain–computer interfacing (BCI). The movement-related cortical potential (MRCP) is a low frequency component of the EEG signal, which represents movement intention, preparation, and execution. In this study,... Read more
TBME,
Neural Data-Driven Musculoskeletal Modeling for Personalized Neurorehabilitation Technologies
This review aims to discuss clinically viable methods for accessing the neural information underlying an individual’s movement from electrophysiological recordings and the development of subject-specific musculoskeletal modeling formulations that can be driven by the extracted neural features... Read more
TBME,
Neural Data-Driven Musculoskeletal Modeling for Personalized Neurorehabilitation Technologies
This review aims to discuss clinically viable methods for accessing the neural information underlying an individual’s movement from electrophysiological recordings and the development of subject-specific musculoskeletal modeling formulations that can be driven by the extracted neural features... Read more
TBME,
Neural Data-Driven Musculoskeletal Modeling for Personalized Neurorehabilitation Technologies
This review aims to discuss clinically viable methods for accessing the neural information underlying an individual’s movement from electrophysiological recordings and the development of subject-specific musculoskeletal modeling formulations that can be driven by the extracted neural features... Read more
TNSRE, Featured Articles
Adaptive Spatial Filtering of High-Density EMG for Reducing the Influence of Noise and Artefacts in Myoelectric Control
Electromyography (EMG) is a source of neural information for controlling neuroprosthetic devices. To enhance the information content of conventional bipolar EMG, high-density EMG systems include tens to hundreds of closely spaced electrodes that non-invasively record the electrical activity of muscles with high spatial resolution... Read more